Artificial Intelligence (AI) in Sports

Dr Patrick Lucey is the Chief Scientist at Stats Perform and has over 20 years of experience working in Artificial Intelligence (AI), in particular face recognition and audio visual speech recognition technology. He also worked at Disney Research (owners of ESPN) where he developed an automatic sports broadcasting system that tracked players in real-time by moving a robotic camera to capture their movements.

Patrick recently talked about the use of Artificial Intelligence in sports, what that means and how we can use AI to help coaches and analysts make better decisions in sport. Artificial Intelligence refers to technology that emulates human tasks, often using machine learning as the method to learn from data how to emulate these tasks. His talk emphasised on the importance of sports data, and provided an overview on the different types of sports data that exist today. Patrick explained what is meant by AI and why is AI needed in sport.

Stats Perform is one of the leaders in data collection in sports, offering a wide range of sports predictions and insights through world-class data and AI solutions. For over 40 years, they have been collecting the world’s deepest sports data, covering over 27,000 live streamed events worldwide with a total of 501,000 matches covered annually from 3,900 competitions. This huge coverage translates into the collection of billions of unique event and tracking data points available in their immense sports databases. To make use of this invaluable dataset, Stats Perform has created an AI Innovation Centre that hired more than 300 developers and 50 data scientists to create a series of AI products with the goal of measuring what was once immeasurable in sport.

Different Types Of Sports Data

Patrick and the Stats Perform AI Innovation Centre have worked on a wide range of different types of data to make predictions on a number of different sports, from football to field hockey, volleyball to swimming using different types of data. There are 3 main types of sports data available: box scores, event data and tracking data. All these types of data facilitate the reconstruction of the story of a match or a particular performance. However, the more granular the temporal and spacial data of a game is, the better the story an analyst can tell.

Box-Score Statistics

The use of high-level box-score statistics (half-time match score, full-time match score, goal scorers, time of goals, yellow cards, etc.) can summarise a 90-minute match of football to provide an idea on how the game was played in just a few seconds. Basic box-score statistics can tell you who won the match, which team took the lead first, when were the goals scored and how close together to each other. Box-score statistics provide a fairly good snapshot of a game and a decent level of match reconstruction.

Box-score statistics Sevilla vs Dortmund (Source: Sky Sports)

Box-score statistics also offer a more detailed level of information. For example, they can illustrate which team had more shots and the quality of those shots by showing the number of shots and shots on goal. They can also explain the distribution of possession between the teams in the match, which team had more corners, committed more fouls, made more saves and so on. Within a few second they can capture the story of the match, which team dominated or how close was that game.

Detailed box-score statistics Sevilla vs Dortmund (Source: Sky Sports)

Event Data

Event data, or play-by-play data, provides a bit more detail than box-score statistics by offering additional contextual information of key moments during a match. For examples, play-by-play commentary of a match can offer textual descriptions of what occurred at every minute of the match. Similarly, spacial data of the game (i.e. spacial location of players) can provide visual reconstructions of some of the key events in a match, such as how a particular goal was scored. While it is not the same as watching the video, it is a quick digitised view of the real-world play that can be reconstructed in seconds.

Text commentary of Sevilla vs Dortmund match (Source: Sky Sports)

Stats Perform, particularly through Opta, is one of the industry leaders in event data collection. They provide event data to sportsbooks through a low latency feed that tells them when a goal, a shot, a dangerous attack or any other key moments occur in close-to-real-time so that the sportbooks can relay that information to their bettors. In these cases, speed of data is crucial, not only to reconstruct a story of what happens on the field through data but to be able to tell that story almost imminently.

Tracking Data

Tracking data is currently the most detailed level of data being captured in sports. It enables the projection of the location of all players and the ball into a diagram of the pitch that best reconstructs a match from the raw video footage of that match. Having a digital representation through tracking data of all players on the entire pitch enables analysts to perform better querying than simply using a video feed that only displays a subsection of the pitch.

Tracking data plotted into a diagram of a football pitch (Source: Patrick Lucey at Stats Perform)

Sources Of Sports Data

Video Footage

The vast majority of data types are collected via video analysis. Video analysis uses raw match footage as the foundation to either manually observe or automatically capture (i.e. computer vision) key events of the match to generate data from. Today, all three types of sports data (box-score, event data and player tracking data) are fundamentally based on video. However, more recently new technologies have been gradually introduced into various sports to collect great details.

Radio Frequency Identification (RFID)

The NFL is now using Radio Frequency Identification (RFID) wearables implemented on players’ shoulder pads to track x and y coordinates of each player’s location on the field.

Radar

In golf, radar and other sensor technology has also been implemented to track the ball’s trajectory and produce amazing visualisations with very accurate detection of the ball.

GPS Wearables

Football and other team sports use GPS devices that, although not as accurate as RFID, can track additional data from the athlete, such as heart rate and level of exertion. These wearable devices have the advantage that they can be used in a training environment as well as a competitive match.

Market Data (Wisdom Of The Crowds)

Market data in sports usually refers to betting data. It is an implicit way of reconstructing the story of the match that relies on people coming up with their predictions where information can be mined from.

AI-Driven Sports Analysis

Sports analysis has traditionally been based on box-score and event data. All the way from Bill James’ 1981 grassroots campaign Project Scoresheet that aimed to create a network of fans to collect and distribute baseball information to Daryl Morey’s integration of advanced statistical analysis in the Houston Rockets in 2007.

However, in the 2010s, tracking data began to set a new path to new ways of analysing sports. Over the last decade, a new era of sports analysis has emerged that maximises the value of traditional box-score and event data by complementing it using deeper tracking data. The AI revolution in sports thanks to tracking data has focused on three key areas:

1. Collecting deeper data using computer vision or wearables

2. Performing a deeper type analysis with that tracking data that humans would not be able to do without AI

3. Performing deeper forecasting to obtain better predictions

Collecting Deeper Sports Data

The main objective of collecting sports data is to reconstruct the story of a match as closely as possible to the one seen by the raw footage that a human or a camera can see. The raw data collected from this footage can then be transformed into a digitised form so that we can read and understand the story of the match and produce some actionable insights.

The reconstruction of a performance with data usually starts by segmenting a game into digestible parts, such as possessions. For each part of this game, we try to understand what happened in that possession (i.e. what was the final outcome of the possession), how it happened (i.e. describing the events that led to the outcome of that possession) and how well it was done (i.e. how well were the events executed).

Currently, the way play-by-play sports data is digitised from the video footage is through the work of video analysts. Humans watch a game and notate the events that take place in the video (or live in the sports venue) as they happen. This play-by-play method of collecting data produces an account of end of possession events that describes what happened on a particular play or possession. However, when it comes to understanding how that play happened or how well it was executed, human notational systems do not produce the best information to accurately reconstruct the story. Humans have cognitive and subjective limitations when capturing very granular level of information manually, such as getting the precise timeframe of each event or providing objective evaluation of how well a play was executed.

In-Venue Tracking Systems

One way tracking data can be collected is through in-venue systems. Stats Perform uses SportVU, which was deployed a decade ago as a computer vision system that installed 6 fixed-cameras on a basketball court to track players at 24 frames per second. Their newer version of SportVU is now widely deployed in football. SportVU 2.0 uses three 4K cameras and a GPU server in-venue to collect and deliver tracking data at the edge in real-time.

Stats Perform SportVU system on a basketball court (Source: Patrick Lucey at Stats Perform)

However, tracking data has a main limitation: coverage. While tracking data provides an immense number of opportunities to do advanced sports analytics, its footprint across most sports is relatively low. This is because for most in-venue solutions a company like Stats Perform requires to be in the venue with all their tracking equipment installed. This is problematic when increasing the coverage of tracking data across multiple events across the world, as it is not realistic to have sophisticated tracking equipment installed in every single pitch, field, court or stadium across the world to cover every single sporting event that takes place every day.

Tracking Data Directly From Broadcast Video

To overcome the limited coverage of in-venue systems, Stats Perform are now focusing their AI efforts in capturing tracking data directly from broadcast video, through an initiative called AutoStats. It leverages the fact that for every sports game being played, there should be at least one video footage of that event being recorded and potentially being broadcasted. The way of getting the best coverage of tracking data is capturing the data directly from broadcasting footage.

PSG attacking play converted to tracking data from broadcast footage (Source: Patrick Lucey at Stats Perform)

This means that the way tracking data is being collected is now evolving away from in-venue solutions to a more widespread approach that uses a broadcast camera. However, the advantage of using in-venue solutions is that you only need to calibrate the camera once. When collecting tracking data off broadcast, you need to calibrate the camera at every frame because it is constantly moving while following the play.

Computer vision systems that collect tracking data directly from broadcasted video footage follow three simple steps:

1. Transform pixels in the video into dots that represent trajectories of the movement of players and the ball. These dots can then be plotted on a diagram of the field for visualisation.

2. The trajectories generated from the movement of the dots over a space of time can then be mapped to semantic events in the sport (i.e. a shot on goal).

3. From the events identified, expected metrics can be derived to explain how well does a player execute on a particular event (i.e. Expected Goals).

Converting Pixels To Dots

Converting video pixels to dots refers the process of taking the video footage of the game and digitally mapping each player movement to trajectories that can be displayed on a diagram of the pitch in the form of dots. The main advantage of this method is the compression of the footage. An uncompress raw snapshot image of a game at 1920x1080px from a single camera angle can be as large as 50MB, which means video footage of that game can be as large as 50MB per frame. If instead of one camera angle you have 6 different camera angles, the data file size multiplies to around 300MB per frame. This is an incredibly high amount of high dimensional data, but not all of it is useful for sports analysis.

Conversion of video footage pixels into dots on a diagram (Source: Patrick Lucey at Stats Perform)

Instead, tracking data representing players on the court or pitch in the form of dots can substantially reduce the size of each frame. For example, in basketball, 10 players, 1 ball and 3 referees can be plotted with their x, y and z coordinates in a digital representation of the court with a size of 232 bytes per frame. This makes tracking data the master compression algorithm on sports video with compression rates of 1 million to 1.

The advantages of using tracking data instead of raw video footage is that it allows to query the dots instead of the pixels in a way that maintains the interpretability and interactivity from the raw video footage. A game can be clearly reconstructed using dots plotted on a diagram of the field to illustrate how each possession happened without the need of the extra detail available in the video footage in the form of millions of pixels.

The way the conversion from pixels to dots occur is via supervised learning, where the computer learns through machine learning processes to map and predict the input data from the pixels to the desired output of the dots. A number of computer vision techniques can be applied to achieve this goal.

Mapping Dots to Events

Once the dots (coordinates) have been generated from the pixel data of the video, the trajectories (movements) of these dots over specific timeframes can be mapped to particular events. For example, in basketball, you can start mapping these dots in the tracking data to particular basketball-related events that describe how certain outcomes occur in terms of tactical themes, such as pick and roll, type of coverages on pick and roll, did the player do a drive or a post up, off-ball screens, hand off, close out, etc. The dot trajectories are mapped to the semantics of a basketball play, and the players involved in that play, using a machine learning model that does that transformation using pre-labelled data.

Mapping Events to Expected Metrics

Expected metrics explain the quality of execution of certain events. The labels assigned to certain events are often not informative enough to explain that event. Instead, expected metrics transform an outcome label of 0 or 1 (goal or no goal) to a probability of 0 to 100% using machine learning. For example, a shot that goes in goal is considered 100% effective. However, a shot attempt that hits the post might be considered 70% effective, even if it did not end up in a goal. Regardless of the final outcome of that event, expected metrics help to evaluate whether an event was more likely to be 0% (unsuccessful), 100% (successful) or somewhere in the middle (ie. 55% successful). This concept of expected metrics is the basis of the Expected Goals (xG) metric in football. Expected Goals can also be extended to passes to calculate the likelihood of a pass reaching a certain teammate on the pitch.

Expected metrics provide an additional degree of context to each situation. For example, in basketball they use Expected Field Goal percentage (EFG) to explain that if a player misses a 3-point shot, rather than simply classify that player as missing a shot we can assess what is the likelihood that an average league player would have scored that shot from a similar situation. This can provide a measure of talent of a player over the league average and better contextualise his performance.

Limitations of Event and Expected Metrics Data

The main limitation of solely using pre-labelled event and expected metrics data using this supervised machine learning process is that not everything can be digitised. Most analysis conducted today are based on events and expected metrics, but these are semantic layers that have been pre-described or pre-categorised by humans. We have put certain patterns of play or combination of player movements into labelled boxes to make it easy to aggregate and analyse sport events. However, the dots generated from tracking data and their identified trajectories open numerous possibilities to perform further analysis that humans can’t do manually by ignoring these pre-labelled categories of patterns of play or specific player movements.

Performing Deeper Sports Analysis

The more granular the data the better analysis we can conduct of a sport. Tracking data provides that necessary level of granularity to conduct advanced analytics. Some of the key tasks that deeper data and better metrics can do much better than humans is strategy, search and simulation.

Strategy Analysis

Marcelo Bielsa once broke down the way he does analysis at Leeds United. His analysis team watches all 51 matches of their upcoming opponent from the current and prior seasons, each game taking 4 hours to analyse. In that analysis, they look for specific information about the team’s starting XI, the tactical system and formations and the strategic decisions that they make on set pieces. However, it can be argued that this methodology is time-consuming, subjective and often inaccurate. This is where technology can come in and help by making the analysis process more efficient than having a team of Performance Analysts spend 200 hours assessing the next opponent.

The idea is to transition strategy analysis in sports from a traditional qualitative approach to a more quantitative method. Tracking data has hidden structures. The strategies and formations of a team in a match of football is hidden within all the data points collected from tracking data. Insights on things like formation or team structures do not directly emerge from the tracking data without additional work on the data. This is because tracking data is noisy, for reasons such as that players are constantly switching positions on the pitch. But what tracking data allows you to do is to find that hidden behaviour and structure of a team or players and let it emerge.

Visual representation of a noisy tracking dataset of players in a football pitch (Source: Patrick Lucey at Stats Perform)

As a way to better visualise and interpret tracking data, Stats Perform have developed the software solution Stats Edge Analysis to enable the querying of infinite formations based on tracking data. The software shows the average formation of players throughout a match, how often each player is in a certain situation, how a team’s structure evolve when they are attacking or defending or how does the formation compare in different context, situations or playing styles.

Formation analysis in Stats Edge Analysis software (Source: Patrick Lucey at Stats Perform)

Search Analysis

How do we find similar plays in sport? How do we search across the history of a sport to find similar situations to the one we are interested in comparing with? One way is to use sport semantics and search using keywords such as a “3pt shot” play in basketball, a “pick and pop” play or a play “on top of the 3pt line”. However, if we want to know where all the players were located in a play, their velocity or their acceleration, as well as all the events that led up to that point, we would need to use too many words to describe that particular play very precisely. In other words, searching across the history of a sport for a similar play using just keywords does not capture the fine-grained location and motions of players and ball and does not provide a ranking of how similar the found plays are to the original play we want to compare them with.

A solution to this problem is to use tracking data. Tracking data is a low dimensional representation of what we see in video. Therefore, instead of using keywords to find a similar play, we could use a snapshot of a play using tracking data as the input in a visual search query. Users could then interact with a visual search query where they describe the type of play they want to search for and the query tool would then output a set of similar plays ranked by the degree of similarity to the play being queried.

Visual search query of similar plays (Source: Patrick Lucey at Stats Perform)

This type of visual search tool based on tracking data can offer the possibility of drawing out the play to search for. It can also offer the ability to move players around the court and use expected metrics to show the likelihood of a player scoring from various positions. It can even show the changes in scoring likelihood based on the position of the defensive players relative to the player with the ball.

Play Simulation

Technology in sports is entering the sidelines. The type of technology coaches need to evaluate plays during a game and simulate different outcomes needs to be highly interactive. One way Stats Perform has used tracking data to improve play simulations is through ghosting. The idea of ghosting is to show the average play movements at the same time as the live play represented with dots on a diagram of the field. For example, tracking data can display the home team in one colour (blue) and away team in another colour (red), but additionally it can add a third defensive team in a different colour (white) that represents how the average team in the league would defend that same situation.

Ghosting of an average team in the league (white) defending a situation (Source: Patrick Lucey at Stats Perform)

Another way Stats Perform is working with coaches in the sidelines to provide more interactive play simulations is through real-time interactive play sketching. A coach can draw out a play that they want their players to perform on their clipboard and what tracking data and technology can do is to make intelligent clipboards that can simulate how that play drawn by the coach would play out.

Performing Deeper Sports Forecasting

The more granular data available the better we can predict sports performance. Some of the applications of tracking data in forecasting include player recruitment (i.e. which players to buy, trade, draft or offer longer contracts) and match predictions (i.e. accurately predict the final outcome, score and statistics of a match both before the match takes place and in-play).

Player Recruitment

In the NBA, the league has a good level of coverage for tracking data. But what happens when a team wants to recruit someone from college? Tracking data might not exists in college leagues, which forces teams to use a very simplified version of reporting to forecast how that player is going to play once he is recruited onto the team.

This highlights the issue of tracking data coverage. Major leagues have that level of detailed tracking data, but most lower leagues and academy competitions do not. Also, historical matches from major leagues and sports prior to the era of tracking data will not have had the systems and equipment in place at the time to produce highly detailed tracking data. This is where the generation of tracking data through broadcasted video footage can fill that void.

Tracking data using broadcasting footage is the ultimate method to produce detailed recruitment data. Analysts can go back in time and produce data from all the previously untracked players by simply using the footage available from past games. Stats Perform achieves this through AutoStats. AutoStats is a data capture system that can identify where players are located even though the camera is constantly moving by applying continuous camera calibration. It detects body pose of players and can re-identify a player once that player comes back into view after having left the frame. Additionally, AutoStats uses optical character recognition to collect the game and shot clock on every frame, as well as using action recognition to track the duration of player events at a frame-level.

Once that tracking data has been generated from lower leagues or college games, AI-based forecasting can be applied to discover which other professional players is the scouted player of interest most similar to. These solutions can even project a young player’s future career performance. It can use prediction models from historical data of former rookies and their eventual successes to forecast future performances of current prospects.

Given the limited coverage of tracking data in lower and junior leagues, another method to overcome that limitation is to use the already collected event data to maximise the value of the coverage in event data compared to tracking data. Machine learning can define the specific attributes of two players to then compare them with each other. These attributes can be spacial attributes, such as where they normally receive the ball, contextual attributes, such as their team’s playing style (i.e. frequency of counter attacks, high press, crossings, direct plays, build up plays, etc.) and quality attributes, such as expected metrics to capture the value and talent of each player. This method can provide a clear comparison of two different players relative to the context in which they play in. For example, how often is a player involved relative to the playing style of a particular situation.

Taking all this data and the derived attributes from event data, you can then run unsupervised models, such as Gaussian mixture model clustering, to discover groupings of players based on their similarities, and then create a number of unique player clusters that divide pools of players. These clusters can then surface information about the roles that different groups of players play in their teams, whether they are “zone-movers”, “playmakers”, “risk-takers”, “facilitators”, “conductors”, “ball-carriers” or any other clusters that can emerge from applying unsupervised methods. This way, if a team wants to find a player similar to a specific successful player (i.e. players similar to Messi), but with some attributes that are slightly different (i.e. age, league, etc.), they are able to specify that search criteria and find players that fit the profile that they are after.

Match Predictions

There are a couple of ways that AI can help in match predictions. One of them is implicitly through crowd-sourced data. Prediction markets like betting exchange facilitate a marketplace for customers to bet on the outcome of discrete events. It is a crowd-sourced method, and if there are enough participants to represent the entire collective wisdom of the market, with enough diversity of information and independence of decisions in a decentralised way, it is the best predictor you can get. It is an implicit market as we do not know the reason why people have made their betting choices, therefore it is not interpretable. If enough people are participating in these markets, then all possible information to make a prediction is present in that market. If that is the case, it is not possible to beat the accuracy of that market prediction.

Another method is to use an explicit data-driven approach using only data from historical matches together with machine learning techniques to predict probabilities of match outcomes. This method relies on the accuracy and depth of the data available and can only capture the performance present within the data points collected. The advantage of using a data-driven approach is that it can be interactive and interpretable. Also, it only needs the data feed of events, which makes it scalable. However, since not all data might be captured in the dataset used (i.e. injury data), there may be gaps in the analysis that can affect the predictions made.

Sportsbooks normally use a hybrid approach of crowd-sourced data together with data-driven methods to balance the action on both sides of the wager and also to manage their level of risk. They initialise the market with a data-driven approach and human intuition and then iterate based on volume, other sportbooks line and any unique incentive they want to offer to their own customers.

AI-based solutions and tracking data can be used to support these prediction markets, particularly in those markets with insufficient coverage to achieve crowd wisdom. One way of doing so is through the calculation of win probability. Win probability is extensively used across nearly every sport for media purposes. The current limitation of win probability is that it is based on the likelihood that an average team would win given a particular match situation. However, simply using an average may miss contextual information about the specific strengths of particular teams or players involved. The way to overcome that is to use specific models that incorporate the players, teams and line-ups of the match in question.

Stats Perform uses models that learn compact representations with features such as the specific opponent, players involved and other raw features describing the lineup to improve prediction performance based on the players involved in the game. This allows them to create specific player props that can predict individual player statistics (i.e. expected points scored in basketball) for each player in the lineup and illustrate that player’s future game performance before the game starts.

Similarly, these predictions can also be made in real-time while a match is being played. For example, using tracking data, in-play predictions in a tennis match can predict who is more likely to win the next point while the rally is taking place. You can even go a level deeper and predict what is the location where the ball will land after the next strike. In football, you could also predict who is the next player who is going to receive a the ball from a pass or where the next shot on goal is going to occur. This is the true value of highly granular levels of data and a data-driven approach to sports analysis.

Contextual Analysis In Sport Using Tracking Networks

Javier Martin Buldu is an expert on the analysis of non-linear systems and the understanding of how complex systems organise themselves, adapt and evolve. He focuses on the application of network science and complex systems theory in the analysis of sports. Buldu’s work is based on the principle that teams are far more than the simple aggregation of their individual players. By collaborating with organisations such as the Centre of Biomedical Technology in Madrid, La Liga, ESADE Business School, IFISC research institute and the ARAID Foundation, he has been able to combine elements of graph theory, non-linear dynamics, statistical physics, big data and neuroscience to construct various networks using positional tracking data of a football match. These networks are then able to explain what happens on the pitch beyond conventional ways of assessing the performance of individual players to understand team behaviours.

