The role of a Performance Analyst in Sports

What is the role of a Performance Analyst in Sport?

Performance analysis is the process of assessing performance in a sport to develop an understanding of actions that can inform decision-making, optimize performance and support coaches and players in their journey towards optimal results. In many team sports this would consist on tactical assessment, movement analysis, video and statistical databasing and modeling and coach and player data presentations.

A few years ago, the role of a Performance Analyst simply consisted on recording a training session or game and creating video highlights to provide to managers and players for review. Video recording and editing constituted the large majority of an analyst’s role. Today, the role of Performance Analysts has evolved where analysts now require a lot more expertise in numerous tracking hardware and software that the advances in technology have brought to the industry, allowing for more sophisticated data collection, storage and increased coaching demands for data presentation. With the growing phenomenon of ‘big data’, the large amounts of data collected in the world of sport requires analytical experts to handle, disseminate and generate insights