What Is Complex System Theory?

A complex system is a system composed by different parts that are connected and interact with one another. This system has properties and behaviours that cannot be explained by simply breaking down the system into its individual parts and analysing each individual part independently. For example, the human brain is a complex system and it has proven extremely challenging for scientists to fully understand how it performs all its functions, from how memory is stored to how cognition appears and disappears during certain illnesses. On the other hand, the human brain’s most fundamental component, the neuron, has been thoroughly studied and documented by science. Scientists have been able to recreate models and simulations of neuron behaviour, understand their shape and how they communicate with other neurons. However, this robust understanding of single neuron behaviour has not been sufficient to allow scientist to comprehend the interplay and interdependencies of the 80 billions neurons that form the human brain and that allows it to perform all of its complex behaviours. Instead, in order to appropriately study the brain, scientist need to pay attention to entire human cognitive system as a whole.

The idea behind complex systems like the human brain is what Buldu wanted to introduce in the analysis of football. While it is interesting to have information about isolated player performance, such as the number of shots, passes or successful dribbles, it is also important to understand the context in which these events take place. Additional insights on the performance of players and teams can be obtained by analysing information about how a player interacted with his teammates and the opposition’s players. Paying attention to individual player performances and aggregating those together is not enough to fully understand how a team behaves during a match.

Instead, a complex system approach to football analysis would, for example, look at the link created between two or more players when they pass the ball between them. A network of these players can then be created by simply leveraging event data collected from notational video analysis to count the number of passes from player A to player B and vice versa. These types of passing networks are increasingly common in football match analysis and team reports, as they clearly illustrate information about how a team played during a match, where its players were most frequently located on the pitch and how they interacted with each other.

Passing Network between FC Barcelona players (Source: Javier Martin Buldu at FC Barcelona Sports Tomorrow)

However, more complex and informative networks can be developed by leveraging positional tracking data instead of event data. While event data is generated through notational analysis by tagging specific actions, positional tracking data instead describes the position of the 22 players and the ball on the pitch at any moment in time during a match of football. Unfortunately, positional tracking data is challenging to access for most analysts. That is why Buldu collaborated with La Liga to obtain a positional tracking dataset containing Spanish football league matches. To capture this information, La Liga uses Mediacoach, a software that acquires the positional coordinates of players and the ball using a TRACAB optical video tracking system that requires the installations of specialised cameras across the football stadiums. Mediacoach’s system allows them to track a player’s position at 25 frames per second and a precision of 10cm. Thanks to this detailed tracking dataset received from La Liga, Buldu was able to explore the different interactions between players to construct a number of complex tracking networks in football. 

Proximity Networks

The first network that Buldu produced explored the proximity between players on the pitch. He first calculated an arbitrary 360 degrees distance around a player, let’s say a 5m radius, and used it as a threshold to identify any other players that may fall inside that particular player’s area. If another player was located inside of the first player’s surrounding area, a link was then created between those two players. If those two players were from the same team, a positive link was created, while if they were from opposing teams a negative link was assigned to that interaction instead. By increasing or decreasing the radius of the distance surrounding each player (i.e. 5m, 10m or 15m radius), Buldu produced different networks and links between players following this method.

Proximity radius at 5m, 10m and 15m showing links with players of the same team (green) and with opposing players (red) (Source: Javier Martin Buldu at FC Barcelona Sports Tomorrow)

The challenge of producing a variety of proximity networks is that they may prove difficult to analyse, as the links identified in a single video frame using a 5m radius around each player may be very different to those found using a 15m radius. On top of that, the analysis should look at how those proximity networks evolve over a number of frames during the match. In order to gather practical insights from these networks, Buldu aimed to study the number of positive and negative links for each of the teams, as well as the organisation of the proximity network structure, its temporal evolution and how they change in relation to the zone of the pitch and the various phases of the game.

Proximity analysis of the 3-player links for all players in a match between Atletico Madrid and Real Valladolid (Source: Javier Martin Buldu at FC Barcelona Sports Tomorrow)

He first counted the number of links between three different players forming a triangle. He then classified each triangle into two categories: positive (all players from the same team) or mixed triangles (at least one player from the opposing team). Buldu was then able to determine which team had dominance over the other at different times of the match by then counting the number of positive triangles and the number of mixed triangles produced with a certain threshold distance. The team with the the highest proportion of positive triangles (i.e. all three players in close proximity to each other forming a triangle were from the same team) was deemed to have been dominant over its opposition.

Marking Networks

The second type of network that Buldu was able to construct with positional tracking data was the time a player was covering an opposing player during a defensive phase of play. Again, by setting an arbitrary threshold distance around a defender, a link between the defender and opposing player can be set by counting the time both players are in close proximity to one another. This process produces a matrix that illustrates the defenders on one of the axis and the attackers on the other axis, and provides a rough idea about the amount of time that each attacking player was being marked and by which defensive player. By interpreting the marking matrix analysts are able to identify the players with the highest accumulated time being marked by a defensive player.

Player marking matrix between Real Madrid (y-axis) and Leganes (x-axis) showing how often each Real Madrid players was marked by a Leganes player (Source: Javier Martin Buldu at FC Barcelona Sports Tomorrow)

Since matrices are the mathematical extraction of a network, this information can be drawn onto a diagram of a football pitch to plot the position of players during defensive actions. The size of each node in this network indicates the time an attacking player was being defended. By using these marking networks, analysts can clearly visualise the interactions and efforts of attacking and defending players during a match of football.

Player marking network between Real Madrid and Leganes (Source: Javier Martin Buldu at FC Barcelona Sports Tomorrow)

Coordination Networks

The third network that Buldu produced evaluated the coordination of movements between players of the same team. The network computed the velocity and direction of movement of two players to measure the alignment of their vectors. When this vector alignment was high, a high value link between these two players was created. When the alignment was low, a lower value connection was also derived from the two players’ movements. This method results in a matrix that illustrates how well players are coordinated with their own teammates. Two different matrices can be produced, one to analyse offensive phases of play and one for defensive phases.

Vector alignment of two attacking players (Source: Javier Martin Buldu at FC Barcelona Sports Tomorrow)

Similarly to marking networks, coordination network matrices can also be translated into diagrams on a football pitch, where the nodes represent each player on the pitch while the size of each node indicates the amount of coordination the player has with the rest of his teammates. The links between two nodes also indicate the level of coordination between two particular players of the same team.

Movement coordination of each player with the rest of his teammates (Source: Javier Martin Buldu at FC Barcelona Sports Tomorrow)

This type of analysis, especially when split between offensive and defensive players, can help analysts better understand the level of coordination between attack and defensive plays. For instance, an analyst or coach may want to see high degrees of coordination when the team defends as a block as well as how that coordination changes during the different phases of the game.

Ball Flow Networks

Lastly, the final network developed by Buldu focused on ball movement between different areas of the pitch. This network was produced by splitting the football pitch into different sections and counting the number of times the ball travelled from one section to another in order to create links between two different sections. This ball flow network can also be visualised on a diagram of a football pitch, with the nodes representing each section of the pitch and links indicating the number of times the ball moved from one section to the next. The size of these nodes indicate the amount of time the ball was being played inside that particular section of the pitch. By constructing an entire ball moving network during a match, analysts can then identify which are the most important sections of the pitch for their teams and assess how to exploit different sections in the opposition’s side in order to create dangerous opportunities.

Ball flow network for a match between FC Barcelona and Espanyol (Source: Javier Martin Buldu at FC Barcelona Sports Tomorrow)

Buldu’s work provides a great analytical framework to assess the complexities of sports in which a large diversity of factors can influence different outcomes of the game. It is crucial that when analysing a sport, all the available contextual information is analysed from various perspectives that can together provide a more complete evaluation of performance. Researchers, scientists and analysts are increasingly producing exciting work with positional tracking data that can open the door to new sophisticated methodologies and models to help coaches better understand the key influential factors of their team’s performance.

Further Reading:

• Futbol y Redes Website

• Buldu, J. M., Busquets, J., & Echegoyen, I. (2019). Defining a historic football team: Using Network Science to analyze Guardiola’s FC Barcelona. Scientific reports, 9(1), 1-14. Link to article.

• Buldu, J. M., Busquets, J., Martínez, J. H., Herrera-Diestra, J. L., Echegoyen, I., Galeano, J., & Luque, J. (2018). Using network science to analyse football passing networks: Dynamics, space, time, and the multilayer nature of the game. Frontiers in psychology, 9, 1900. Link to article.

• Garrido, D., Antequera, D. R., Busquets, J., Del Campo, R. L., Serra, R. R., Vielcazat, S. J., & Buldú, J. M. (2020). Consistency and identifiability of football teams: a network science perspective. Scientific reports, 10(1), 1-10. Link to article.

• Herrera-Diestra, J. L., Echegoyen, I., Martínez, J. H., Garrido, D., Busquets, J., Io, F. S., & Buldú, J. M. (2020). Pitch networks reveal organizational and spatial patterns of Guardiola’s FC Barcelona. Chaos, Solitons & Fractals, 138, 109934. Link to article.

• Martínez, J. H., Garrido, D., Herrera-Diestra, J. L., Busquets, J., Sevilla-Escoboza, R., & Buldú, J. M. (2020). Spatial and Temporal Entropies in the Spanish Football League: A Network Science Perspective. Entropy, 22(2), 172. Link to article.

Automating Data Collection And Match Analysis From Video Footage

Dr Manuel Stein has spent over 7 years researching and analysing player movement using detailed positional football data. His work has focused on the investigation of real-time skeleton extraction to perform match analysis of player movement with the aim of fostering the understanding of comparative and competitive behaviours in football. He has revolutionised the way match and tactical analysis is performed by teaching computers how to measure key playing aspects of the sport, such as team dominance or a player’s control of space derived directly from video footage. Stein has developed an automatic and dynamic model that takes into account the contextual factors that influence the movement and behaviour of players during a match. This novel player detection system automatically is able to display complex and advanced 5-D visualisations that are superimposed on original video footage.

Generating Data From Match Video Footage

The first step for any meaningful quantitative analysis is to obtain highly detailed data to properly test our assumptions. However, gathering highly detailed sport data may be challenging to obtain unless sophisticated tracking technology is used and the results of such tracking are easily accessible to the analyst. On top of that, when it comes to positional player data in football (i.e. xy coordinates of players on the pitch), gaining access to this level of granular data is especially challenging for most analysts. This is the same problem Stein faced during the initial phases of his research and that led him to develop a method for data extraction on his own using television footage and computer vision techniques.

Identifying Players On The Pitch

Stein’s method of extracting data from television footage started with the detection of each player on the pitch. In order to automatically identify the players, Stein addressed the unique colours that are present on the football pitch, more specifically the colours of the players’ shirts. By picking a player in the video, he constructed a colour histogram that best described the most prominent colours in that player’s shirt. Once those colours were identified, he then automatically searched across the video frame for contours of a minimum size that contained those same colours detected from that player’s shirt to spot all other players with the same colour shirt. The computer then automatically calculated the centroid of each detected area (i.e. the players as well as minor noise) and used the average measurements of human proportions to draw boxes enclosing the entire player on the screen.

Colour-based player detection (Source: Manuel Stein at FC Barcelona Sports Tomorrow)

This colour-based player detection method enabled Stein to identify all players on the pitch. The additional noise captured on the sidelines and stadium crowd was later removed by using threshold and ignoring areas that only appear on screen for a brief moment of time. However, this colour-based detection approach has certain limitations depending on the match footage. Lighting variations during matches that kick off under sunlight and finish around dusk do not impact colour perception in humans, but they do so for automatic colour-based player detection systems, as towards the end of the match computers will not be detecting the same colours as they did during kick off.

In order to solve this limitation and develop a system that works on all match conditions, Stein explored additional automated real-time methods to simultaneously extract player body poses and movement data directly from the video footage. One of those methods was the use of OpenPose, a well-known and established computer vision system for human body pose detection. However, OpenPose was not a suitable option when working with football footage, as the system struggles to detect small scaled people on the screen and is also unable to be computed in real-time during a match. Instead, Stein developed and trained his own deep learning model completely from scratch.

Body pose detection system (Source: Manuel Stein at FC Barcelona Sports Tomorrow)

Stein’s human body detection model uses a skeleton model based on a hierarchical graph structure that represents a body’s pose. Every node on the hierarchical graph corresponds to the position of a body part from the person’s skeleton such as joints, ears, eyes and so on, called key points. The edges of this hierarchical graph represent an anatomical correct connection between two body parts. Stein’s body pose detection process followed two stages: the detection of individual body parts followed by the probabilistic reconstruction of the skeletons by connecting all identified body parts together. The constructed skeletons of the players were then overlayed on the original video footage for easy visualisation. Stein model’s estimation accuracy results outperformed those of OpenPose when estimating the skeletons on medium-scale people from the Microsoft COCO dataset. Moreover, their model architecture is also optimised for real-time and low latency video analysis, unlike OpenPose which struggles to run on resolutions of close to 4k.

Identifying The Ball

The next step was to detect the ball. For that, the model followed a two-step approach: a per-frame candidate detection step followed by a temporal integration phase. It first detected all possible objects on the screen that could potentially be the ball by using a convolutional neural network. The computer detected things such as the penalty spot, the corner kick spot, the centre spot, white football boots or the ball itself as being possible candidates. The next step was to identify an accurate and realistic ball trajectory over a period of time from the previously identified candidates using a recurrent neural network. This enabled the model to specify which one out of the previously detected objects was indeed the ball, as it was moving throughout the footage as a ball would be expected to move. By using this approach, the ball could be tracked even when it was not visible on the video footage. For instance, the computer continued to track the ball even when a player picked it up before a penalty kick and happened to hide it from the camera.

Determining Player And Ball Location On The Pitch

Once both players and the ball have been detected, the following step is to determine their location on the full football pitch. The challenging part in this section is the fact that the camera is continuously focusing on different parts of the pitch rather than the pitch as a whole. To solve this issue, Stein had to produce a static camera shot by creating a panoramic view of the complete stadium using a subset of input frames from the video footage (i.e. all frames from the first two minutes of a match). The overlap of all these snapshots from the video footage was then used to recreate a panoramic view of the pitch that allowed Stein to calculate the pitch’s homography. He was then able to identify how two different images connected together, or detect whether one image was simply a subset of a larger image. The homography calculation then enabled Stein to project each of the frames from the video footage into the panoramic view of the pitch as a unique reference frame and fully visualise where on the full pitch each frame took place.

Projection of frames on the panoramic view of the full pitch (Source: Manuel Stein at FC Barcelona Sports Tomorrow)

With all players and the ball correctly identified and their position accurately projected on a panoramic view, the next step was to project these player locations into a normalised football pitch to start generating usable positional data for further analysis. By providing the system with a standard image of a football pitch, a user can select a minimum of four points both from the panoramic view and their image of the pitch in order for the system to use the homography calculations from the panoramic view and translate them into the standard image of the pitch. This allows the system to automatically plot accurate player positional data on a standard diagram of a football pitch.

Player locations and movements illustrated in real-time on a diagram of the pitch on the top right corner (Source: Manuel Stein at FC Barcelona Sports Tomorrow)

Automatically Measuring Contextual Information From Video

Stein took his research further by incorporating the tracking of elements in a match that are not clearly visible to a computer, areas such as the dependencies, influences and interactions between players during the various scenarios of a game. For a fully automated football analysis system to work, this context information that is obvious to humans also needs to be taken into account and measured by the computer. In a dynamic team sport like football, players are more than simple and independently moving dots on a pitch. There is a complex network of interactions and dependencies that dictate how a player reacts to a situation, how they cooperate with teammates and how they attempt to prevent the opposing players’ actions.

Interaction Spaces

One way to automatically measure contextual information from player positional data was to identify the specific regions on the pitch that are controlled by the different players. Stein argued that each player has a surrounding area around them that he fully controls based on his position on the pitch. These control regions are what he called ‘interaction spaces’ on the pitch that a player can reach before any opposing player or the ball could reach that same space. The size and shape of these interaction spaces are influenced by player speeds and directions, as well as the distance between the players and the ball. This is because players further away from the ball may have more time to react.

Interaction spaces for each player (Source: Manuel Stein at FC Barcelona Sports Tomorrow)

On top of that, competition between two opposing players to control a certain zone has also an impact on the shape of these interaction spaces, as players from the opposing team will aim to restrict certain opposing player movements. Therefore, when defining interaction spaces on the football pitch, Stein aimed to consider these interdependencies that may restrict a player from reaching a particular zone before an opposing player to maintain ball possession. This can be seen in the above illustration between the blue team’s defensive line and the red team’s forwards, where players that are close to opposing players may restrict each other’s interaction spaces. Lastly, Stein was able to leverage the pitch visualisations of the previously recorded positional data and enrich it with additional context information that clearly illustrates each interaction space in real-time.

Free Spaces

An alternative way of contextualising automatic tracking data was the inclusion of free spaces. Stein calculated free spaces by segmenting the pitch into grid cells of 1 squared metre. He then assigned each respective cell to the player with the highest probability of reaching that cell in relation to the distance to the cell, their speed and direction of movement. Similarly to interaction spaces, free spaces where the cells from the grid that a player could reach before any other opposing player. Ultimately, free spaces represented the pitch regions a specific team or player owned.

All free spaces identified for a team in blue (Source: Manuel Stein at FC Barcelona Sports Tomorrow)

To evaluate which free zones were more meaningful for analysing, Stein ranked all free spaces on the pitch by their value in relation to their respective sizes, number of opposing players overlapping such spaces and the distance to the opposing goal.

All high value free spaces shortlisted for a team in blue (Source: Manuel Stein at FC Barcelona Sports Tomorrow)

Dominant Regions

Stein expanded his concepts of region control on a football pitch by using similar calculations to those of interaction spaces to create a model that highlights the dominant regions for each team. These dominant regions are calculated by looking at areas on the pitch that can be reached by at least 3 players of the same team simultaneously. Ultimately, they represent the areas in which a particular team has substantially more control over the other.

Dominant zones by players in the blue team (Source: Manuel Stein at FC Barcelona Sports Tomorrow)

Cover Shadows

Similarly, Stein extended the concept of interaction spaces to calculate player cover shadows, referring to the area a player can cover in relation to the position of the ball. In other words, a player has full control to prevent a ball from reaching their cover shadow region. Cover shadows can be thought of as a hypothetical light source coming from the ball at a 360 degree angle. These cover shadows represent the regions that the player is able to control before the ball gets to them.

Cover shadows illustrating a player’s area coverage in relation to the ball (Source: Manuel Stein at FC Barcelona Sports Tomorrow)

Applications Of This Automated Player Tracking System

When looking at the possible applications of his automated tracking system, Stein had to consider the roles of Performance Analysts and the coaches. For a Performance Analyst, video and movement data are key when analysing the strengths and weaknesses of their team and the opposition. On one side, analysts have a window on their screens with their video analysis software opened, such as SportsCode or Dartfish, to notate events and analyse playing actions. While on the other side, they have another window with the original video footage of the match that they use to verify and interpret any observations captured from their coding. Often what this means is that the analyst is looking at two different windows and comparing them to one another. While this is common practice in the field of Performance Analysis, the exercise of switching focus between two screens may often prove to be an inefficient approach to video analysis. Focusing on two windows simultaneously can prove significantly challenging to the human eye, often leading to a ‘pause and play’ exercise during analysis.

Stein aimed to solve this problem by combining the benefits of the visualisation of the pitch from his new automatic player tracking system with the original match footage. By simply inverting the homography from the abstract pitch into the video footage, he was able to draw visualisations directly on the real pitch. This allowed him to illustrate in real-time different types of analysis, from evaluating offensive free spaces to looking at players’ interaction spaces.

Interaction spaces automatically displayed directly on real match footage (Source: Manuel Stein at FC Barcelona Sports Tomorrow)

Stein’s dynamic and automatic real-time visualisation offered a whole new range of design opportunities for match analysis in football. For instance, the system was able to change a player’s shirt colour based on their behaviour (i.e. based on fatigue). It was also able to illustrate the best passing options available to the player with the ball.

Automatically computed best passing options (Source: Manuel Stein at FC Barcelona Sports Tomorrow)

This novel tracking method provides an invaluable automatic measurement of the context of a match situation. However, similar to any other analytical tools, it needs to be correctly applied in order to make a difference to team and player performance. Aside from the clear operational efficiencies brought by the automation of tedious notational work, the benefits in knowledge acquired from this system needs to be appropriately incorporated into the analysis loop. For instance, data on free spaces can be used to automatically detect suboptimal movements from players and suggest potential improvements for such behaviours. For example, an analysts can select specific situations where there was a shot on goal or dangerous play by the opposition to then identify which of their own players had control over free spaces that could have prevented such occasion. Once a selection of possible players have been identified, analysts can assess which one of those players lost control of their space the fastest and how such player could have kept control over his opponent. The identified player can then receive information about which should have been his optimal position on the pitch and their control of field space in order to reduce the free spaces towards his own goal left to be exploited by their opponents. Stein’s system is able to provide this guidance to analysts, coaches and players by automatically calculating the player’s moving trajectory based on his speed and interactions space and suggest an optimal realistic movement for that player, from the starting position to the optimal point. This means that the system can automatically suggest improvements in collective behaviour based entirely on the contextual information being processed.

Click and drag interactivity (Source: Manuel Stein at FC Barcelona Sports Tomorrow)

The system also offers interactivity, where analysts and coaches can drag and drop players around the pitch to explore the different control spaces the player would benefit from if they were in a different location of the pitch. By moving a player to a different location, the system automatically updates the player’s trajectory and interaction spaces relating to their new location and the other players around him. This gives coaches and analyst the possibility to interact with the analysis and to adapt the system based on their own acquired knowledge of the sport.

Automated systems such as the one developed by Manuel Stein are bringing exciting levels of innovation to the sport by directly integrating data and video together. Thanks to these systems, football experts, coaches and analysts become more aware of the power of analytics once they are shown the context of real world scenarios, which in turn leads to better analytical approaches being developed that are better incorporated into the daily realities of the roles of analysts and coaches. Ultimately, it reduces or completely removes numerous tedious and time consuming work performed by analysts today in a revolutionary way that frees up time away from simple data collection which can in turn be placed in more dedicated and advanced analysis of the sport.

Further Reading:

• Manuel Stein’s publications

• Stein, M., Janetzko, H., Breitkreutz, T., Seebacher, D., Schreck, T., Grossniklaus, M., Couzin, I. & Keim, D. A. (2016). Director's cut: Analysis and annotation of soccer matches. IEEE computer graphics and applications, 36(5), 50-60. Link to article

Setting Up Performance Analysis Equipment On Matchday

The following guide explains the setup process of Performance Analysis equipment during match days. This setup is frequently used in a number of major sports, particularly in those sports where analysts and coaches sit close to each other. However, the level of venue infrastructure can significantly vary between sports, clubs and divisions. Therefore, the same setup is not always possible and analysts need to have contingency plans at hand to be able to achieve the objectives of obtaining match footage, generating statistics and sharing real-time insights with coaches.

The example presented below represents a relatively simple setup often used in events with little to no technical infrastructure available in the match venue and where coaches are in close proximity to the analysts. This is frequent in sports such as Rugby Union where the coaching staff is located in the stands or gantry where the analysts perform their live coding. The equipment setup described here can easily be transported between venues, quickly assembled and later dismantled after the match. It provides sufficient flexibility to be used in a wide range of sporting events at different levels, from academy teams to elite matches.

Scenario:

The hypothetical match setup in this guide covers a scenario where two performance analysts code the match live as it takes place. Three coaches sit next to them in the gantry of the stadium, each with a laptop available in from of them. As the match is played, the performance analysts import the video feed received from the cameras into SportsCode Elite. They then use the software’s live coding capability to generate live statistics, such as possession in the different pitch zones, number of tackles, shots, infractions (penalties, cards, fouls, etc.) and other relevant match actions.

Coaches have access to the same SportsCode Elite file from the performances analysts available in their laptops. By opening the SportsCode file on their own laptops, coaches can review all key statistics generated by the analysts in real-time and use the information to make immediate tactical decisions. They also have access to the coded timeline, allowing them to replay footage of any actions or incidents from the match that they wish to review.

Objectives:

• Obtain video files of two different camera angles for post-match analysis

• Generate live statistics and video replays of key actions in real-time

• Display key statistics to coaches for immediate tactical decision-making

Personnel:

• Camera operators (usually Performance Analysts if event is not broadcasted) x2

• Performance Analysts x2

• Team coach x3

Technical Equipment:

• HD Camcorders x2

• Camera Tripods x2

• SD Cards x2

• SDI Cables x2

• Blackmagic Design SDI to HDMI Converters x2

• MacBook Laptops (x5)

• SportsCode License (x5)

• Ethernet Router

• Ethernet Cables (x5)

Setup:

Filming

Two HD camcorders film the match in two different angles: one camera films a wide angle capturing full areas of the pitch to evaluate team structure and positioning of players while the other camera films a tight angle closing in on the play to capture the players’ technique and closer movements. Since the footage from these two cameras needs to be stored for post-match analysis, each camera should be equipped with an SD card that contains sufficient capacity to store the footage from the full length of the game. The storage capacity of the SD card would greatly depend on the length of the match and the video quality format of the footage recorded.

In most major events, camera operators from TV broadcasters usually operate their own advanced filming equipment that already capture multiple angles of the pitch in high definition. This means that performance analysts may not require to operate their own cameras to capture match footage during these events. Instead, if the infrastructure permits, video feeds are shared to all interested parties (i.e. home and away Performance Analysts teams) by the TV camera operators by sharing an end of their Serial Digital Interface (SDI) cables connected to their cameras. These SDI cables are essential for the type of video transmissions required in sporting events, as they allow for stable transfer speeds of around 270 megabits per second in an uncompressed format. They also ensure that video quality is maintained from the camera to the receiving device.

Whenever a video feed from an HD camcorder is sent directly to a laptop via an SDI cable, a converter needs to be used to be able to connect the feed to the laptop, as most common laptops do not have SDI ports. A popular converter used in Performance Analysis is Blackmagic Design’s Mini Converters. Like with most adapters, the SDI cable coming from the camera is plugged into the mini converter, then a USB cable is then plugged from the mini converter to the laptop.

In the scenarios where a video feed is sent directly to the analyst’s laptop from a camera that does not have the analyst’s SD card inserted in it to store the footage, it is important for the analysts to record and store the incoming video feed in their laptop for later post-match analysis. To do so, performance analysts often use media capture software, such as Blackmagic Design’s Media Express, to log and capture the footage coming from the SDI video feed and store it as a video file in their computers. Often this process is followed regardless of whether there are other means to obtain the footage (i.e. SD cards or shared between Performance Analysts teams), acting as a backup option to avoid the loss of footage if any of the primary methods were to fail.

Coding

Once the filming equipment has been setup, analysts can now make use of the incoming video feed to analyse the match in real-time. The video feed cables are connected to each of the analyst’s laptops via an USB cable coming from the SDI converters. One of the analysts would input the footage into their laptop from the camera filming a wide angle while the other analysts would do the same with the tight angle.

Now that the laptops are receiving the footage from the game, analysts can open SportsCode Elite and use the live footage to code events in a new SportsCode timeline. Using the SportsCode Live Capture functionality, analysts can record the video feed and create a movie file inside the SportsCode package for the match. Recording the video feed and creating a movie file enables the software to refer back to specific coded sections of the match footage and replay the videos of specific events whenever they are selected from the timeline (i.e. show replay of the latest foul). Moreover, Analysts are able to rewind, review and re-code the footage as necessary while SportsCode continues to record the live footage into the SportsCode movie file.

The coding windows used by performance analysts to generate live statistics and video highlights during matches are prepared prior to the event. These code windows tend to follow a standardised format that is discussed and agreed with the coaching staff prior to the match. The match actions and in-play events that these code windows track would depend on the key areas of interest that a particular coach may want to have instant access to. For instance, a coach interested in closely monitoring their team’s defensive performance to make defensive adjustments may want to know the number of last third entries the opposition team has achieved so far in the game, the number of shots the team has conceded or the amount of possession given away in the team’s defensive zone. Knowing the coaches’ preferences beforehand enables a performance analyst to prepare their code window with the right level of trackers and descriptors that would provide a coach access to the right information at the right time throughout the match.

Presenting Statistics

The final part of the setup of the Performance Analysis equipment during matchday is the process required for coaches to be able to access key information in a timely and easy manner. The information generated by analysts through their live coding needs to add value to a coach’s decisions by being delivered at the right instances of the match to be able to influence decision-making and impact the team’s performance during the game.

The coded SportsCode timelines and statistics can be presented to coaches by interconnecting the analysts’ laptops with the coaches’ laptops via a local area network (LAN). This allows to create shared files from the analysts’ laptops that can be accessed by the coaches’ ones. A simple local network can be setup by plugging each laptop to a local network router using ethernet cables. Once all laptops are connected to the router, the “host” laptop (one of the analyst’s laptops) connects to the ethernet network via System Preferences > Network. The other computers can then connect to that laptops IP address by going to Finder > Go > Connect to Server > typing the host laptop’s IP address > Connect. This way, the coaches laptops would be able to access the shareable folders from the analyst’s laptops via the private local network.

A LAN connection is often a preferred option in sporting events, especially with large crowds, as WiFi connections tend to have bandwidth limitations that can significantly delay, or completely interrupt, the transfer of large video files across the network. During match events, when speed of decisions can be critical, a fast network connection is essential for coaches to received their analysts’ outputs without any delays.

The SportsCode packages being coded by the performance analysts are saved into the shared folder in the local network. As analysts continue to code the game into the SportsCode timeline, coaches can access the latest file through their own laptops at any time. The default auto-save feature in SportsCode makes sure that the file on the shared folder is always up-to-date. SportsCode’s statistical windows are also opened in coaches laptops to clearly display live statistics calculated from the coded events in the timeline.

Lastly, whenever the match venue does not permit this sort of setup, performance analysts often choose to communicate with coaches via radio to inform them of the key insights they have gathered. As previously mentioned, different sports, club venues or even playing levels have different infrastructures and venue formats allow certain setups and restrict others. Regardless of the specifics of a Performance Analysis setup, the objectives across the field remain the same: providing coaches with immediate information to make quick decisions while obtaining as much video footage from the match for post-match analysis.

Computer Vision In Sport

What Is Computer Vision?

Computer Vision (CV) is a subfield of artificial intelligence and machine learning that develops techniques to train computers to interpret and understand the contents inside images. This can also be applied to videos, as a video is simply a collection of consecutive images, or ‘frames’. Computer Vision aims to replicate parts of the complexities in human vision system and visual perception by applying deep learning models to accurately detect and classify objects from the dynamic and varying physical world.

The first basic neural networks were developed around the 1950s to detect edges of simple objects and sort them into categories (i.e. circles, triangles, squares and so on). These systems were further developed to help the blind by enabling them to recognise written and typed text and characters using a method known as optical character recognition. By the 1990s, the rise of the Internet meant that unprecedented datasets of millions of images were regularly being shared and generated across the web. These extensive visual datasets enabled researchers to better train their models and develop face recognition programs that helped computers identify specific pictures inside of photos and videos.

Today, the advancements in smartphone technology, social media and their frequent use by billions of users - more than 3 billion images are shared online every day – is continuously generating even greater amounts of visual data than ever seen before. Together with the increased accessibility to large computer power and the innovations in deep learning and neural networks algorithms (i.e. the invention of convolutional neural networks), the availability of such immense amounts of images have brought invaluable opportunities for computers to learn the patterns and characteristics of these images and enhance the accuracy rates for object detection and classification. As a result, computer vision systems have surpassed the accuracy of human vision at certain detection, categorisation and reaction tasks, reaching accuracy rates of 99% in a number of their applications.

How Does Computer Vision Work?

Computer Vision is now able to perform a variety of tasks in a wide range of fields, from self-driving cars to medical diagnosis. Some of these tasks include photo classification, object detection, face recognition and searching image and video content. In order to perform these tasks, computers first need to be able to generate information from images (i.e. “see” the image). Since computers can only operate using numerical values (i.e. bits), they first need to read an image in its most raw numerical form: the matrix of its pixels. This matrix represents the brightness of each pixel in an image, from the darkest black (at value 0) to the brightest white (at value 255).

Images are a made up of thousands of pixels. These pixels are one-dimensional arrays with values from 0 to 255. One single image will contain three different matrices for the three components that represent the three primary colours: red, green and blue (RGB). By combining different brightness levels of the different primary colours (from 0 to 255), a pixel can display alternate colours to those primary ones. For example, a pixel that displays a vivid colour purple will have the values Red=128, Green=0 and Blue=128 (mixing red and blue results in purple), while a vivid yellow pixel in an image will contain values Red=255, Green=255 and Blue=0 (mixing red and green results in yellow). On the other hand, a grayscale image will only contain one single pixel matrix corresponding to the brightness of its black and white colours.

Deep learning algorithms in computer vision make use of these pixel arrays to apply statistical learning methods, such as linear regression, logistic regression, decision trees or support vector machines (SVM). By analysing the brightness values of a pixel and comparing it to its neighbouring pixels a computer vision model is able to identify edges, detect patters and eventually classify and detect objects in an image based on previously learned patterns. These methods often require the model to have already previously processed, stored values and learned patterns (i.e. to have been trained) of similar images containing the object of interest to be detected and tracked in the new, unseen image.

For example, to be able to detect a person in an image, a significantly large number of pre-labelled images of people are uploaded into the system, allowing the model to learn on its own by recognising patters in the features that make up a person. Once a new, not previously seen image is fed to that model, the computer will look for patterns in the colours, the shapes, the distances between the shapes, where objects border each other, and so on. It will then compare them to the characteristics from the images and labels it had previously identified and decide, based on probabilistic rules, whether there is a person or not in this new image. In other words, computer vision systems are able to ingest many labelled examples of a specific kind of data, extract common patterns between those examples and transform it into a mathematical equation that will help classify future pieces of information.

Often, computers require images to be pre-processed prior to applying any detection and tracking models to them. Image pre-processing simplifies and enhances the image’s raw input by changing its properties, such as its brightness, colour, cropping, or reducing noise. This modifies the pixel matrices of the images in a way that a computer can better perform its expected tasks, such as removing a background in order to detect objects in the foreground. This is particularly useful in video footage, where computer vision can track moving objects using a discriminative method to distinguish between objects in the image and the background. By separating the two, it can detect all possible objects of interest for all relevant frames and use deep learning techniques to recognise the specific object to track from the ones detected.

Deep learning models are often trained to automate this process by inputting thousands of pre-processed, labelled or pre-identified images. Training of models can follow a variety of techniques, such as partitioning the images into multiple pieces to be examined separately, using edge detection to identify the edges of an object and better recognise what is in the image, use pattern detection to recognise repeated shapes, colours or other indicators, or even use feature matching to detect matching similarities in images to help classify them. Models may also use X and Y coordinates to create bounding boxes and identify everything within each box, such as a football field, an offensive player, a defensive player, a ball and so on. More than one technique is frequently used in conjunction to improve the accuracy and precision of object detection and tracking in an image or video.

The Applications Of Computer Vision In Sport

In sports, artificial intelligence was virtually unknown less than five years ago, but today deep learning and computer vision are making their way into a number of sports industry applications. Whether it is used by broadcasters to enhance spectator experience of a sport or by clubs themselves to become more competitive and achieve success, the reality is that the industry has substantially increased its adoption of these modern techniques.

Most major sports involve fast and accurate motion that can sometimes become challenging for coaches and analysts to track and analyse in great detail. This is particularly difficult in those situations when the use of wearable tracking equipment and sensors to augment data collection is not an option. In training sessions and certain matches, especially if they are untelevised, performance analyst are only able to obtain a limited number of angles of video footage. This footage is limited to providing visualisation of the player’s movement rather than detailed analysis. The data and insights obtained from the footage requires the analyst to spend numerous hours manually notating and collecting events as they replay the video. Scenarios such as this is where the application of computer vision techniques can bridge that gap between the sporting event and analytical insights by offering novel ways to gather data and obtain valuable analysis through automated systems that locate and segment each player of interest and following them over the duration of the video.

In the context of sports, footage is usually acquired through one or more cameras installed at close proximity of where the event takes place (i.e. the sidelines of a training field or the stands in a stadium during a match). The angle, positioning, hardware and other filming configurations of these cameras can vary greatly from sport to sport, event to event or even within the different cameras used for the same match or training session. This can pose a challenge for certain computer vision applications to accurately detect the precise positioning of objects or their direction of movement as they may fail to understand the varying configurations used to capture the different footage presented to them, where it is for training the models or classifying new, unseen images.

Traditionally, costly camera calibration for multi-camera tracking systems was essential ball and player tracking systems. For fixed-angle cameras, this could be done through scene calibration, where balls were rolled over the ground to account for non-planarity of the playing surface. However, broadcast cameras present additional challenges in that they often change their pan, tilt and zoom. This dynamism needed to be accounted for by using sensors on the camera mounting and lens to measure zoom and focus settings and be able to relate the raw values from the lens encoders to focal length. Gaining access to these advanced filming equipment is not often an option for most Performance Analysis departments within sporting clubs, limiting their capacity to apply advanced tracking of players.

Computer vision has partially solved these limitations. With its application of image processing, computer visions systems are now able to distinguish between the ground, players and other foreground objects. Methods such as colour-based elimination of the ground in courts with uniformly coloured surfaces allow computer vision models to detect the zones of a pitch, track moving players and identify the ball. For instance, colour-based segmentation algorithms are currently being used to detect the grass by its green colour and treat it as the background of the image or video frame, where players and objects move in front of it. Moreover, image differencing and background subtraction methods have also been used on static footage to detect the motion of the segmented foreground players against the image background.

Player Tracking

One of the key aims when applying computer vision in sports is player tracking. This involves the detection of the position of all players at a given moment in time. Player tracking is a pivotal element for coaches to help improve the performance of their teams, allowing them to instantly analyse the ways in which individual players move on the field and the overall formation of their team. Today, the most advanced applications of computer vision in sport use automated segmentation techniques to identify regions that likely to correspond to players.

The results obtained from a computer vision system can be augmented by applying machine learning and data mining techniques to the raw player tracking data. Once key elements in an image or video frame are detected, semantic information can be generated in order to create context on what actions the players are performing (i.e. ball possession, pass, run, defend and so on). These techniques can label semantic events, such as ‘a one-two pass’ in football, and be used for advanced statistical analysis of player and team performance. Suggestions can also be constructed on the optimal positions of players on the pitch and be displayed to coaches in a manner in which they can compare ideal player positioning against their actual positions in a given play. The vast opportunities created from this player tracking technology has the potential to revolutionise training and scouting for players in sports.

Data Collection

The use of action and event recognition techniques aim to localise sets of actions that a player performs in both space and time. These techniques can detect events – such as goals, penalties, near misses, and shots - during video clips by identifying visual information about the environment, such as court colour and lines on the pitch. They then use that information to classify each action into sport-specific groups by assigning them labels (i.e. shot, pass, etc.). Ultimately, action recognition and classification can be used to automatically generate performance statistics in a match or training session, such as shot types, passes or possession. It can also be applied to index videos by predefined themes based on their contents to be able to easily browsed through footage and automatically generate highlights movies.

How Is Computer Vision Used In Different Sports?

In racket and bat-and-ball sports, such as Tennis, Badminton or Cricket, computer vision has been widely used since the mid-2000s. Ball tracking systems attempt to look through each camera image available to identify all possible objects resembling the characteristics of a ball (i.e. searching for elliptical shapes in an expected size range). Once these objects have been detected, they then construct a 3D trajectory of the playing ball by linking multiple frames where the ball was detected to define the ball path across the various camera angles. The results from this system can then be used to instantly determine whether a ball has landed in or out of bounds. The system provide further analysis, such as predicting the path that a cricket ball would have taken if the batsman had not hit it.

An example of the use of computer vision in tennis can be spotted in one of the major tournaments in the sport. In 2017, Wimbledon partnered with IBM to include automated video highlights picking up key moments in the match by simply gathering data from players and fans, such as crowd noise, player movements and match data. Similarly, on the commercial side, a pocket-sized device was designed by Grégoire Gentil that called in and out in a tennis match by using computer vision to detect the speed and placement of a shot and determine whether the ball was out of bounds.

Other major invasion team sports have not been indifferent to the emergence of these new technologies. In football, FIFA certified goal line technology installations in major stadiums using a 7-camera computer vision system developed by Hawk-Eye. It uses a goal detection systems with multiple view high-speed cameras covering each goal area that detect moving objects by sorting potential objects resembling the playing ball based on area, colour and shape. With an accuracy error rate of 1.5cm and a detection speed of 1s, it enables football referees to immediately decide whether or not a ball has crossed the goal line and a goal should be awarded.

Aside from widespread implementations of computer vision, such as FIFA’s goal-line technology, other ad-hoc projects have also attempted to incorporate computer vision into football. In the 2011/2012 football season in Germany, Stemmer Imaging helped Impire develop an automatic player tracking system using two cameras in the press area of any stadium. This reduced the number of operators required to get accurate data without losing the quality of the information.

In American sports, such as the NFL, computer vision has been applied to automatically generate offensive formation labeling by classifying video footage based on the coordinates of players when tracked throughout a particular play. This application has supported coaches and analysts in the evaluation of oppositions’ patterns of play by generating a wealth of data on the most common formations employed by rival teams. Furthermore, the system has provided teams with additional information on oppositions’ tactics, such as the likelihood of passing or running out of each formation, run frequency for each side of the field, split between right guard and right end, frequency of runs up the middle, pass frequency on short routes, and average yard gains between running and passing plays.

Challenges Of Computer Vision

Despite the great potential that computer vision can bring to the world of sport and the field of performance analysis, there are still critical challenges that need to be overcome before that potential can be fully exploited. Some of these challenges relate to the fact that computer vision cannot yet fully compete with the human eye. A system that fully automates video analysis of sports by tracking and labelling players remains a challenge as optical tracking systems cannot yet cope with the varying body posture of a person during sports exercises, as well as the partial or full occlusion of players by equipment or other players during collisions or interactions. Tracking of sports players is also particularly challenging due to the fast and erratic motion, similar appearance of players in team sports, and often close interactions between players.Tracking the ball is a further challenge in team sports, where several players can occlude the ball (i.e. a ruck in Rugby Union), and it is possible that players are in possession of the ball with either their hands or between their feet.

The reason for these to continue to be a challenge within the field of AI and computer vision is that we still do not completely understand how human vision truly works. Even though the field of Biology studies the eye, the visual cortex and the brain, we are still far from fully understanding all the components of such a fundamental function of the human brain. For instance, how the influence of our memory, past experiences and inherited knowledge through billions of years of evolution impacts our perception and our ability to identify elements in our world. This lack of detailed understanding of human vision and our abstract perception makes it difficult to replicate our inherited knowledge of the world through a computer. On top of that, the external dynamism, variance and complexity of our physical world proves an extreme challenge to solve through computers that have to be thoroughly instructed on the types of objects, captured through the lens of a camera, that they must detect. Particularly when they are unable to deviate from what they have been trained to identify.

Nevertheless, the field of AI and computer vision continues its rapid development thanks to heavy investments by key players, such as Google, Intel, Amazon and many others, to continue to advance the computer power, increase datasets and develop new techniques that get closer to our human vision capabilities. Eventually, these advances will inevitably continue to make their way into the world of sport as athletes and teams aim to leverage modern technologies to improve their performance and become even more competitive. As performance analysts continue to support these athletes and coaches in objective evaluation of performance, it is without a doubt that the expansion of computer vision will eventually transform key areas of Performance Analysis in sport.

Citations and further reading:

• Brownlee, J. (2019). A gentle introduction to computer vision. Machinery learning mastery. Link to article.

• Dickson, B. (2019). What is Computer Vision? TechTalks. Link to article.

• Dickson, B. (2020). What is Computer Vision? PC Mag. Link to article.

• Kaiser, A. (2017). What is Computer Vision? Hayo. Link to article.

• Le, J. (2018). The 5 computer vision techniques that will change how you see the world. Heart Beat. Link to article.

• Lu, W. L., Ting, J. A., Little, J. J., & Murphy, K. P. (2013). Learning to track and identify players from broadcast sports videos. IEEE transactions on pattern analysis and machine intelligence, 35(7), 1704-1716. Link to paper.

• Mihajlovic, I. (2019). Everything you ever wanted to know about Computer Vision. Towards Data Science. Link to article.

• Monier, E., Wilhelm, P., & Rückert, U. (2009). A computer vision based tracking system for indoor team sports. In The fourth international conference on intelligent computing and information systems. Link to paper.

• Sennaar, K. (2019). Artificial Intelligence in sports – current and future applications. Emerj. Link to article.

• Softarex. (2019). Computer vision and machine learning in sports analytics: injury and outcome prediction. Softarex. Link to article.

• Thomas, G., Gade, R., Moeslund, T. B., Carr, P., & Hilton, A. (2017). Computer vision for sports: Current applications and research topics. Computer Vision and Image Understanding, 159, 3-18. Link to paper.

What is Performance Analysis in Sport?

Since the early-2000s, the analysis of performance in sport has seen a dramatic transformation in both its methods (i.e. incorporating advanced statistical modelling and new analytical frameworks) and technologies (i.e. GPS tracking, time-lapsed notational analysis software and a large variety of tracking sensors and other tracking equipment). What started as shorthand notations with pen and paper has since evolved to advanced computerised systems and technologies that collect vast amounts of performance-related data.

The rise in lucrative financial opportunities in most major sports thanks to the ever-growing revenues from broadcasting deals and the rising global audiences have inevitably raised the stakes of winning. Consequently, sporting organisations are now turning to more scientific, evidence-based approaches when managing their institutions and developing their athletes. Standards in elite sports to achieve and maintain success are continuously being raised, placing increasing pressure on clubs, coaches and athletes to develop more efficient training structures, enhance athlete development processes and gain better understanding on the factors that determine success in major tournaments.

The highly competitive environment with constantly narrowing margins have triggered the emergence of Performance Analysis as an independent, yet interdisciplinary, backroom function that specialises on the objective, and most often quantitative, evaluation of performance. This relatively new field aims to support coaches in identifying key areas of performance requiring attention, evaluating the effectiveness of tactical and technical performance, as well as the strengths and weaknesses of upcoming oppositions. Its purpose is to provide valid, accurate and reliable information to coaches, players and any relevant stakeholders to augment their knowledge on a particular area of the sport.

Traditionally, Sports Performance Analysis has been defined as an observational analysis task that goes from data collection all the way to the delivery of feedback, and aims to improve sports performance by involving all coaches, players and analysts themselves. The observation of performance is carried out either live during the sporting event or post-competition through video footage and gathered statistics. Performance Analysts can now be spotted in stadiums, whether in the coaching box or a separate good viewing location within the stands, notating events and actions from the match using specialised software, such as SportsCode, Dartfish or Nacsport. In this process, they develop statistical reports that can be sent in real-time to the devices used by coaches (i.e. iPhones or iPads) and display to them a summary of key performance metrics, as well as short video feeds of key highlights. However, the additional time available in post-match analysis allows for a more detailed evaluation of performance using additional complementary sources of data. The data used during post-match analysis can come from sources beyond the analyst’s observations, such as qualitative data, video sequences and even measurements athletes’ exertion, heart rate, blood lactate levels, acceleration, speed and location metrics collected through wearable devices. Some of these data will often be sourced internally within the club but external sources, such as that of data provides like Opta, are often utilised across multiple sports to complement internal databases. Training sessions are also subject to analysis, with continuous monitoring of players to inform debriefing sessions by coaches and help plan the next session.

Research in the field has also emerged as its own specialised field. The International Journal of Performance Analysis in Sport now regularly publishes studies on key sports analysis research areas, such as the identification of key performance indicators, injury prevention through work-rate analysis and physical analysis, movement analysis, coaches’ behaviours and feedback processes, effectiveness of technique and tactics, normative profiling, overall match analysis and even the analysis of referees’ performance.

Performance Analysis As Its Own Backroom Function

Over the last two decades, Performance Analysis has established itself in many top sporting clubs and organisations as a pivotal element in the extrinsic feedback process that coaches use to accelerate the learning process and assist athletes reach their optimal performance levels. It is now considerate its own separate function within the backroom staff of a team, having differentiated itself from other sports science disciplines its core focus on quantitative performance evaluation, yet with a high degree of cross-functional aspects requiring it to maintain a close relationship with wider sports science disciplines. For instance, a work-rate analysis performed by a Strength & Conditioning department may complement the work of a Performance Analyst team on informing player selection based on both performance metrics and player fitness.

The Purpose Of Performance Analysis In Sport

The large volume of quantitative and qualitative information produced from the complex and dynamic situations in sport needs to be carefully disseminated and clearly presented – using clear visuals such as tables, charts or special-purpose diagrams of the playing surface - to allow coaches to obtain quick insights on areas requiring their attention. Performance Analysis enhances the coach’s ability to ‘feed-forward’. It aims to anticipate an opposition’s strengths and weaknesses by performing thorough opposition analysis to produce acquired knowledge that allows the team to rehearse appropriate plays and improve those individual skills that would aid to outperform the upcoming opponent.

The insights generated through Performance Analysis work such as opposition analysis help coaches make informed decisions on tactical choices and squad selection that would better exploit the weaknesses and overcome the strengths of a given opponent. Traditionally, these decisions were made in its entirety following a coach’s acquired wisdom through years of experience in the sport, often having previously played at elite levels themselves. However, studies have repeatedly proven that coach recall capacity of critical incidents that take place in a sporting event is limited to between 42% and 59% of events. On top of that, the events that are remembered are prone to incompleteness, emotional bias, inaccuracy and misinterpretation due to the natural flaws in human perception and cognitive capacity. Cover for these limitations in an increasingly more competitive environment coaches have turned to technology and analytics to have immediate access to both objective information of past events as well as instant video footage to review specific events they wish to recall and re-evaluate. For this, most top-level coaches now benefit from their own Performance Analysts departments that provide them with the necessary data collection, data manipulation, analytical and video analysis skills to allow them to take advantage of the vast amounts of information generated from their sport, yet receive those key elements most important to them in a clear, timely and concise manner.

The Scope Of Performance Analysis In Sport

Technical Analysis

The development of better athletes, from elite levels to grassroots programs, has been a key focus of the field of Performance Analysis in Sports over recent years. The mechanical detail of skills performed by athletes are carefully analysed to detect flaws in technique, monitor progress and identify changes during preparation or even assess rehabilitation from injury. The effectiveness in which an athlete performs specific skills or a broader passage of play is measured, compared and classified, either positively or negatively, against a predetermined expected outcome. For example, a coach may expect a minimum passing completion rate from its midfielders or a minimum speed from its wingers in football. Often, these measurements are presented as ratios or percentages of successfully performed skills, such as the percentage pass completion or tackling success. They are then used to develop performance profiles of players that are used to benchmark and compare them against teammates or rival players.

Tactical Analysis

Similarly, tactical analysis carried out by Performance Analysts help coaches better understand the impact of their tactical decisions. It can also help identify specific tendencies and preferred tactical setups by opposing teams. By leveraging the latest video analysis and player tracking technologies, Performance Analysts are now increasingly more capable of evaluating patterns of play in conjunction with skills performed, location on the field, timings and players involved to draw an accurate representation of tactical variances given particular match scenarios.

Physiological Analysis

Player movements are also carefully assessed to ensure they achieve positions of advantage, as well as desired velocities, distance covered and speed ranges. This line of work by Performance Analysts is closely complemented with the work by a Strength & Conditioning team. The aim is to enable the athlete to achieve their optimal physical condition by providing performance analysis on areas relating their strength, power, endurance, agility, stability and mobility. Injury prevention is also a priority, especially in sports with intense physical contact where likelihood of injury is high. GPS trackers and other wearable technologies are combined with video analysis to understand the physical efforts that players go through during training and matches and allow coaches to better manage the intensity of sessions.

Psychological Analysis

Psychological training is a key element of the coaching process when it comes to mentally preparing athletes to the pressures of a sport and the challenging conditions that may impact their motivations and ambitions of reaching their desired goals. Performance Analysts are able to support coaches through the evaluation of an athlete’s discipline, exertion, efforts and other fluctuations of work-rate that could be associated to mental factors it an attempt to minimise effects of negative mental influences and positively influence athletes. Most often, Performance Analysts use their video analysis abilities to create motivational clips and video highlights that can support coaches with the mental preparation of their teams and athletes.

Equipment And Technologies In Performance Analysis In Sport

Today, most Performance Analysis departments at elite clubs start their analytical process by recording video footage of training sessions and competitive events. Often, more than one HD camcorder is set up at high viewpoints on the sidelines of training pitches or stadiums to collect footage in various angles, whether is at a closer angle capturing just a few players or a wider angle of the full sections of the pitch. In some instances, drones are also used to capture an even wider angle from above the players on the pitch to be able to clearly identify gaps during plays or structural setups and formations. Certain actions during training sessions may also allow for the Performance Analyst to get physically closer to the play and use a handheld camera, such as a GoPro, to capture an additional angle that shows closer movements and player technique. The footage from the camcorders is captured into SD cards inside the cameras or directly into a laptop using media management software, such as Media Express from BlackMagic Design. Often both are used in conjunction to act as a backup of each other. Alternatively, Performance Analysts may also obtain video feeds for certain matches or competitive events that are broadcasted from the broadcasters themselves, freeing up their time to perform additional real-time data collection and analysis during the event.

Once the video footage is gathered, Performance Analysts leverage the capabilities of time-lapsed computerised video analysis software, such as SportsCode, Dartfish or Nacsport, to notate key events and actions and generated meaningful data for later analysis. These solutions allows them to replay the training session or match and tag key events to construct a database with frequency counts, length of specific actions and supportive contextual information of each individual action (i.e. whether a tackle was successful or a missed opportunity). Coaches and players can later go through the coded timeline of the event and view specific video highlights automatically generated by the software. Analysts would then export the frequency data into data manipulation and analysis software, often being Microsoft Excel, and perform further analysis on the data and combine it with historical datasets, data from wearable tracking devices - often players wear GPS trackers, such as Catapult, StatSports or Playertek - or even data obtained from external sources and data providers, such as Opta.

The insights generated from the analysis are then delivered to the interested parties, coaches or players. The method of delivery varies greatly from club to club and depends greatly on the audience receiving the information. Summary reports may be printed and distributed amongst players and coaches with key statistics and areas requiring attention. In other occasions, data visualisation software such as Tableau may be used to interactively display charts and other visuals of team and player performance. Most often, coaches and players get a great deal of value from watching replays and highlights of the areas being analysed. Therefore, analysts often create short highlights clips using video editing tools, such as CoachPaint, KlipDraw, Adobe After Effects or Premiere Pro, or simply Apple’s iMovie application, to produce a combination of notated footage that clearly displays the information they want to portray to the coaching staff and team.

What Is Next For Performance Analysis In Sport?

As technology continues to evolve and data-related solutions increasingly bring new functionality to the field, the field of Performance Analysis will continue to grow. New technologies will bring new opportunities for sporting organisations to become even more competitive and better maximise their athlete’s potential. Inevitably, as a club’s main goal is to outperform and outsmart its competitors, this will continue to raise the standards of success in all major sports, where investment in solutions and human resources that allow them to exploit these new opportunities will continue to increase overtime, given that the financial incentives of winning will remain lucratively attractive to owners and investors.

However, further advances in technology and the sophistication of processes will also bring new complexities to the environment that Performance Analysts will operate in. This will place additional pressures to the skills demanded in the field, where not only a good acumen of a sport and coaching processes will be needed, but also highly technical skills to effectively navigate a growing data ecosystem will be essential. Inevitably, some of the current manual and repetitive tasks will be automated using modern solutions. For instance, analysts often make use of video analysis software to manually code every single event as it takes place in the footage. However, computer vision could eventually replace these repetitive and labour-intensive tasks during data collection from video footage by automatically detecting and tracking players and moving objects (i.e. the ball) in the field and performing frequency counts using pre-programmed functions. This automation enable clubs to free-up resources from the Performance Analysis departments and allow analysts to reallocate their time into generating insights through deeper analysis of the collected data.

The field of Performance Analysis is, today, at its early stages. Different sports are at different stages in their adoption of this new and critical function inside their backroom teams. Some are not yet considering Performance Analysis a priority when hiring and developing such teams. The novelty of the field, a limited understanding of its use and benefits by owners and club decision-makers, as well as the competitive labour market, where wealthy companies from other industries are also interested in hiring individuals with an analytical and technical skillset, has challenged the consolidation of Performance Analysis in certain sports. However, not all sporting clubs and institutions have been slow at their incorporation of specialised analysis of performance. Wealthier and more established clubs have been able to experiment and appreciate the benefits of investing in the skillsets that have allowed them to better understand key factors of success and develop their athlete’s performance through acquired knowledge that has placed them above their rivals. These innovative actions taken by top-tier teams have usually had an effective trickle-down effect on the rest of clubs within a sport, where the rest of rivals follow suit in order to remain competitive. As the field continues to grow in line with technology, we will undoubtably see an exciting evolution in the composition and structures of coaching teams and sporting organisations as a whole.

Citations and useful resources:

• Laird, P., & Waters, L. (2008). Eyewitness recollection of sport coaches. International Journal of Performance Analysis in Sport, 8(1), 76-84.

• McGarry, T., O'Donoghue, P., & de Eira Sampaio, A. J. (Eds.). (2013). Routledge handbook of sports performance analysis. Routledge.

• O'Donoghue, P. (2009). Research methods for sports performance analysis. Routledge.

• O'Donoghue, P. (2014). An introduction to performance analysis of sport. Routledge.

The Increasing Presence Of Data Analytics In Golf

Dating back to the 15th century, golf is one of the most traditional sports in the world. Even in its modern form, it continues to maintain most of its original characteristics and etiquette from centuries ago. However, golf has not been immune to the technological revolution that has seen many individual and team sports adopt the latest data technologies to optimise performance and enhance entertainment value for fans.

In today’s golf, every single aspect of the game, from a player’s swing to their round strategy and even the equipment they use is being transformed through scientific advances, data analysis, machine learning and cloud technologies. Impressively, this highly traditional sport has rapidly embraced data analytics as a means to provide a deeper understanding and enjoyment of the game. As a sport with one of the tightest of margins amongst its elite players, where one single dropped shot can cost you a tournament, golfers have turned to technology to develop an intelligent and information rich training regime and strategy to improve their chances of winning.

The Largest Golf Database By PGA Tour

One of the first developments that triggered the data revolution in golf dates back to 2003, when PGA Tour partnered with CDW to create an advanced ball-tracking system: ShotLink. The concept of ShotLink was first designed in 1983 as an electronic scorecard to catalogue historical data. However, technological advancements allowed CDW and PGA Tour to develop an improved system that aimed to break down every detail of every stroke taken by every player to facilitate the analysis of each player’s round and overall performance. The objective was not only to help players improve their game through data, but was also considered as an attempt by the Tour to help make the sport more accessible to modern players and fans.

Since its launch, ShotLink has dramatically evolved over the years to the point that it can now laser map each golf course and create a digital image of each hole to calculate exact locations and distances between any two coordinates, such as the location of all players and their distance to green. The system has been continuously upgraded in line with its increasing adoption by most of golf’s data ecosystem, through apps, devices, software and consultancy agencies available today.

One of the latest improvements PGA Tour has made to its data collection system is the installation of three fixed, high-resolution cameras that replaced the human-operated laser on every green to capture the ball in motion. Thanks to ShotLink, PGA Tour have managed to develop a database of 174 million shot attributes and 80,000 hours of video over the past 20 years in operation. But once the data had been collected, practical insights needed to be produced from the large number of individual data points gathered over the years. To make sense of such large dataset, they partnered with Microsoft to leverage artificial intelligence through Azure cloud-based services and create a Content Relevancy Engine (CRE) that processed ShotLink’s immense database to find the most relevant, most interesting stats that are contextual.

Today, ShotLink is used in 93 events per year. Its data feeds are accessed by broadcasters as well as top-flight players, who use the statistics from the system to analyse, compare their performance against competitors and improve their play. But not only players have benefited from the introduction of this high-tech system. Through ShotLink, PGA has managed to enhance viewers entertainment experience when watching the sport by making the ball highly visible through television.

The statistics captured through ShotLink have also been turned into into eye-opening insights that have increased the level of engagement from most golf fans. By having unprecedented data available for analysis, PGA Tour was able to uncover valuable insights relating to the different patterns of play amongst top PGA players. Some of these interesting insights included:

• Winning players tend to make a higher number of putts between 11 and 20 feet away.

• A third of all putts are over 20 feet of distance, with better golfers often leaving themselves 3 feet or closer on the first putt.

• 99% of PGA players make puts within 3 feet distance.

• Top golfers rarely go three-putt or over.

• Hitting the fairway means the PGA golfer will under par on the hole.

• Top players average under par after hitting the rough, which adds 0.25 of a shot to the hole.

• The most frequent approach shot distance range is 150-175 yards. From there, 71% of PGA golfers hit the green from the fairway; but need to be between 75-100 yards to hit 71% from the rough.

• Golfers gain shot advantage instead of losing it if they aim 25, 30 or 35 yards back to avoid the rough or other hazards.

• Golfers should always aim for the green instead of laying-up on a par 5 that has no water or hazards around the green. This allows them to hit their third shot from within 50 yards of the hole, increasing their chances of cutting their putting distance and error rates in half.

• An improvement of a half-stroke per round increases a player’s earning potential by 73 percent.

Development Of Data Gathering Systems, Devices And Smart Equipment

The technological revolution in golf has brought new devices and systems that can now provide statistical analysis to enhance training, playing and viewing experience of the sport.

One of the most crucial and difficult aspects of golf is the swing. It is considered one of the most complex sequence of movements in any sport, with muscle groups of the whole body involved to provide the millimetric, biomechanical prerequisites to transfer the swing energy efficiently and accurately to the golf ball. Therefore, it is not surprising that swing sensors, grip guides, shot trackers, laser rangefinders, and even virtual caddies, that help inform and improve the swing in varying circumstances have increasingly become more predominant amongst professional and amateur golfers to help them achieve the perfect swing.

Some of these devices include systems like TrackMan or K-Motion, which monitor granular variations in motion using a combination of HD cameras and microwave transmissions that reflect back from a moving golf club and golf ball and capture data of what happens at the exact moment of contact with the ball. Others, such as inertial sensors and depth cameras for 3D analysis like Golf Integrated, have been used to evaluate the swing of golfers in relation to their joint length and initial posture. These systems are able to display many factors of the golfer’s swing, such as club head launch speed, distance carried and ball spin. With the captured movement, they provide expert interpretative biomechanical reporting on body, arm, hand and club motions, as well as balance and weight distribution, during each golf swing.

Additionally, systems that use highspeed, high-resolution cameras, such as Foresight Sports’ GC2 Smart Camera System, are also able to measure club performance and ball launch data, such as ball speed, total spin, launch angle, deviation angle and spin tilt axis, to determine the ball trajectory, peak height, angle, distance in relation to initial launch condition and total final distance including bounce and roll. In combination with Foresight Sports’ HMT Head Measurement Technology, Foresight’s Sports’ devices can measure the delivery of the club head in terms of path, face plane, closure rate, velocity and impact location of the golf ball. All these data points are intuitively displayed in Foresight Sports’ Performance Fitting app using illustrated depictions of ball flight and club head data.

Traditional golf equipment is also experiencing significant change with the incorporation of analytics and technology into its manufacturing. Cobra Golf’s KING F8 club lines developed clubs with connected smart grips powered by an embedded Arccos computer sensor that tracks and analyses a golfer’s performance through shot tracking, distance calculation and location. These clubs come with their own smartphone app that uses GPS to track positioning and displays multiple analytics on the golfer’s performance, such as strokes gained and handicap breakdowns for driving, approach, chipping, sand and putting. Golf balls are also getting smarter. Coach Labs’ GEN i1 and i2 smart golf ball and OnCore’s Genious Ball now contain nine-axis sensor and on-board MCU that acts like a miniature launch monitor to measure initial direction, speed, impact force and ball rotation during putting and direction, spin rate, distance and speed in full swing and transmits the data to a smartphone app.

Amateur players have also seen their golfing experience expand thanks to technology. For instance, recreational players can now enhance their playing skills and enjoyment of the game through systems such as virtual caddies. Arccos Golf developed an Arccos Caddie solution that uses wireless club mounted sensors that attach directly to the player’s golf clubs, as well as using GPS trackers from smartphones, to collect player performance data in real time. The system can track which clubs the player uses, where they hit the ball and how many shots it took to complete each hole, broken down into driving, approach, chipping, sand, and putting. Arccos Caddie uses Microsoft’s Azure Machine Learning to leverage artificial intelligence against the 120 million shot data and 368 million geotagged data stored in its system from 40,000 golf courses to provide golfers with specific advice on how far to hit each shot, which club to use and how to make corrections as they play their round. It also offers golfers their optimal strategy off the tee after considering their likely shot distance as impacted by wind, weather, elevation and other factors. It can also calculate for them their expected score and odds of making par, their likelihood of hitting the fairway, and their chances of missing to either side. For example, it can detect a player’s tendency to miss fairways to the left with the 3-wood, or even a glaring inability to hit the green with the 8-iron.

Generating Valuable Information By Contextualising The Data Collected

Sensors, GPS, cameras and other tracking devices are unable to paint a complete picture of a player’s performance without the underlying analytics to tell the story. Even though increasing amounts of raw data points, such as swing speed, can now be captured with these new devices, analytics is pivotal to generate value and context from such vast data.

In 2017, GolfTEC tested 13,000 pro golfers and amateurs across 48 different body motions per swing using motion sensors, cameras and monitors in a study the labelled as SwingTRU Motion Study. The study aimed to define what makes a great golfer. They found that the difference between a competent golfer and a top one can be summarised in their hip sway and shoulder tilt at the top of the swing and then point of impact, as well as the hip turn at the point of impact and the shoulder bend at the finish of the swing. By statistically correlating these factors to better performance, GolfTEC developed a benchmark in which golfers can compare themselves in these different areas and make improvements.

Moreover, USGA is making use of its database of 2 million golfers and 50 million scores collected through the Golfer Handicap Information Network by developing an algorithm that creates a professional-style benchmarking ability at the recreational level to allow golfers at all levels to compare their game against others and gain insight into how they are playing. For example, this system enables amateur golfers to compare their Saturday’s round on a relative basis among the 150 others who played the same course that day.

Furthermore, there are numerous in-depth golf analytics websites, such as GOLFstats.com or the official PGA Tour website, that have emerged to take advantage of the technological wave in golf and provide data accessibility. These websites provide fans and players access to vast amounts statistics on professional golfers and tournaments at an incredible level of granularity (i.e. their longest driving average or the number of fairway hits). Additionally, the Canadian site DataGolf.org has made available a live statistical model that displays the probabilities of every player’s winning changes for every PGA Tour and PGA European Tour as they happen. By mid-2018, their predictive model was outperforming most major betting companies. They present their data through outstanding data charts and other visualisations, including historical numbers dating back to 1990.

Other websites and mobile apps, such as ShotByShot.com, Arccos 360, Anova or Golfmetrics, have also started to leverage the use of advance analytics to improve amateur golfers’ game. Any player can now have access to the right tools that allow them to easily and accurately track different data points of their game, from driving, approach shots, sand shots to putting. These apps statistically break down a player’s game to help them identify the areas that most significantly improve their overall performance. They aim to accurately pinpoint a player’s strengths and weaknesses in driving, approach shots, short game and putting, and in more detailed subcategories using the strokes gained metric popularised by Mark Broadie. Through these apps, a player would enter their scores in the app, which in return will calculate their strokes gained values and compare them against golfers at various levels. The website or app will record and analyse the player’s data, determine the relative handicaps of their game and then identify the highest improvement priority and contributing factors to improve their game.

Data Analytics Agencies Are Supporting Golfers Make Sense Of Their Performance Data

Performance Analysis agencies and consultancies, such as Golf Data Lab or TeeBox Golf, have started to emerge in professional golf. These agencies often provide golfers with tailored technical support and produce objective analysis of their game to identify trends and assess strengths and weaknesses. Teams of analysts record a golfer’s round and provide them, or their caddy, a detailed breakdown of their performance with comparisons against previous rounds and other competitors. Some of the statistics collected and analysed by these agencies include:

• Driving accuracy to fairway

• Par 3, 4 and 5 accuracy analysis

• Long, medium and short iron approaches

• Short game analysis (<50 yards)

• Putting analysis (including data such as conversion per distance, 3 putt frequencies, tap-in rates and missed putts analysis)

• Clubs used and club efficiency

• Shots type

• Dropped shots analysis

• Comparisons with PGA averages

• Drive versus approach analysis

• Strike quality examination

• Directional tendencies

Consultancies like 15th Club, an unofficial stats partner for the 2016 European Ryder Cup team, have now established themselves as key influencers in the European game, from informing qualification process and captain’s picks to the partnerships and singles order. Through their valuable application of data intelligence, they have become another crucial voice in preparing every member of the European Tour and defining their training structures. They now work with over 40 professional golfers, who have seen an average increased in earning of $600,000 by simply improving their stroke by +0.15-0.25 per round. Similar to ShotLink in America, 15th Club uses GPS, lasers and cameras operated by a group of people to collect all the necessary data points to build their algorithms and models. Additionally, they offer a visualisation platform, Waggle, for players to access their performance data. Some of the statistics available in Waggle include strokes gained against the field, top three and bottom three strokes as well as other traditional stats.

New science-based and statistical data-driven golf training centres, such as Every Ball Counts, have been recently established to help elite pros and serious amateur golfers through demanding physical and mental training sessions. Aside from leveraging various of the technologies previously mentioned, Every Ball Counts also developed an algorithm with Harvard University that takes a player’s ShotLink data and looks at 900 data points calculates 19 different metrics to formulate a game plan on how to improve a golfer’s game.

New Metrics Are Leaving Traditional Statistics Behind

One of the most popularised metrics that has appeared from the analytics revolution in golf in recent years is strokes gained. The strokes gained metric was developed in 2011 by Mark Broadie, writer of the 2014 best seller Every Shot Counts, as an attempt to modernise more traditional golfing stats previously employed, such as driving distance or putts per round. One of the issues with traditional statistics that Broadie discovered was relating to the counting of the number of putts per round. This conventional metric did not take into account the distance of each putt. In other words, players who hit their approach shots closer to the hole may have fewer putts per green in regulation than a player who is a superior putter but doesn’t hit his approach shots as close. Instead, strokes gained adjusts for the initial distance of the putt and other relevant factors to illustrate a more accurate representation of the golfer’s skill level.

To calculate strokes gained, an analysis was performed on ShotLink’s database composed of 15 million shots from players across every PGA tournament to determine the value of each shot by benchmarking it to the average of historical shots with those similar characteristics. It is a model that predicts the probability of a golfer’s score for each hole on a shot-by-shot basis. Mark Broadie applied mathematical techniques of simulation to analyse different strategies using different clubs and targets off the tee. He simulated thousands of shots and played the hole thousands of times using different strategies to identify the most effective one. He also applied dynamic programming by optimising the sequence of play in a hole and coming up with the best strategy on the tee by working backwards off the green to determine what should be the target on the first shot.

Since its development, strokes gained has allowed golfers to better understand where they gain or lose ground. Mark Broadie started discovering aspects of the game that contradicted common beliefs. For example, he found that putting is only 15 percent of the shots difference between better players and average players, with the biggest difference actually being in ball striking, especially the number of penalised shots that those with high handicaps hit. In essence, long game is the separator between the best pros and average pros, since it explains about two-thirds of the scoring differences. Putting at 27 feet or 30 feet distance on the green does not matter as much as a shot in the bunker or the shot that lands on the green instead of the rough. The distribution of the importance of each type of shot that Broadie found suggested that approach shots accounted for 40% of the players’ scoring advantage, while driving was responsible for 28%, short game for 17% and putting covered the remaining 15%.

Data Analytics In Course Management

Aside from the direct benefits to a golfer’s play, courses all around the world have also made use of technology to improve their grounds. Data systems are allowing golf clubs to track every single shot played on their course in relation to handicap, age, gender, weather conditions, pace of play, tee usage and pin locations and provide them a detailed understanding of the interaction between players and the various features of their golf course. The aim is to efficiently improve golfer experience by increasing playability, course strategy or difficulty, environmental impact or pace of play, while reducing maintenance costs through reductions in redundant water, chemical and fertiliser usage, green, fairway, tee sizes and bunker volume and size in areas of little to no play. Companies like Golf Course Architecture are also providing golf-course operators with smartwatches that are worn by members to track every shot hit and its location, while golfers get all their statistics in real-time as they play.

How Are Pro Tour Golfers Applying Data To Their Play?

In recent years, a new generation of professional players have employed statisticians and data analysts to analyse the vast amounts data available and identify their strengths and weaknesses against those of their opponents in order to improve their performance and define winning strategies. One of these golfers is Rory McIlroy, who has made heavy use of the 32,000 data points per event that ShotLink System captures to benchmark himself against everyone else, particularly using statistics such as strokes gained.

In 2012, Dustin Johnson found immediate results when discovering through data analysis that he ranked 166 in wedge game. After identifying his specific area of weakness and fine-tuning his wedges using a high-tech Trackman device to monitor and improve the accuracy of his short game, he managed to improve his approach shots from 50-to-125 yards. By 2016, he had become fourth in the ranking.

Other golfers like Brandt Snedeker also embraced technology as early as 2011, when he became the first tour player to hire a full-time analyst. By 2015, using radar technology to track swing, he determined that his best swing launched the ball at 12 degrees with a spin rate of 2,400 revolutions per minute. He then used this information as a baseline when testing and acquiring new equipment that incorporated the latest advances in design and verify whether it improved his performance.

Other examples include Danny Willett, when in 2016 he made use of 15th Club to gain access to a team of golf professionals, data experts and software engineers who analysed ball locations at Augusta National and helped him plot his winning strategy during the 2016 Masters Tournament. The strategy consisted on taking advantage of Willet’s great wedge game between 75 to 100 yards on par 5s when his tee shots went wrong. He went on to win the tournament by making 11% of shots above par compared to the 26% field average.

Luke Donald, through his golf coach Pat Goss and the help of Mark Broadie, also rose through the ranks by taking advantage of analytics and the strokes gained formula to understand where to improve and inform the design of practices to improve specific statistics. These statistics showed Goss that even though Donald did not drive the ball far, he was very good at short game and putting. It allowed him to define a winning strategy where Luke Donald had to get almost a full shot in putting and the rest from the short game inside 100 yards and from iron play, and just break even with driving.

Today, data analysts in golf are becoming as important to tour pros as swing instructors and fitness trainers. They parse statistics to create better training plans and arm the golfers with game plans for each week. As data gets more complex and margins tighter, data analytics and the integration of technology in the sport will continue to rise and gain in importance. Golfers seem to have understood and accepted that and appear to be embracing the ever-growing technological revolution in sport.

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Impact of Data Analysis And Technology in Rugby Union

In August 1995, the International Rugby Board declared Rugby Union a professional sport. As we approach the 25th anniversary of the professionalisation of Rugby Union, it is worth reflecting back on the evolution of the sports during the last two and a half decades. The sport has experienced incredible change, with multi-billion worldwide audiences, broadcasting agreements and lucrative contracts for players, coaches and clubs. This rise in popularity led to the rise of the standards to performance demanded at an elite level. Competitive margins became tighter as athlete development, the coaching processes and overall club management became more complex. Incentives of winning to attract sponsors and broadcasters became a major focus and so did the efforts of clubs to acquire an extra competitive edge over their opponents. This added complexity triggered the emergence of new backroom functions that dealt with areas from physiological, psychological or biomechanical aspects affecting players (i.e. Strength & Conditioning coaches or Team Psychologists) to those providing an objective evaluation of performance and addressing the need of a better understanding of the determinants of success in the game (i.e. Performance Analysts).

Emergence of the Use of Technology and Data

Over the years, advancements in technology and data management processes in all top sports have led the way in better defining individual and team performances, and Rugby Union is no exception. Coaches and other backroom staff can now be seen in the stands with a wide variety of computers and technology monitoring all aspects of the match in great detail. Different camera angles, data and analysis are now available to them right there and then to make instant decisions, as well as post-match reviews.

VIDEO ANALYSIS TECHNOLOGY

Amongst the many new practices emerging from the use of technology, the introduction of video analysis in the coaching process has enabled for dynamic and complex situations in sports to be quantified in an objective, reliable and valid manner. Time-lapsed software packages like SportsCode have enabled Performance Analysts to analyse match or training footage by manually tracking event frequencies and creating datasets for later analysis. Thanks to SportsCode and other videoanalysis software, these datasets are also linked to video footage for better contextualisation during review.

Source: Gloucester Rugby

RESHAPING BACKROOM STAFF PROFILES

The ways in which the collected data is used is also evolving from basic visualisations, historicals and dashboards to more complex prescriptive approaches that provide more informed recommendations and can predict possible outcomes. This change is being driven by a new generation of Sport Scientists and Performance Analysts who have come into rugby with an increasingly stronger background in data and analytics. With the hand of coaches willing to listen to data, they are changing the culture within clubs into a more evidence-based approach to performance. These analysts not only analyse all aspects of their team’s performance but also aim to detect the strengths and weaknesses of their next opposition for coaches to use in their game plan. Thanks to the latest technologies and availability of data through third party providers like Opta, they can now perform incredibly detailed analysis, such as an opponent’s key player’s kicking game (i.e. the types of kicks, when he made them, from which part of the field and the distance he tended to get) or even identifying who are the key players in an opposition’s running game.

IMPROVED TRACKING EQUIPMENT AND DEVICES

In today’s modern rugby, all leading rugby union clubs use data to monitor fitness, prevent injuries and track player’s positions through devices such as wearable GPS trackers. The data captured from these technologies have played a key role in preventing player injuries. GPS technology company Catapult - which develops wearable devices sewn into the back of players’ shirts - recently aimed to deepen the use of data in rugby by launching a unique set of algorithms engineered to quantify key technical and physical demands in the sport. They achieve this by automatically detecting scrums, kicks and contact involvements in Rugby Union players. This data providing insights on the physical demands imposed to players gives coaches crucial information to manage the load given to players during training and matches to better maintain adequate levels of fitness while preventing injuries from physical overexertion. Coaching staff can now see the levels of effort put in during training sessions and, by monitoring the players’ thresholds, they can better design training sessions to keep the players fresh for the games. One of the benefits from Catapult’s Rugby Suite is the measurement of contact involvement duration (i.e. the time a player takes to get back to feet, also known as Back In Game Time). This allows strength & conditioning coaches to identify player fatigue levels and their intent when returning to the defensive or offensive line.

Source: Business Insider - Credit: Harlequins/Catapult

INNOVATIVE TECHNOLOGY TO ENSURE PLAYER WELLBEING

Another key area strongly impacted by technology is concussions. Concussions are a growing issue in the sport, leading to players eventually suffering from chronic traumatic encephalopathy, a degenerative brain condition with symptoms similar to Alzheimer’s. This has been a focus of technological developments aiming to better prevent and monitor them across various contact sports. Historically, pitch-side doctors rely on player honesty for their risk assessment when deciding whether the player should return to play. However, companies like OPRO+ are now building impact sensors into the personalised gumshields frequently worn by players to protect their teeth. By having impact detection technology closer to the centre of the skull doctors can paint a more accurate picture of the forces involved in each impact. OPRO+ can transmit impact data to a laptop in real-time so that pitch-side doctors can assess whether a player requires further assessment. This has proven particularly important in training sessions, where 20% of head injuries take place, although most of them go unseen. Thanks to this technology, coaches are now able to assess the forces exerted by players during drills and adjust the practice accordingly to avoid undetected head injuries. This type of tracking technology could eventually help develop a digital passport of historical head impact data for individual players, which can help them lengthen their careers by preventing early retirement due to poorly treated head injuries.

Source: Opro Mouthguards

Further advancements in the use of technology to prevent concussions were introduced as recent as five years ago across the world of rugby. In 2015, World Rugby also introduced a cloud-based technology developed by CSx into the Head Injury Assessment (HIA) process. This system collected neurocognitive information that medical staff can review to determine if a player suffered a concussion. They transferred the data on the players involved, incidents and medical assessments to the data analytics platform Domo via an API, where the various datasets would be joined up in one single consolidated platform for further analysis. This new technical process introduced by World Rugby brought the estimated number of players allowed to continue to play after being concussed down from 56% to just 7%, while the chances of being removed of the game without being concussed only increased from 3% to 5%.

Source: The Times

How Are Unions And Clubs Managing The Relationship With Data And Technology In The Sport?

Successful rugby unions like New Zealand Rugby have started considering the balance between data and intuition. Their performance analysis department now operates in a highly dynamic technological environment where it provides its teams the ability to quickly analyse data for performance insights. The All Blacks turned to SAS in 2013, when they adopted SAS Visual Analytics as their main reporting tool. It enabled them to obtain a formal data management process that consolidated all real-time match data, post-match data and data retrieved through third party data providers in one unified and centralised platform.

New Zealand Rugby manages the relationship between players and technology by adopting the philosophy that when it comes to match play players are considered the ones in control of the game, as they are the ones that see, hear or feel what’s happening on the field. Technology is considered a supportive tool in the background to help inform decisions by bringing context and evidence to conversations, but not take over them.

As per England Rugby Union, head coach Eddie Jones addressed the significance of data prior to travelling to the 2019 World Cup in Japan. He suggested that data has had a key role for him in seeing what is important and deciding where to invest in to build the strength of your squad. England Rugby benefits from an extensive analytics team that provides post-match analysis but also real-time tactical suggestions to coaches during matches. The department implemented a philosophy of always looking for the winning edge. For instance, they aim to discover winning trends such as the now well-established theory that the use an effective kicking game tends to lead to more successful match outcomes, a theory now considered a basic principles in the sport.

Moreover, Rugby Australia also entered the world of data analytics by partnering with Accenture to develop a bespoke high-performance unit (HPU) analytics platform using Accenture's Insights Platform (AIP) that consolidated all their data activities. The system placed sports data at the core of all team’s management processes. As data ecosystems have become more complex with numerous sources and purposes for different datasets, Rugby Australia was able to integrate data, deliver insights and enable users in a single platform that provides a smarter and more automated approach that has led to a more effective way to manage their data assets. Insights are now available to players and staff via a mobile app that provides clear visibility of a particular player’s performance and health as well as allowing deep-dive exploration into highly detailed statistics about daily performance.

The growth of data management systems and processes has also extended beyond unions. Overtime, media, consultancies, tech companies and clubs themselves are beginning to gather larger amounts of data of the game in an attempt to develop big data capabilities. For instance, Accenture and RBS developed an analytical package for the 2017 Six Nations tournament that contained six million data points per match. IBM and the RFU also performed a similar exercise by developing a predictive analytics software, TryTracker, to forecast the outcome of a game by mining data from historical rugby matches obtained from Opta.

However, when it comes to professional clubs, data is increasingly more custom-made by the clubs themselves to tailor for particular coaching philosophies and needs, as well as team-specific insights. Most clubs will receive data from third-party providers like Opta at a certain level of granularity, but will then gather their own internal data often at a much deeper level. They create their own datasets where they might even analyse the technique of every single player in the team individually. For example, teams may track a more detailed view of their defense, detailing the dominance of each tackle. Coaches can also have an input in data captured by providing their expert insights as additional data points. Analysts will incorporate the couches’ perceived effectiveness or quality of a given action by a player as categorical data variable to the dataset (i.e. positive or negative movements according to effectiveness in performing a set of moves).

Have Data and Technology Been Fully Accepted In The Sport?

In December 2019, a study by Andrew Manley and Shaun Williams from the University of Bath triggered a new debate of whether the essence of the sport (i.e. enjoyment of the players) seen during the amateur era and the early professional years has been lost. Players are, allegedly, increasingly concerned about the use of modern technology to provide clubs with greater surveillance and pressure to perform over them.

OVER-EXPOSURE THROUGH TECHNOLOGY

The qualitative study by Manley and Williams interviewed 10 professional rugby players and asks them about their experience with data and technology at their club. Like many others at an elite level, their club used a series of devices such as laptops, camcorders, GPS devices, heart rate monitors, body fat recordings, mood score sheets, iPhones/iPads and mobile apps to map, track and monitor individual performances and player wellbeing. Data from these devices was collected by analysts and matched against the team’s key performance indicators. Analysts and coaches would then assess each player’s performance and set appropriate improvement plans. Once collected and validated, the data was published in the club’s mobile app for players to access it. According to the players interviewed, the open exposure of individual statistics created a climate of fear of public embarrassment when failing to meet personal performance indicators.

A CHANGING CULTURE

The club had also developed a global Work Efficiency Index for each player that was derived from 70 different variables describing a players positive and negative actions and physical condition. The use of this new metric by the club extended all the way to contract negotiations. This raised serious concerns from players, who often failed to understand how to improve their Work Efficiency Index, thus became suspicious that the results were being manipulated to suit the management’s rhetoric at any given time. Players started to obsess over this metric, prioritising it above their individual impact to the overall team performance. On the field, they also became risk adverse to avoid negatively impacting their specific stats defined by the clubs. They feared being called-out by coaches and judged by teammates during post-match reviews. Even then, performing well in individual stats had other negative effects on team dynamics. Players with positive individual stats had incentives to take it easy and ignore the additional contribution they could bring to the team after they ticked all the boxes.

INVASIVENESS OF CONSTANT MONITORING

Players also found the introduction of technology to be invasive in nature. The mobile app used by their club involved continuous monitoring of their activities and sent frequent notifications and reminders to players’ phones. Some of the features of this app included the monitoring of weight management. The club had even introduced fines if players failed to meet their body weight targets set in the system. Additionally, the new machine mentality at the club had coaches increasingly turning to technology to zoom in on the deficiencies affecting individual and team performance as a response to the pressures of a growing fan-base and increasing commercial interests of owners and sponsors that demand an acceleration to title success. Players felt that the excessive use of technology had introduced a Big Brother surveillance on players and was used as a coercive method of ensuring that players meet institutional objectives. Data and technology had simply become standard practice in elite coaching of modern rugby. However, players felt that these unrelenting practices of constantly monitoring had harmful consequences to their playing and private lives, as well as relationship with coaches, which had not yet been addressed. In their interviews, they argued that technology has enable coaches to formalize a regime of power, with the risks of turning the humanistic approach of coaching into pure data engineering.

PURISTS VERSUS OBJECTIVISTS

Other critics of the use of technology argue that Rugby Union is losing its way due to data. According to them, individual wizardry and innate empathy in the sport created from the unpredictability in the game is suppressed by those digital data profiles created by analysts and coaches that players are constantly trying to meet. The researchers in the study argued that data is taking away intelligence, creativity and human connection from the sport through mechanistic and restrictive routines imposed to players. As players become more risk adverse, predictable and formulaic, a culture without instinct, emotion and unpredictability is introduced in the sport, inevitably becoming less attractive to fans. This culture, according to researchers, encourages individualism over team dynamics and incites anxiety amongst players by throwing large amounts of data at them to pressure them to perform to the stats. This has become detrimental to their enjoyment and performance in the game.

While having recently praised the significance of data in achieving success, England coach Eddie Jones also expressed his concerns regarding the production of player at grassroots levels that lacked dimension. He stated that academy players are now coached to regimentally follow a game plan rather than react to dynamic and unpredicted events in a game. They are decision followers rather than decision makers. The study claimed that the surge in technological practices, to the detriment of players and the game, has also been accelerated by the new generation of head coaches entering top division clubs. These group of coaches are former players who have only known Rugby Union as a professional sport and who feel the need to keep up with technology not to fall behind. They prioritise control over players through procedural management at the expense of educational aspects of the job.

DATA OWNERSHIP AND SECURITY

Data ownership has also become a key concern to players. Even prior to the launch of GDPR regulations, legal proceedings had been discussed between players and their respective clubs on this matter. Their main concern was relating data accrued by clubs and unions through GPS units, and other performance measurement devices, relating to a player’s medical history, such as injuries. They wanted to prevent clubs from using their data without their consent, or even selling it to third parties, which could have detrimental effects to their careers and future earnings. The International Rugby Player Association addressed this issue by pressing on the efforts to make personal statistical data relating to the player to be owned by the player themselves, who should also receive any benefits that may arise from the commercialisation of such data.

Player statistics may not only be used in contract negotiations by the player’s current club but also by clubs interested in incorporating them in the near future. For instance, if a player’s performance in training has statistically declined (i.e. speed tests, work-rate or lifting in the gym) that information could be valuable to a club interested in signing said player. However, the information at the club’s disposal may lack completeness and paint an imprecise picture of the player’s true value. For example, there is a lack of measurement of soft skills a player can bring to a team, such as leadership and motivational impact on the rest of his teammates. Additionally, the security of their private and confidential data stored at the club is also an area of concern. As larger amounts of complex player data is gathered and stored in the club’s systems, the risk to data breach is also increased, particularly those of phishing or hacking attacks. This means that clubs and backroom departments have to now face structural and procedural challenges relating the way they manage and secure their vast amounts of data collected and have sufficient know-how to identify and prevent any serious security gaps.

Teething Problems Of A Rapidly Growing Field

The experiences described by the players interviewed in the study reflect the eagerness in today’s big data society to make use of the ever-evolving technological advancements. Everything is turned into data in order to be objectively understood. However, one of the most important conclusions in the study is that a lot of the data used in professional Rugby Union lacked relevance. Instead of aiming to capture as many variables as technology allows, a fewer amount of data should be made available to players that is substantially more meaningful to them. That is not to say that conclusions should be drawn from insufficient data samples. Another important issue in the application of analytics in Rugby Union, particularly at an international level where fewer matches are played, has been the generation of insights from too sparse and small sample sizes that are insufficient to make predictions. Focus should be placed in collecting and analysing large enough sample of the data identified as being truly meaningful for player and team development towards achieving excellence in the game.

Practical applications should be place at the core of any consideration for using data and technology. There have been numerous studies made on different aspects of the game, but more often than not these have dubious practical applications or mere usefulness in coaching practices. For instance, a study concluding that shared experience by players within the same team is correlated to better outcomes may have minor practical applications to coaches, as it is rare or difficult to buy shared experience and there is little a coach can do in that regard. Instead, analysts should look at performance patterns and trends rather than one-dimensional statistics, such as ratios or frequency counts. For example, analytical studies should aim to identify trends that develop before tackles are missed so we can help coaches and players identify the root flaws within a team’s defensive pattern.

The use of data in the sport should advance into true rugby analytics and deep intelligence by effectively and meaningfully using the data available in the sport. Analysts should aim to fully understanding what the team is trying to achieve and then go on to identify the metrics that influence those goals. This will allow them to inform decisions that impact performance and change behaviours. Since context is key it should become the central piece of most analytical work, as without it data insights presented to coaches lack value and practicality.

The role of analytics and technology is only going to grow even further. There is increasingly new technology coming into Rugby Union. This places increasing demand on people who can process vast amounts of data and come up with relevant analysis, while at the same time not losing touch with the nature of coaching practices in the Rugby Union. While some questions can be raised about today’s appropriate use of data analysis in defining and optimizing team performance, it is without a doubt that technology has open the doors to a wide range of developments that have evolved the jobs of coaches and players. While the study by Manley and Williams exposes some concerns of how data is being applied at a club level, it is also true that player wellbeing (i.e. concussion prevention) has seen a substantial improvement with the aid of technological advancements. The idea of data analysis is not to replace all other aspects of the coaching practice but to combines the coaches’ experience and intuitions with video and data analysis to help inform decisions on training priorities, on team selection, on tactics, and longer term on player recruitment and player retention issues. There is an important place for technology and data in the sport, but like everything, a healthy balance needs to be established where data and intuition strongly complement each other.

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• Rees, P. (2020). Is rugby union losing its way by becoming a numbers game? The Guardian Online. Sports: Rugby Union. Link to article.

• Rees, P. (2020.) Body fat recordings and mood scores: has technology gone too far in rugby?. The Guardian Online. Sports: Rugby Union. Link to article.

• Streeter, J. (2019). Catapult elevates use of data with all-new Rugby Suite. Insider Sport Online. Link to article.

• Watt, D. (2019). Five things that business leaders can learn from England Rugby. Director Online. Link to article.

Nacsport: The Most Accessible Video Analysis Software

Founded in 2008 in The Canary Islands, Spain, Nacsport is another important player in the development of videoanalysis software for performance analysis across emerging regions. Similar to SportsCode or Dartfish, Nacsports allows analysts to tag any action and build a deep understanding of what’s happening for later review. The tool works similarly to its competitor, where analysts decide which events need to be analyzed in any specific game or training situation. These event can be specific actions, players, pitch areas, or any other points of interest. Buttons are created for each event, where the analyst clicks the corresponding buttons for each of event as they occur. Each click generates a tag marking the time when they happened. When the match analyses end, Nacsport software displays all the tracked events grouped into category rows and/or chronologically on a timeline.

Nacsport enables you to analyse from up to two video feeds and a total of 5 videos at once in their most basic version of their software. It also includes unique features, such as the ability to add a video overlay on top of another one for comparison purposes, or even tagging events in real-time without the need of video footage using Nacsport Remote or Tag&View apps on a mobile phone or tablet. Nacsport allows you to create your own fully customised templates to track the actions and data you want to explore, review the key moments with a timeline and interactive data tools. You can then share these high-quality insights as presentations with notes and KlipDraw drawing tools to enhance your feedback delivery to athletes and coaches to make better decisions in the future. These clip editing and presentation features provide Analysts with the ability to incorporate text notes, ratings, image overlays, logos and even drawings to analyse actions.

In 2019 alone, Nacsport managed to sell over 4,000 licenses as the company’s impressive growth continued, particularly amongst grassroots clubs, schools, colleges and universities. The company has now an established presence in 60 countries and more than 35 different sports. During the same year, they also managed to launch over 100 new features as the company continuous to enhance their product offerings in line with the developments of new technologies and capabilities in the industry. Amongst some of these features, Nacsports video analysis software is now compatible with other video analysis tools, such as SportsCode, Dartfish, InStat, Wyscout, Synergy or STATS, in an attempt by the company to facilitate a simpler, more efficient way for clubs to manager their data in a consolidated manner and encourage a smoother transition to their tool.

Five Products At Comparatively Affordable Costs

Nacsport offers five very competent products with incredible depth for coding, annotating, highlighting, reporting, analysing and broadcasting relevant sports moments. However, their main competitive advantage over other major players in the market is their affordability. Video analysis software packages often come at exorbitant prices that have kept them out of reach for numerous elite and amateur sports teams. Nacsport has disrupted this market by producing very affordable comparative video analysis product. They want to comply with the requirements of all coaches and sports staff - no matter their level, budget, or sport - with a suite of products that is scalable depending on their evolving needs. Analysts, clubs and coaches that want to start incorporating performance analysis into their workflows can obtain a Nacsport software license from as little as £130 GBP (150 EUR) per year, all they way up to their advanced version priced at £1,025 GBP. Unlike many of its competitors, Nacsports also offers a lifetime fee of 1,700 EUR so that you do not have to worry about ongoing subscription payments overtime.

Entry Level: Nacsport Basic And Basic+

Nacsport entry-level version of their software already provides sufficient functionality for basic event analysis of sporting events and data gathering. It enables analysts to track up to 50 code buttons in Basic Plus (25 in Basic), real-time event tracking and a complete timeline with slow motion, text additions and creation of highlights movies.

These substantially affordable versions are perfect for newcomers to video analysis who can quickly pick up the software and benefit from the key features and feedback resources. It is worth mentioning that the Basic+ version enables analysts to mirror high-end processes and interact with other software and services like Opta, Wyscout, Gamebreaker and SportsCode due to the ability to import/export XML files.

Some of the key functionality for these versions include:

• Unlimited templates with up to 50 code buttons

• Rating events during coding (i.e. rate a shot from 1-5 as you track it)

• Group buttons by categories

• Tag&Go for off-footage tracking (i.e. track events in your iPad and import them to your Nacsports software later on)

• Button formatting for a more intuitive coding exercise (i.e. different shapes)

• Exclusive links between buttons (turn one button off when another one is active)

• Set actions within buttons (i.e. display the current score based on event counts of goals)

• Text notes on timeline events

• Frame by frame, fast forwards (x6) and slow motion playback modes

• MP4 video capture, compatible with USB Digitizer, AverMedia and Black Magic H264

• Export videos from timeline

• Compare up to 8 events simultaneously

• Draw and insert images on footage (integration with KlipDraw functionality)

• Data matrix showing event counts and export functionality in XLS

• Export XLS and PDF reports

• Create interactive presentations with notes and actions (i.e. display certain group of events based on buttons clicked

• Create highlights clips with transitions, slow motion, text and logos in 1080p Full HD

• One dashboard with unlimited charts and labels that can display results in absolute data and percentages, with the option of real time display of stats

Professional Level: Scout+, Pro+ and Elite

These versions of Nacsport offer a huge amount of functionality. Scout+ alone already offers those key features necessary to perform the majority of processes seen in most professional setups. These advanced versions allow you to create an unlimited amount of buttons within your personalised template, open 5 separate databases of different games within the timeline, review a matrix with data from multiple databases and create independent presentation windows so you can gather clips from different games. The most advanced versions - Pro+ and Elite - include high-end live processes, such as the ability to review actions whilst you are capturing them as well as wireless connectivity amongst devices on your same network.

Some noteworthy features in the advanced packages include:

• Unlimited buttons and templates.

• Inactive buttons (for headers or code window design)

• An extra layer of buttons to describe certain actions (similar to SportsCode’s code vs label buttons)

• Two-angle display (four-angle in the Pro+ and Elite versions) in highlights video as well as additional video creation functionality, such as external audio file upload.

• Unlimited dashboards.

• Intergrations with other providers, such as Opta or SportsCode.

These higher-end versions of the software also offer additional advanced and exclusive features such as:

• Panel Flows allowing you to navigate between coding windows (i.e. templates) by clicking specific buttons

• Heatmaps for a more visually attractive display of event frequencies within areas of the pitch

• Players Connections allowing you to specify the active players on the game and analyse performance by group of players

• Up to 4 Analysts can simultaneously track the same match onto one consolidated report

• Category Frequency Chart provides visuals on the fluctuations of specified events over the course of the footage

• Data Patterns provides a click and drag interface to create visual that expose patterns in the data tracked

Live Tagging Using Nacsport Tag&View

Tag&View is an iPad and iPhone app which allows Analysts to track events without the need of a computer. The process consists on first importing or creating personalised templates within the app during the match or training session to later link up the data gathered with a video using the Nacsport software. The tracking process is similar to that of when using laptop, with the difference that no video footage is required to track events.

Amongst many features of Nacports Tag&View, it is worth mentioning the ability to create two different types of buttons: Categories (track events) or Descriptors (add extra information to the events). These buttons are fully customisable, not only in their appearance but also their preset length when using PRE and POST times or MANUAL MODE. Buttons can also be linked to one another using Activation and Deactivation links to specify the relationship between each event (i.e. exclude possession of the home team when away team possession button is active).

Adoption Of Nacsport Amongst Elite Clubs

Nacsport software is used by some of the world’s leading teams, including Liverpool FC, Atletico de Madrid, Arsenal, the Spanish National Basketball Association, England Rugby League and Scottish Rugby Union. In 2016, two analysts at Atletico de Madrid decided to start using Nacsport on a personal level, powering their respective academy teams towards their season’s success and impressing their managers. This triggered the creation of the Department of Analysis across all academy teams at the club, focusing on Nacsports software as their core video analysis software. Since then, Analysts at Atletico have often claimed that Nacsport software has been critical to the club’s successful integration of performance analysis as a team function thanks to its easy and intuitive interface that allows anyone to become quickly proficient with it, which has empowered its adoption across the club by not only Analysts themselves but also players and coaches.

Similarly, other major clubs like Gloucester Rugby are now fully transitioning from other video analysis software to Nacsports. Gloucester Rugby currently use Nacsport as their way of editing down large video files into important events during matches or training sessions for players to easily review. Additionally, former Valencia boss Marcelino Garcia Toral is also a longstanding Nacsport user and has recently emphasized on the importance of the insights gathered from the video analysis and how it has been critical in helping him manage his squad’s performance. Other clubs like Sevilla FC have Nacsport video analysis software integrated within the entire sporting structure of the club and their analytical workflows in First and Academy teams, while others like Coventry Rugby have also extended their use of Nacsport products to Nacsport Coach Stations or Nacsport Viewer to allow coaches to review and provide live feedback to players during games and training.

It will be exciting to continue to see the growth of Nacsport within the industry and how they will maintain their attractive affordability while continuously improve their product offerings at the same speed as their competitors.

Compatibility of Nacsport:

Finally, it is worth noting that currently, Nacsports is only able to run from a Windows PC (7 or older). In order for it to be used on a Mac, Nacsports recommend using an emulation software, Parallels or BootCamp. Additionally, the complete software can be tested for free on a 30-day free trial and downloaded straight from their website.

SportsCode Scripting Guide

Coding windows and statistical windows in SportsCode can be substantially enhanced using scripting, from automating certain event tracking to displaying real-time statistics to creating movie highlights of players or types of plays. However, unlike most analytical software packages, SportsCode uses their own coding syntax, creating the need to learn and understand their software-specific way of writing any command. Some of these are similar to functions in Excel or Numbers, although their slight alterations make it crucial to ensure they are correctly written in order to work. This guide walks through some of the key commands that can make your coding experience in SportsCode a lot smoother.

Where To Use Scripting?

Scripting in SportsCode is done through either a Code window or a Statistical window. You can use an existing code window that you already have or create a new on in File then New then Code window.

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SportsCode Code Window

The actual script is written inside a code button. You can either add a new code button or use an existing one. If what you want to do is show an output in that button, then you need to open the “Inspector” popup window for your button, select the “Appearance” tab and tick the “Show output” option. This will set the code button as a button with the function of displaying information.  To start writing a script go to the “Script Editor” tab within the Inspector window.

While not necessary, SportsCode recommends adding an “Execute” button in your Code window’s tool bar by right-clicking on the toolbar and selecting “Customize Toolbar…”. This “Execute” button is used to run the code after it is written, although the Play button of the Code window will have the same effect of running the code.

Commands To Select Elements & Events

Prior to starting writing commands, one of the first things to understand from scripting in SportsCode is how to select different elements from your code window and timeline to be used in your scripts. For example, if you want to display the number of shots by the home team together with the number of shots by the away team, you will want to translate that task into the appropriate script syntax that represents the calculation of “shots home team” + “shots away team” from your timeline. To do so, you will need to have the script select the appropriate elements from SportsCode that contain the shot numbers for either team, so that you can then add them together.

Below is a list of how to select an element from your code window or timeline to be used in your script commands.

Selecting Elements From Code Window

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SportsCode Script Example:
Display the number of shots for the player appearing in the Button “PlayerName” by retrieving the button name with the name of the player and counting the events in the timeline with that name.

$

Creates a new variable to be used at a later stage in the script.

• Examples:

  • $HomeGoals = count instances where row = “Home Goals”

  • $AwayGoals = count instances where row = “Away Goals”

  • $TotalGoals = $HomeGoals + $AwayGoals

---

$BUTTON_ID

Returns the button ID of the code button where the script is written.

Similar Command:
$THIS_BUTTON (returns the button name of the code button where the script is written)

• Examples:

  • Show “This Code Button’s ID is “ + $BUTTON_ID

---

BUTTON NAME button_id

Retrieve a code button name using the button id of the button name you want to obtain.

• Examples:

  • BUTTON NAME “Shots On Target”

  • BUTTON NAME “Home Goals”

  • BUTTON NAME “Away Goals”

---

CODE button_name

Selects the output from another button using the button’s name as reference (i.e. if button with the name Goals has a script that outputs 5, it will grab that number 5)

• Examples:

  • CODE "Goals"

  • CODE “Total Shots”

  • CODE “Home Possession"

---

CODE ID

Selects the ID of a different button using that button’s name.

• Examples:

  • CODE ID "Goals FC Barcelona"
    (if the button name “Goals FC Barcelona” has an ID called “Home Goals” it will return “Home Goals”)

  • CODE ID “Messi”
    (if the button with the name “Messi” has an ID called “Player” it will return the text “Player”)

---

BUTTON button_name STATE

Specifies whether a button in the code window is activated or not.

• Examples:

  • SHOW BUTTON "FC Barcelona Possession" STATE
    (displays whether the possession button for Barcelona is activated or not)

---

Selecting Events From Timeline

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SportsCode Script Example:
Display the total shots that took place in a match for both teams by first, counting the number of shot events in the timeline for each team inside a new variable and then adding the two team variables together by creating a third variable (i.e. $TotalShots) with the total.

ROW

Selects specific row within the timeline (i.e. Home Team Shots).

Similar Command:
ROW_NAME(#) (Specifies the name of the row from the value you select, i.e. 1 = first row, 2 = second row)

• Examples:

  • ROW = “Shots On Target”

  • ROW > 1

  • ROW < 10

  • ROW_NAME (1)
    (selects the name of the first row in the timeline)

---

INSTANCES

Selects specific events tracked within one or multiple rows in the timelines.

Similar Commands:
INSTANCES2 (only instances between red markers in timeline)
INSTANCE[X] (only the ‘x’th instance specified inside the brackets).

• Examples:

  • INSTANCES WHERE ROW = “Shots On Target”

  • INSTANCES2 WHERE ROW = “Home Goals”

  • INSTANCES[2] WHERE ROW = “Away Goals”
    (selects the second goal by the Away team)

---

LABEL label_name

Select events with a specific a label in the timeline.

Similar Commands:
LABELS (returns all labels)
LABEL IN (specific instances)
LABELS IN (all labels in specified instances)
”GROUP”.”LABEL” (selects events matching the group and label specified)
”GROUP”:”LABEL” (selects event occurrences matching the group and label specified)

• Examples:

  • SHOW COUNT LABEL “On Target”

  • SHOW COUNT “On Target”

  • SHOW COUNT “On Target” WHERE ROW = “Home Team Shots” 

  • SHOW COUNT “ShotType”.“On Target” WHERE ROW = “Home Team Shots” 

---

FROM

If you are using multiple timelines, FROM allows you to specify which timeline to select events from.

• Examples:

  • Show count instances from “Barcelona v Real Madrid” where row= “Barcelona Goals”
    (displays a total number of goals scored by Barcelona in the match against Real Madrid)

  • Show count “Messi” from “Barcelona v Real Madrid”, “Barcelona v Atletico” where row= “Barcelona Goals”
    (displays the total number of goals scored by Messi in the Barcelona matches against Real Madrid and Atletico.

---

GROUP

Selects all events with a specific group of labels.

• Example:

  • Show count instances where group = "Shot"
    (displays the number of instances with labels in the group shot, i.e. with labels such as “On Target”, “Missed” or “Scored”)

---

LIMIT

Selects a specific instance or group of instances based on their position in the timeline.

• Example:

  • Instances limit 2 where row= “Goals”
    (selects first 2 goal events from the timeline)

  • Instances limit 4,2 where row= “Fouls”
    (skips the first 4 fouls and selects the next 2)

  • Instances limit 4,-1 where row= “Free Kick”
    (skips the first 4 free kicks and selects all the remaining ones)

  • Instances limit -3,-2 where row= “Saves”
    (selects the second and third last saves)

---

OVERLAP

Selects events in the timeline that occur at the same time.

Similar Commands:
OVERLAP_LENGTH (total length of time in second that two events or labels occur at the same time)
UNIQUE (opposite command to OVERLAP, returning events that occur at completely different times to other events)

• Examples:

  • $MessiAssists = OVERLAP ( "Messi", "Assist" )
    (display the events with the label Messi and label Assist taking place at the same time)

  • Show start OVERLAP ("Scored" where row="Shot" ,"Messi" where row="Assist")
    (display start time of event when both the shot got scored and Messi assisted overlap)

  • $NotMessi = UNIQUE (“Shot”, “Messi”)
    (display the number of shots where Messi was not involved)

---

START

Select the earliest start time of the labels or instances in the timeline in seconds.

Similar Commands:
START TIME (select events with a specific start time)
END (select the latest end time of the labels or instances in the timeline in seconds)
END TIME (select events with a specific end time)

• Examples:

  • START "Goal"
    (display start time of first goal)

  • START "Messi" and "First Half" where row="Goal"
    (display the start time of Messi’s first goal in the first half)

  • Count instances where start time < 60
    (display the number of instances that occurred in the first 59 seconds)

  • END "Ball In Play"
    (display the latest time that the ball was in play)

---

RANGE

Select events based on when they occur in the timeline.

Similar Command:
HH:MM:SS (select events based on when they occur using hours:minutes:second format)

• Example:

  • Count instances where range > 60
    (display the number of events that happen after 60 seconds of play)

  • Count instances where range >= 60
    (display the number of events that happen from 60 seconds of play onwards)

  • Count instances where range != 60
    (display the number of events that happen before or after, but not at 60 seconds of play)

  • Show count instances where start time > 00:05:01.45
    (display the number of events that take place after 5 minutes and 1 second)

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SportsCode Script Example:
Display the number of shots in the match that were on target by counting the number of events “Shot” but only those with the label “On Target” in them. Then display the result with a text in front with the message “Total Shots On Target” (i.e. Total Shots On Target 12”).

Commands To Set Conditions

These commands are used to create the logic of your selection. For example, if you want to select all shots in the first half, you may use the AND statement to make sure the scripts only considers the events of “shots” when the events “first half” is also true (i.e. shots AND first half). These commands can also be used for filtering specific events by name, using the WHERE statement (i.e. apply only WHERE an event name is “Shots”).

---

AND

Add additional true statements to your element selection logic. Used inside other commands. 

• Example:

  • SHOW COUNT BUTTON NAME “Shots” AND BUTTON NAME “First Half”

  • SHOW COUNT $Away_Goals AND $Home_Goals

  • SHOW COUNT $PlayerOne AND $PlayerTwo

---

IF (condition, true, false)

Similarly to Excel, it conditions a the script or section of the script to whether the condition in the IF statement is true or false (i.e. IF Home Goals are higher than Away Goals then write “Home Win”, otherwise write “Draw/Loss”).

• Examples:

  • IF ($AwayGoals < $HomeGoals, show “Home Win”, show “Draw/Loss”)

---

WHERE

Filtering by adding specific conditions (WHERE ROW = X)

• Examples:

  • WHERE ROW = “Shots On Target”

  • WHERE ROW > 1

  • WHERE ROW < 10

---

NOT

Exclude an event or label from your selection.

• Examples:

  • $Goals= NOT “Messi” show count $Goals
    (display all goals scored by all players BUT Messi)

  • $Cards = NOT “Red” show count $Cards
    (display all cards that are not red, therefore all yellow cards)

---

OR

Selects events or labels if either one of two conditions is met.

• Examples:

  • $Goals= “Messi” OR “Suarez” show count $Goals
    (display all goals scored by either Messi or Suarez)

  • $Pass= NOT “Iniesta” OR “Xavi” show count $Pass
    (display all passes by players that are not Iniesta nor Xavi)

Commands To Display Outputs

Counts, percentages, ratios or even time metrics can be displayed in real-time in a code button within the code window. You can also display any text (known as string) by writing it with quotation marks (i.e. “Goals Scored”).

Similarly to Excel or a normal calculator, you can perform calculations inside your Script Editor of the code button and display the results. To do so, you simply add the operation after the word ‘show’ and the code button will display the result.

Scripting also allows you to display both text and numbers in one single code button. To do this, you will use a plus symbol (+) to join the text and the calculation results. This plus symbol allows you to join any text together with a number or other pieces of text. Any calculations will need to be written using parenthesis.

---

SHOW “text”

Display text or/and numbers in a code button.

Similar Commands:
SHOW calculation (add, subtract, divide or multiply any number and display the result in the code button , i.e. 2+2)
SHOW “text” + numbers (add a label before the number you want to display, i.e. shots = 5)

• How it works:

  • Type the word show in your Script Editor panel.

  • Anything you type after “show” will be displayed in the code button.

• Examples:

  • Show “Home Team”

  • Show “Shots On Target”

  • Show 2 + 2
    (the code button will display the number 4)

  • Show (5 – 2) * ( 4 – 1 )
    (displays the number 9)

  • Show “shots = “ + 5
    (displays ‘shots = 5’)

  • Show “goal difference = “ + (4 – 2)
    (displays ‘goal difference = 2’)

  • Show “Team scored “ + (1 + 2) + “goals”
    (displays ‘team scored 3 goals’)

---

TIMER (seconds, format)

Converts a time value that is in seconds to an hourly format.

Similar Command:
TIMER2 (converts a time value format from seconds to minutes)

• Examples:

  • TIMER ( 3601.123,0 )
    (displays 1:00:01)

  • TIMER ( 3601.123,2 )
    (displays 1:00:01.12)

  • show TIMER ( 3601.123, "HH.mm.ss a" )
    (displays 01.00.01 AM)

  • TIMER2 ( 3601.123,2 )
    (displays 60:01.12)

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SportsCode Script Example:
Display the % possession of each team by first, creating a variable that calculates the total length of time (in seconds) each team had the ball, and then creating a third variable that divides the length of time for a particular team by the total length of time that both teams had the ball. Display the results by removing the decimal points and adding the % sign as text after the calculation result.

Commands To Run Calculations

COUNT

Counts the number of labels in the timeline, even if they appear multiple times in one event.

• Examples:

  • COUNT “Messi”

  • COUNT “Messi” where row = “FC Barcelona Goals”

  • COUNT “Missed” where row = “Home Team Shots”

---

LENGTH

Calculates the length of time (in seconds) of the event labels or instances in the timeline.

• Example:

  • LENGTH “Home Possession”

  • LENGTH “Home Possession” IN ROW = “Ball In Play”

  • LENGTH “Home Possession” IN ROW = “First Half” OR “Second Half”

---

ROUND (#, digits)

Rounds a number to the specified number of digits from the decimal point.

Similar Commands:
DECIMAL (#, digits) (rounds down a number to the specified number of digits from the decimal point, returning it as a string)
FLOOR (#, digits) (rounds down a number to the specified number of digits from the decimal point, returning it as a number)
CEILING (#, digits) (rounds a number up to the nearest decimal point)
ABS (#) (converts a number into its absolute value by removing all decimals)

• Examples:

  • show ROUND (34.235, 2)

  • show ROUND (3423.456, -2)

  • show DECIMAL ( ROUND(0.499,0) ,2)

  • show CEILING (34.23001, 2)

---

TIME

Selects the events with a specific length of time.

• Examples:

  • Count instances where time < 60
    (counts the number of instances shorter than 60 seconds)

  • Count "Counterattack" where row="Possession" and time < 30
    (counts the counterattacks of less than 30 seconds)

Commands To Change Formatting & Appearance

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SportsCode Script Example:
Create a shot frequency map using toggle buttons of player name that, when pressed, change the name of a separate button. This button is then used as the reference for all six buttons in the pitch location map to count the number of shots for the selected player for each position of the pitch using two types of labels: player name and shot location. The % of each location is then calculated with these counts. Lastly, an IF statement changes the colour of the button based on the % calculated by setting a different colour for different ranges.

BUTTON COLOR

Changes the background colour of the current button.

Similar Command:
SEND BUTTON COLOR (changes the background colour of a different button)

• Examples:

  • BUTTON COLOR (100,0,0)
    (red)

  • BUTTON COLOR "FC Barcelona"
    (changes the background colour of the current button to that from the button named "FC Barcelona"

  • SEND BUTTON COLOR (100,0,0) TO BUTTON "Manchester Utd"
    (changes the background colour of the button “Manchester Utd” to red)

---

TEXT COLOR

Changes the text colour in the name of the button.

Similar Command:
SEND TEXT COLOR (changes the text colour in a different button)

• Examples:

  • TEXT COLOR (100,0,0)
    (red)

  • SEND TEXT COLOR (100,0,0) TO BUTTON "Manchester Utd"
    (changes the colour of the text in the “Manchester Utd” button to red)

---

BUTTON OPACITY

Changes the opacity of the current button

• Examples:

  • BUTTON OPACITY 50
    (50% visible)

  • BUTTON OPACITY 0
    (hides button)

---

MOVE BUTTON BACK

Arranges the button so that it moves to the back of the code window not to overlap with other buttons.

Similar Command:
MOVE BUTTON FRONT (moves button to the front over other code buttons)

• Examples:

  • MOVE BUTTON BACK

  • MOVE BUTTON FRONT

---

OUTPUT COLOR

Changes the text colour of the output from the button’s script.

• Examples:

  • OUTPUT COLOR (100,0,0) changes the output color of the text to red.

---

 Commands To Perform Actions

PUSH BUTTON button_name UP

Activates or deactivates a button.

Similar Commands:
PUSH BUTTON button_name DOWN (pushes a specific button down)
PUSH BUTTON UP WITH DELAY(pushes current button up after a specified delay in seconds)
PUSH BUTTON DOWN WITH DELAY (pushes current button down after a specified delay in seconds)
PUSH BUTTON UP (pushes the current button up)
PUSH BUTTON DOWN (pushes the current button down)

• Examples:

  • PUSH BUTTON "name1" DOWN

  • PUSH BUTTON "name1" UP IN WINDOW "window1"

  • PUSH BUTTON DOWN WITH DELAY 0.2

---

RENAME new_button_name

Renames the current button.

Similar Commands:
RENAME GROUP new_group_name (renames the group name for the current button)
SEND value TO BUTTON button_name (changes the button name of a different button to the specified value)

• Example:

  • RENAME "Atletico Possession"

  • RENAME GROUP "Away Team"

  • SEND "FC Barcelona" TO BUTTON "Home Team"
    (renames the button with ID “Home Team” to be called “FC Barcelona”)

---

OPEN

Check whether a timeline is currently open in SportsCode.

Similar Command:
NOT OPEN (check whether a timeline is currently not open in SporstCode)

• Examples:

  • IF ("FC Barcelona v Real Madrid" open, show "YES")
    (display the text "YES" if the timeline is open)

---

Automated Tracking Of Body Positioning Using Match Footage

A team of imaging processing experts from the Universitat Pompeu Fabra in Barcelona have recently developed a technique that identifies a player’s body orientation on the field within a time series simply by using video feeds of a match of football. Adrià Arbués-Sangüesa, Gloria Haro, Coloma Ballester and Adrián Martín (2019) leveraged computer vision and deep learning techniques to develop three vector probabilities that, when combined, estimated the orientation of a player’s upper-torso using his shoulder and hips positioning, field view and ball position.

This group of researchers argue that due to the evolution of football orientation has become increasingly important to adapt to the increasing pace of the game. Previously, players often benefited from sufficient time on the ball to control, look up and pass. Now, a player needs to orientate their body prior to controlling the ball in order to reduce the time it takes him to perform the next pass. Adrià and his team defined orientation as the direction in which the upper body is facing, derived by the area edging from the two shoulders and the two hips. Due to their dynamic and independent movement, legs, arms and face were excluded from this definition.  

To produce this orientation estimate, they first calculated different estimates of orientation based on three different factors: pose orientation (using OpenPose and super-resolution for image enhancing), field orientation (the field view of a player relative to their position on the field) and ball position (effect of ball position on orientation of a player). These three estimates were combined together by applying different weightings and produce the final overall body orientation of a player.

1. Body Orientation Calculated From Pose

The researchers used the open source library of OpenPose. This library allows you to input a frame and retrieve a human skeleton drawn over an image of a person within that frame. It can detect up to 25 body parts per person, such as elbows, shoulders and knees, and specify the level of confidence in identifying such parts. It can also provide additional data points such as heat maps and directions.

However, unlike in a closeup video of a person, in sports events like a match of football players can appear in very small portions of the frame, even in full HD frames like broadcasting frames. Adrià and team solved this issue by upscaling the image through super-resolution, an algorithmic method to image resolution by extracting details from similar images in a sequence to reconstruct other frames. In their case, the researcher team applied a Residual Dense Network model to improve the image quality of faraway players. This deep learning image enhancement technique helped researchers preserve some image quality and detect the player’s faces through OpenPose thanks to the clearer images. They were then able to detect additional points of the player’s body and accurately define the upper-torso position using the points of the shoulders and hips.

Source: Arbues-Sangüesa, A.; Haro, G.; Ballester C. & Martin A. (2019) Head, Shoulders, Hip and Ball... Hip and Ball! Using Pose Data to Leverage Football Player Orientation. Barça Sports Analytics Summit.

Once the issue with image quality was solved by researchers and the player’s pose data was then extracted through OpenPose, the orientation in which a player was facing was derived by using the angle of the vector extracted from the centre point of the upper-torse (shoulders and hips area). OpenPose provided the coordinates of both shoulders and both hips, indicating the position of these specific points in a player’s body relative to each other. From these 2D vectors, researchers could determine whether a player was facing right or left using the x and y axis of the shoulder and hips coordinates. For example, if the angle of the shoulders shown in OpenPose is 283 degrees with a confidence of 0.64, while the angle of the hips is 295 degrees with a confidence level of 0.34, researchers will use the shoulders’ angle to estimate the orientation of the player due to its higher confidence level. In cases where a player is standing parallel to the camera and the angles of either the hips or the shoulders are impossible to establish as they are all within the same coordinate in the frame, then researchers used the facial features (nose, eyes and ears) as a reference to a player’s orientation, using the neck as the x axis.

Source: Arbues-Sangüesa, A.; Haro, G.; Ballester C. & Martin A. (2019) Head, Shoulders, Hip and Ball... Hip and Ball! Using Pose Data to Leverage Football Player Orientation. Barça Sports Analytics Summit.

This player and ball 2D information was then projected into the football pitch footage showing players from the top to see their direction. Using the four corners of the pitch, researchers could reconstruct a 2D pitch positioning that allowed them to match pixels from the footage of the match to the coordinates derived from OpenPose. Therefore, they were now able to clearly observe whether a player in the footage was going left or right as derived by their model’s pose results.

Source: Arbues-Sangüesa, A.; Haro, G.; Ballester C. & Martin A. (2019) Head, Shoulders, Hip and Ball... Hip and Ball! Using Pose Data to Leverage Football Player Orientation. Barça Sports Analytics Summit.

In order to achieve the right level of accuracy in exchange for precision, researchers clustered similar angles to create a total of 24 different orientation groups (i.e. 0-15 degree, 15-30 degrees and so on), as there was not much difference in having a player face an angle of 0 degrees or 5 degrees.

 2. Body Orientation Calculated From Field View Of A Player

Researchers then quantified field orientation of a player by setting the player’s field of view during a match to around 225 degrees. This value was only used as a backup value in case of everything else fails, since it was a least effective method to derive orientation as the one previously described. The player’s field of view was transformed into probability vectors with values similar to the ones with pose orientation that are based on y coordinates. For example, a right back on the side of the pitch will have its field of view reduced to about 90 degrees, as he is very unlikely to be looking outside of the pitch.

Source: Arbues-Sangüesa, A.; Haro, G.; Ballester C. & Martin A. (2019) Head, Shoulders, Hip and Ball... Hip and Ball! Using Pose Data to Leverage Football Player Orientation. Barça Sports Analytics Summit.

3. Orientation Calculated From Ball Positioning

The third estimation of player orientation was related to the position of the ball on the pitch. This assumed that players are affected by their relative position in relation to the ball, where players closer to the ball are more strongly oriented towards it while the orientation of players further away from it may be less impacted by the ball position. This step of player orientation based on ball position accounts for the relative effect of ball position. Each player is not only allocated a particular angle in relation to the ball but also a specific distance to it, which is converted into probability vectors.

Source: Arbues-Sangüesa, A.; Haro, G.; Ballester C. & Martin A. (2019) Head, Shoulders, Hip and Ball... Hip and Ball! Using Pose Data to Leverage Football Player Orientation. Barça Sports Analytics Summit.

Combination Of All The Three Estimates Into A Single Vector

Adrià and the research team contextualized these results by combining all three estimates into as single vector by applying different weights to each metric. For instance, they found that field of view corresponded to a very small proportion of the orientation probability than the other two metrics. The sum of all the weighted multiplications and vectors from the three estimates will correspond to the final player orientation, the final angle of the player. By following the same process for each player and drawing their orientation onto the image of the field, player movements can be tracked during the duration of the match while the remain on frame.

In terms of the accuracy of the method, this method managed to detect at least 89% of all required body parts for players through OpenPose, with the left and right orientation rate achieving a 92% accuracy rate when compared with sensor data. The initial weighting of the overall orientation became 0.5 for pose, 0.15 for field of view and <0.5 for ball position, suggesting the pose data is the highest predictor of body orientation. Also, field of view was the least accurate one with an average error of 59 degrees and could be excluded altogether. Ball orientation performs well in estimating orientation but pose orientation is a stronger predictor in relation to the degree of error. However, the combination of all three outperforms the individual estimates.

Some limitations the researchers found in their approach is the varying camera angles and video quality available by club or even within teams of the same club. For example, matches from youth teams had poor quality footage and camera angles making it impossible for OpenPose to detect players at certain times, even when on screen.  

Source: Arbues-Sangüesa, A.; Haro, G.; Ballester C. & Martin A. (2019) Head, Shoulders, Hip and Ball... Hip and Ball! Using Pose Data to Leverage Football Player Orientation. Barça Sports Analytics Summit.

Finally, Adrià et al. suggest that video analysts could greatly benefir from this automated orientation detection capability when analyzing match footage by having directional arrows printed on the frame that facilitate the identification of cases where orientation can be critical to develop a player or a particular play. The highly visual aspect of the solution makes is very easily understood by players when presenting them with information about their body positioning during match play, for both first team and the development of youth players. This metric could also be incorporated into the calculation of the conditional probability of scoring a goal in various game situations, such as its inclusion during modeling of Expected Goals. Ultimately, these innovative advances in automatic data collection can relief many Performance Analyst from hours of manual coding of footage when tracking match events.

Citations:

Arbues-Sangüesa, A.; Haro, G.; Ballester C. & Martin A. (2019) Head, Shoulders, Hip and Ball... Hip and Ball! Using Pose Data to Leverage Football Player Orientation. Barça Sports Analytics Summit. Link to article.

Application of Video Technology in Football Refereeing – VAR

A referee in a match of football has full authority to enforce the Laws of the Game, consequently being exposed to controversies when it comes to interpreting a given situation. This risk is evidenced when considering that the referee’s observation can be influenced by various factors:

• Position on the field (i.e. being prone to a parallax errors; the deceptive change in the relative position of an object with a change in the position of the observer)

• The frequent high ball speeds, particularly during shots on goal, that can reach up to 120km/h, thus make it is challenging for the human vision and other cognitive systems to estimate its position.

Supporting technologies to aid referees’ decision-making and reduce incorrect decisions have been emerging in recent years. Some of these include the use of microchip balls and signals to referee, microphones and earpieces, electronic sensors in goal posts, tracking systems for off-side play, goal-line technology and, most recently, video assistant referee. These type of technological officiating aids aim to enhance the overall quality of the refereeing in the game of football. By having an improved decision making by referees, the integrity of the game is protected and more qualitative content for football fans is produced. Nevertheless, arguments have emerged suggesting that the introduction of delays may have tainted the viewing experience, amongst other controversies.

What is Video Assistant Referee (VAR)?

Hawk-Eye Innovations, owned by Sony, currently holds the patent for the VAR technology and are under a licensing agreements with FIFA to supply access to Hawk-Eye’s technology for top leagues around the world. This additional refereeing process consists on Video Assistant Referees (VAR) watching matches in a video operation room (VOR) assisted by an assistant VAR (AVAR) and replay operator (RO). For all Premier League matches since VAR adoption, this video operation room and all VAR officials have been based in Stockley Park in South West London.

This refereeing system is football's first broad use of video technology to reach more correct decisions. It enables referees a second chance to look at a play before deciding on the appropriate course of action. For instance, it solves the issue of diving in the box to force a penalty and also protects referees from enraged fans and players by giving them more credit, more authority, fewer mistakes and overall more robust justification when taking action against them.

Inside the VOR, VAR has access and control of TV broadcast footage and is connected to the communication system used by match officials, with either the VAR or the AVAR advising the referee if the game needs to be reviewed. VAR is also in charge of selecting the best angle and replay speed during reviews. In theory, it should only get involved when officials have made a 'clear and obvious error' in one of four key areas; goals, penalties, red cards and mistaken identity. This philosophy of “minimum interference with maximum benefit” promoted by the International Football Association Board (IFAB) in their VAR protocol hopes that the introduction of VAR won't disrupt the flow of the game.

Additional to the video operating room, the referee is able to further review a given play by entering the Referee Review Area (RRA) on the pitch side, an ‘on-field review’ (OFR) can be undertaken.

This RRA must be:

• in a visible location outside the field of play

• clearly marked

This area is exclusive to the referee. A player, substitute or substituted player who enters the RRA is cautioned and any team official who enters the RRA is publicly given an official warning or also cautioned with a yellow card.

Other official criteria when using VAR includes:

• VAR reviews are not to be used for first or second yellow cards.

• The referee must always make a decision prior to the use of VAR.

• Final decision is always taken by the referee.

• No time limit: accuracy is more important than speed.

• Players and team officials must not surround the referee.

• Referee must remain visible during review.

• If play continues after a reviewed incident, any disciplinary action taken during the post-incident period is not cancelled.

• If play has stopped and been restarted, the referee may not use VAR.

• Coaches or players cannot request a review.

The VAR review process follows three main steps: incident, review/advice and decision. In a match situation, it would look something like the following:

1. The Video Assistant Referees review the video footage of an incident or referee requests review. However, only the referee can initiate a formal review where play is stopped.

2. The Video Assistant Referees advises the on-field referee through an earpiece.

3. The referee puts his hand up to stop play if required.

4. The referee informs the players that a decision is being reviewed.

5. If VAR information is accepted, referee reverts decision by drawing a rectangle with their arms.

6. When decisions are not obvious or subjective, the referee will watch a replay on a pitch-side screen (avoided whenever possible to prevent long delays).

7. Referee decides to maintain or reverse the original decision.

On top of the general IFAB guidelines for the use of VAR, the Premier League also implemented variations with their own specific criteria. Some of these include:

• Referees avoid on-pitch reviews at the pitch-side screen and instead have been told to trust the advice by VAR.

• Stick to a higher threshold and only intervene when 'clear and obvious' errors.

• Follow a softer interpretation of the handball rule when not a deliberate act of extending the arm away from the body, as oppose to IFAB’s laws that state penalties should be given if the hand or arm extends beyond the natural silhouette of the body, regardless of intent.

• Disallow any goal in which the ball strikes the hand of an attacking player in the build-up, deliberate or otherwise.

Impact Of VAR To Modern Football

In raw statistical terms, Lago-Peña et al. (2019) found that there was a significant decrease in the number of offsides, fouls and yellow cards after the implementation of the VAR in both the Bundesliga and Serie A. This may possibly be due to a less aggressive behaviour by players due to additional officiating, as players become more careful with committing fouls, tackles and protesting. The study also found an increase in the number of minutes added to the playing time in the first half. Moreover, the number of goals and number of fouls decreased in Serie A, as so did offsides in Bundesliga and yellow cards in both competitions.

Caruso et al. (2016) also discovered that slow motion video review can distort reality and can change the way body movements and intentions are perceived. Splitz et al. (2018) asked 88 elite referees to evaluate 60 different foul play situations taken from international matches, replayed in either real time or slow motion. They found that decisional accuracy was similar in slow motion and real time, as faster speeds more closely replicate the cognitive processing demands required in dynamic and time-constrained environments. However, in situations like offside decisions slow motion might be of added value to assistant referees as they require evaluation of spatial and temporal landmarks. They also found that referees penalized situations more severely in slow motion compared to real time as viewing a situation in slow motion, compared with regular speed increases the perceived intent of a violent action. This meant that video replay speed has an important impact on the disciplinary decision given by the referee.

Moreover, adjustments to the way the rules are enforced also have behavioural influences on players and coaches. Pauses during video review allow coaches to relay information to players in a more cohesive manner. It provides them an opportunity to have an immediate input in the game, particularly during high stake scenarios. They are able to reach players directly, rather than through a captain or teammate, thoroughly explain their tactical changes and checking for understanding. This ultimately improves communication between players and coaching staff. The increased number of opportunities to introduce additional tactical inputs by coaches during VAR officiated matches enhances the importance on coaching decisions by each team at critical intervals of the match. In other words, coaches are now more capable of closely managing their players.

Attacking players may also see their game dynamics affected. Referees are now more likely to let play continue if there is a close offside decision and that could be checked later through VAR. This new scenario increases attackers’ incentive to make early runs on high defensive lines to push the limits of close offside margins. They are more likely to seek scoring opportunities as incorrect offside calls that stop attacking plays become less frequent. Ultimately, the returns on risking an offside call during attacking play, particularly when passing through defensive lines, become substantially higher.

Another study by Anik, L. (2018) evaluated how VAR influences the observers’ perceptions on quality, flow, outcome and enjoyment of matches, as well as the perception on referees’ performance, credibility and authority. She found that a referee who made the correct call without using VAR was considered more competent. On the other side, the referee’s reputation was negatively impacted when using VAR, even in ambiguous situations where he made the correct decision by overturning the original call. During these debatable situations, a referee was perceived as less competent when choosing to benefit from video technology. A referee’s use of VAR may be only beneficial to him when he confirms the decision he has already made on the field.

Controversy

A number of fans and pundits have argued that VAR disrupts the flow of the game. Even though IFAB encourages a “minimum interference for maximum impact” use of VAR, a general dissatisfaction exists amongst fans with the current amount of time taken to review incidents. In comparison, Rugby Union’s TMO and cricket’s DRS have proven very quick, efficient methods of reviewing play with confusion created. The perception with football, however, is that VAR has a long way to go to become as established as similar systems in other team sports.

Additionally, spectators are not always aware of when a decision is being reviewed, particularly in grounds that don't have big screens but with a loud speaker announcement. Transparency of the referee’s decision process is currently inexistent, as conversations between VAR and match officials are not broadcasted to the public, unlike video arbitrage in other sports such as Rugby Union. Gary Lineker has referred to this point in various occasions, as seen in the below interview with Danny Baker.

Another area of concern in the use of technology for refereeing is the strict and subjective nature of football's laws. For instance, goals can be disallowed for the tiniest of margins and many incidents are left open to interpretation. Despite the availability of replays, debates and disagreements about penalty and other incidents still remain. Human interpretation is still a major factor in decision making. Suggestions have emerged about now being a good time to look at the laws of the game, such as the offside rule bring redrafted to try and avoid goals being disallowed over matters of inches, claiming that microscopic analysis of offsides goes against the original spirit of the law.

Concerns about this spirit of the game are also raised regarding goal celebrations. Fans and pundits have addressed the issue that VAR reviews during goals are taking the joy out of the game because nobody can celebrate properly until VAR review is completed. This spontaneous joy of goal celebrations being lost due to the possibility of a review could become detrimental to the atmosphere in stadiums.

Other have also suggested that the lack of debate it causes amongst fans can also affect the nature of the sport’s fanbase, arguing that fans would rather a free flowing game over a perfectly refereed match where they sit in silence. This brings in the perspective of addressing the game of football as an entertainment rather than purely a sporting machine.

Lastly, in developing countries, the high cost of using the system, such as licensing fees, may restrict access to VAR technology for many leagues and divisions, thus widening the gap with more established, wealthy leagues. In Brasil, VAR was rejected due to the estimated cost per season of $6.2 million. South American and African leagues will face financial challenges adopting these new IP-protected technologies.

Citations:

• Anik, L. (2018) How Soccer’s Video Assistant Referee (VAR) Influences Belief in Human Referees’ Competence. Behavioural Scientist. Link.

• Caruso, E. M.; Burns, Z. C. & Converse, B. A. (2016). Slow motion increases perceived intent. Proceedings of the National Academy of Sciences of the United States of America, 113(33), 9250–9255 https://doi.org/10.1073/pnas. 1603865113

• IFAB (2017) Video Assistant Referees (VARs) Implementation handbook for competitions. The International Football Association Board, Zurich Switzerland. Link.

• Lago-Peñas C.; Rey E. & Kalén A. (2019) How does Video Assistant Referee (VAR) modify the game in elite soccer?, International Journal of Performance Analysis in Sport, 19(4), 646-653, DOI: 10.1080/24748668.2019.1646521

• Lakpini, C. (2019) The Video Assistant Referee (VAR): The Patent-Protected Technology Changing the Face of Football, IP Unit, University Of Cape Town, Intellectual Property Unit. Link.

• Spitz, J.; Moors, P.; Wagemans, J. & Helsen, W.F. (2018). The impact of video speed on the decision-making process of sports officials. Cognitive Research: Principles and Implications, 3(16), 1-10. https://doi.org/10.1186/s41235-018-0105-8

Scout7, a bespoke software for scouting

Scout7 is one of the platforms offered by Opta to help decision making in the global recruitment and development of players. It offers clubs performance data on over 520,000 players across the world and the ability to watch over 3 million minutes of video footage on their performances. The advantage of Scout7 over similar platforms is that it is usually integrated in a bespoke manner into the club's systems, allowing it to be tailored differently for each club according to that club's needs.

More than just an extensive player database, Scout7 allows clubs across the general management of their data by providing them with clear organisation and access to their information and support various departments' needs. Under the umbrella brand Intelligent Sports Framework, the Scout7 platform offers three different services to not only help with scouting but also improve the video databases for the clubs as well as provide tools for training and player development. The iSF platform is constituted of ProScout7, Scout7.tv and TrainingGround, each offering a different set of features to complement the overall software. iSF enables a scouting team to create their own custom report templates and live data widgets so that the information most frequently needed can be accessed almost immediately.

Scout7 captures their own data from matches and players across the world that can be easily accessed by scouts through Scout7.tv, where Scout7 uploads all their high definition footage. Scout7.tv also offers many advanced filtering options to find specific players or game, analyse game statistics and also create your own clips of interesting players. On top of that, the data can be augmented with other compatible third party integrations if the club needs to do so, converting it in an even more complete platform for scouting. 

It is with ProScout7, another piece of Scout7's overall platform, where all the scouting information and actions take place. ProScout7 is a management system for scouting reports and assessment of players, where information can be flagged and shared to the rest of the scouting department for further analysis or decision making. In this section, scouts can create recommendation lists of players they wish to flag and rate each of the players the club wishes to pursue. These lists and player ratings can also be archived for later use. Similarly to Scout7.tv, scouts can also use advanced search functionality to find players of certain criteria and characteristics they are looking for, and compliment their assessments with reports from the Scout7 team themselves to consolidate a more complete view on particular players.

Lastly, the TrainingGround platform from Scout7 aims to take a more internal look at the club's current players and support coaches with development and injury prevention. From basic functionally such as planning training drills and reporting on performance of the team's matches to capturing physiological data of each player to run comparisons and deeper analysis as well as keeping a health record of injuries and treatments. While TrainingGround offers a simpler set of tools than ProScout7 and Scout7.tv, it demonstrates the attempt Scout7 is making to become the sole platforms for day-to-day club management in all areas and departments. Thanks to their close collaboration with the clubs due to its tailor-made integration of Scout7, they can find technological gaps in other areas of the club, get valuable feedback directly from the team and go back and build solutions that fit exactly those needs.

LEARN MORE ABOUT SCOUT7

GPS technology in professional sports

Global Positioning System technology has been used in professional sport for some time, in both training sessions and during competition. Through the use of Electronic Performance and Tracking System (EPTS) devices, teams can track player’s movement on the pitch and collect vast amounts of data on their performance; such as their running speed, distance run, their position on the pitch, their heart rate and their body's work rate.

These 'wearable' devices and the data they collect have multiple uses, one major of them being the prevention of injuries. By tracking a player's sprints and distance covered the coaching staff can determine whether such player is fit for their next game or could benefit from resting. According to Taylor from iSportAnalysis (2017), studies have shown that when athletes train at a higher rate than the season's average there tend to be more injury occurrences. An increase in training and game-play intensity without adequate recovery can results in an increase of injury rate. Coaches can now predict and prevent player injuries by monitoring these patterns from the GPS metrics obtained, and can make the right decisions by knowing whether their player is over training, whether they need a rest or whether they are in peak condition.

However, GPS is not only used to track a player's health and fitness. The value of the data collected through these EPTS devices goes beyond that. This data can also map a player's positioning on the pitch to help identify the most frequent spaces covered and provide insights on how well various areas were utilised. This can then provide a valuable source of information to adapt training and development of specific players according to their physical and tactical needs.

The type of data captured by the GPS trackers can vary largely by provider and the needs of the team using the data. As with most areas in performance analysis, the data captured by GPS needs to be used and analysed appropriately in the context of the sport, athlete or situation. An isolated data point can only provide very little insight on what is really happening, if any at all. This is why the use of GPS metrics require the combination of multiple variables in order to obtain a complete picture. For instance, two athletes may run the same distance at the same average speed, but taking a look at heart rates or speed intervals can provide a closer look into their fitness and amount of amount of load each body is taking to deliver that outcome. The most common data point being collected are:

• Total distance covered

• Average running speed

• Total running distance (high pace)

• Total sprinting distance (full speed sprinting)

• Average acceleration time

• Average deceleration time

• Heart rate (to identify athlete's work rate)

• Positioning on the field

• Time of high intensity play

• Time of low intensity play

• Athlete's load (the demand on an athlete's body)

• G-Force / impact data (for impact sports like rugby)

There are various providers of GPS technologies offering devices and services to professional clubs and athletes. One of the technology providers is Exelio, which sells its technology under the brand name GPEXE and partners with clubs such as AC Milan or AS Monaco. Their strength in the market can be attributed by its 20 Hz device frequency, much higher frequency devices than that of most of its competitors. With this high frequency GPEXE achieves a higher accuracy of information when tracking a player's changes in speed and direction, something a lot of providers struggle to do with lower frequencies. However, there are many important players in the GPS Sport technology industry partnering with elite sport clubs:

• Catapult

  • Partners with: Bayern Munich FC, Paris Saint Germain FC, Wales Rugby and NFL's Steelers, amongst others.

• PlayerTek

  • Partners with: Liverpool FC, Celtic FC, Wigan Athletic FC and Malmo FF, amongst others.

• StatSport

  • Partners with: Tottenham Hotspurs FC, Portugal FA, Manchester City and West Ham United, amongst others.

• GPSports

  • Partners with: Real Madrid FC, Chelsea FC, Atletico Madrid and Spain FA, amongst others.

• GPEXE

  • Partners with: AC Milan, Inter Milan, Sampdoria and AS Monaco, amongst others.

Historically, acquiring this technology was cost-prohibitive for most teams, even at professional levels. However, as technology advances these devices are becoming more budget-friendly allowing more teams to adopt them for their training sessions and player development. Some lower league clubs are even loaning the technology from the providers in exchange of free usage of the data collected for research and development to improve their products.

These wearable pieces of equipment are normally placed on athlete's torsos. They are composed of various sensors to track different types of metrics and allow to store and transfer the data to a common data repository. According to SimpliFaster (2017), there are 4 types of sensors used in Player Tracking devices today: an accelerometer, a gyro, a magnetometer and a GPS module. Each sensor has a unique function that compliments the role of each other sensor. For instance, an accelerometer measures the changes in rates of perceived forces while a gyro give the data from the accelerometer direction by using the Earth's gravity. Similarly, the magnetometer will use the Earth's magnetic field to also provide direction to the data from the accelerometer. On top of that, the GPS module completes the data with satellite-positioning information.

However, no modern tracking device has been proven to be 100% accurate and reliable. An example of that is that these torso devices may be missing important information about the center of gravity of each athlete. Also, the data captured may often be indicative rather than factual due to the limitations of GPS accuracy today. Advances in technology will show an improvement in coming years on these devices and their reliability. Not only by extending battery life or reducing the size of the wearable equipment but developments in sensors and data capturing technologies will drive the future of GPS tracking in sport. For example, foot sensors are currently being explored and can prove to provide a lot more precise information of the forces and gravity of each athlete.

An overview of Dartfish, a powerful videoanalysis software

Founded in Switzerland in 1999, Dartfish is a videoanalysis solution that allows analysts to capture, analyse and share videos of training sessions and matches. The software offers tools to capture the footage directly into the platform, tag events real-time, and upload, organize and share the various videos produced. A video is displayed with the match footage on one of the screens with a panel of tags and codes next to it where an analyst is able to visualize instantly key actions identified and underline what the action reveals.

Source: Dartfish.com

Dartfish offers a complete set of features for analysis in many different sports. Analysts are able to tag, review and edit actions seen in the footage in real-time while continuing to record events that continue to take place. Report creation tools are then used to identify certain patterns in actions in order to sport strengths and weaknesses and better define an athletes or team's strategy. The software is also great for video highlights, with ability to playback and zoom in key actions and add tables, lines and any other shapes into the footage for clear presentations.

While Dartfish has a built-in capture system, it also enables you to import footage from other sources from a wide range of devices. It supports multiple video formats such as Mpeg-4, h.264 and even 4K videos. Their most complete solution also allows you to record video from static IP cameras around a playing ground. Once the video has been captured or imported, the trim and time-shift tool allows you to edit and replay certain parts of the footage before starting to code it.

During video analysis, aside from basic drawing features such as freehand, line, circle, rectangle and arrow, Dartfish allows you to create slowmotion highlights, fast-forward/fast-rewind the less important sections, zoom in relevant parts of the screen and create snapshots of key moments. But it is feature such as their split video analysis when playing to moments simultaneously, as well as the measuring of angles and automatic tracking of trajectories, that make Dartfish standout as a powerful video analysis platform.

Dartfish is a flexibile and adaptable platform as the interface can be modified to each analyst's preferences and needs. An analyst can define his or her own tagging panel by identifying the right keywords to use as tags and assigning them a particular button in the keyboard. Tabs and boxes can also be created with multiple panels for different tagging functions. Tagging can take place either through an imported video or live as the video is being captured.

Once the footage has been imported and tagged completed, Dartfish offers reporting capabilities to analyse the relevant highlights that have taken place. The software summarises frequency and duration data in stats tables and graphs of the different tagged events to provide a quantitative summary of the match or training session. It also allows analysts to apply multi-criteria filters and create various playlists and montages for future reference. All these videos can then be exported or shared via the cloud with coaches and athletes.

The Dartfish software comes in four different packages:

• Dartfish Mobile for $5 a month

• Dartfish 360 for $20 a month

• Dartfish 360 S for $40 a month

• Dartfish Live S for $70 a month

Find out more about Dartfish

Opta Sports: the leading sports data provider

Launched in 1996, Opta Sports has been the major player in data collection and distribution in sports for over two decades, offering statistical information and player performance data from major sport leagues all over the world to media clients and clubs themselves. After transferring ownership several times over the years, in 2003 Opta was acquired by Perform Group, a sports media company based in the UK who also own other brands such as Goal.com and Sporting News Media in the US. Perform Group itself is owned by Ukrainian businessman Len Blavatnik through his privately held multinational industrial group called Access Industries.

With over 400 employees around the world, Opta collects data on 60,000 fixtures a year across 30 different sports in 70 countries. While football is their main business, the company also collects data for rugby union, rugby league, cricket, american football, baseball, basketball and ice hockey. Their data varies in the level of complexity and depth, from simple live scores to augmented data through statistical modeling. They classify their data offerings in four different tiers:

• Core: live scores, updates and post-match content

• Classic: team and player-level aggregated data and statistics

• Performance: the most detailed and accurate level of data

• Advanced: analytical assessment and modelling on augmented data

But how do they collect all this data? Opta hires teams of full-time and part-time analyst to watch every single game that takes place and, with the use of their data collection video software, notate all the various events that occur on the field, often capturing up to 2,000 pieces of data per match. Opta's data collection software operates similarly to a video game, where each combination of buttons will represent an action by a certain player allowing the analyst to press the appropriate combination of buttons as they watch the live game. Three analyst will be involved in each game: one for the home team, one for the away team and a third one to double check the data. The data is then checked by a post-match team to ensure 100% accuracy.

Once the data has been collected, Opta offers different methods to distribute the data to its clients, such as feeds, widgets and apis. Feeds are one of the most popular method due to the level of flexibility and detail Opta can offer depending on its client's requirements, whether is live or historical data in a number of different formats. While Opta don't expose their feeds pricing structure, presumably to be able to adapt it based on client needs, it is suspected that it can range between £500 to £2,000 in a number of cases. However, pricing is stablished case by case and is highly negotiable. Opta takes into consideration whether it can form a strategic partnership with the client (ie. size of the business requesting the data), revenue model, type of data requested and other various factors before determining a final price for their feed service.

Aside from their raw data collection and distribution, Opta is also pioneering the development of new ways to look at sport through the creation of new metrics to augment the data captured. Their most popular metrics so far has been xG (expected goals), where they provide a value to a specific shot, or group of shots, to determine the likelihood of the shot being scored based on historical data of similar shots. In a similar fashion, they expended xG to create xA (expected assists), to identify the likelihood of a pass becoming an assist in an goalscoring opportunity. However, their most recent developments are sequences and defensive coverage. Sequences refer to the passage of play that takes place from the moment possession is gained to when it is lost, including by a shot on goal. Within sequences, they also look at possession, which is the number of consecutive sequences the team has without losing control of the ball (ie. a shot that ends up in a corner for the attacking team will mean 2 sequences in 1 possession). In terms of defensive coverage, Opta has developed a metric to measure the area of defensive actions by a player during a match.

Opta has established themselves as the leaders in football, rugby and cricket data around the world. Their client portfolio continues to expand and they are now working with major sporting organisations from media to clubs. Some of their most memorable partnerships include Sky Sports, Arsenal FC, Real Madrid, Manchester City, the MLS, BBC Sport and the All Blacks, amongst many others.

An overview of Sportscode, a key video analysis platform for performance analysts

Sporstcode is one of the leading video analysis software in the performance analysis field today, used by thousands of analysts, coaches and athletes around the world. This popular platform goes as far back as 1999, when Australian coaching applications and professional services company Sportstec first launched the first version of the revolutionary video software. However, in 2015, the company was acquired by the American counterparts Hudl in an effort to strengthen the companies position in the industry by combining the elite-level sport market dominance of Sportstec with the broader reach within amateur and grassroots level of Hudl, as stated by former Sportstec Managing Director Philip Jackson (PR Newswire, 2015).

The platform allows analysts and coaches to visually identify what went well and what could be improved in a training session or game by providing a quick and easy way to create interactive reports linked to key highlights. The process is very simple: capture your video into Sportscode, code the different events that take place in the footage, evaluate the results of the relevant events captured and present the insights to coaches and athletes. However, the platform offers a very wide range of functionalities and features that require some time to get familiarized with before being able to effectively use it.

Capturing and Uploading Video

With Sportscode you can capture video live, uploading it into the platform in real-time and from multiple angles when using multiple cameras, to then code the recorded footage by tagging all the relevant events to later analyse. With the most recent version released in 2017, you are even able to capture video from a remote IP camera, allowing the capture of angles that are impossible or impractical to collect through a lift or tripod camera. This is particularly important for sports where teams are spread out across the field, as focusing on a specific area of the pitch may not capture players standing outside the angle of reach.

Coding the video footage

Once the video has been uploaded into the platform, Sportscode provides analysts with enough flexibility within their set of features to define what code windows suit best the KPIs of their team. In this coding process, analyst can define the text, colour, size and alignment of their tags, create filters to play back key moments, define the length and category of each event, execute calculations of the data as it is being coded and more. The analyst will start by defining the codes, flags and labels they will want to track in the video footage and then run the video while indicating which relevant tags apply for each section of the video based on the actions and events that take place.

Analysing the events captured

Aside from tracking the numerous events from a game or training session, Sportscode also allows analysts and coaches to evaluate a particular play of action with interactive visualizations and drawing features. Plays can also be played back from multiple angles, if they were captured in such way, and multiple games can be combined into one unique video if the purpose is to analyse a specific style of playing across various matches. While Sportscode is primarily a video-centric software, it also offers the ability to generate quantitative insights based on the events tracked in the footage and produce spreadsheet style reports. These reports can be shared remotely and in real-time with coaches as the insights are generated to allow for quicker reaction and decision-making.

Presenting and sharing the results

Once the video has been captured and coded, highlights have been generated and the analysis has been completed, Sportscode allows you to export the final video with all the information to share it with coaches and players. This can be done in the form of individual videos or even playlists with groups of videos sorted by different categories. These video presentations can also include notes and commentary on relevant highlights for a more detailed review.

LEARN MORE ABOUT SPORTSCODE HERE

How Wyscout has evolved football scouting

Wyscout initially launched in 2004 in Italy as a Football Match Analysis and Advertising provider, amongst other minor services the company offered. It was not until 2008 when they launched their first user interface to offer access to their footballer database containing basic stats such as weight and height of players. Since then, the platform has experience rapid growth and popularity in the world of football and particular in the scouting field.

By 2012, Wyscout had captured videos and statistics of over 200,000 players around the world and was now actively being used by 300 professional clubs and 15 national sides, as reported by The Guardian newspaper right before the opening of the 2012/13 season's winter transfer window. Wyscout had established themselves in the forefront of worldwide scouting, ending with the most traditional methods historically used where scouts went to view players across the world with a notepad. With a platform like Wyscout, all the information and video footage they needed to know about their next multimillion signing or future youth academy star was as far as the click of a button.

However, as CEO of Wyscout Matteo Camponodico points out, the platform is not intended to replace scouts, as their roles continue to be crucial in shaping the future of clubs. Wyscout simply makes their job better by offering videos of players for them to review before or after they view them live. With the expanding range of functionalities the company continues to add, clubs can now list their transfer-listed players, examine footage of player trials, contact agents to discuss potential offers, view contract duration of players they are interested in signing and much more.

By 2016, SkySports reported that Wyscout had hire a team of 200 analyst collecting data for 1,300 matches a week and the platform had achieved a total of 32,000 professional users. With such a rapidly growing usage and user base, the demands for the data also continue to grow. Clubs asked Wyscout to go deeper into specific areas, to not only track major leagues worldwide but collect statistics in lower divisions too to sport future talent. Today, the company offers data for even semi-professional level players. The growing amount of data collected by Wyscout also increasingly requires smarter analytics to be applied to it. For example, to help digest and compare the wide variety of data offered, Wyscout develops indexing models to allow clubs to compare two team across completely different leagues using similar ratios.

Today, Wyscout is the main platform during transfer windows worldwide. The large majority of transfers in the world of football initiate and often get closed through Wyscout. But the use of the platform has also expanded to track player performance and even journalists are now using it to write articles about particular players. Even players are now making use of Wyscout to track their stats and those of their next opposition.

Matteo Camponodico's plans don't end here. He has an ambitious vision to continue the incredible growth of the platform and we are guaranteed to continue to hear a lot more about this great platform.

Videoanalysis editing software: Coach Paint and KlipDraw

Video editing software plays a key part during review sessions with players and coaches after a match or training session where tactical analysis is discussed in an engaging visual manner. Tools like Coach Paint, or the emerging KlipDraw, are great assets for Performance Analyst when grabbing player and coaches' attentions by visualizing formations, movements on the pitch, tactics and any in-game action that requires analysis.

Some of the key features these software offer are: player cut-out, spotlight, zoom, player tracking, zone tracking, distance measurement, trajectory marking and formation tools. They allow you to import your recorded videos, select the type of graphics and features to apply on them, trim the clips to ensure only highlights and relevant actions are included and export the final video as a standalone video file.

Between Coach Paint and KlipDraw the biggest difference is the license pricing. Coach Paint is a lot more expensive than KlipDraw, with the 'Fundamentals' subscription priced at $100 a month per user. KlipDraw on the other hand only costs $49 for a 6 months subscription, making it a lot more affordable. It is important to note that KlipDraw can only run on a Windows computer.

Both software offer trial periods before purchasing the complete license. It is recommended to try both of them before committing to one to see which one works for your needs.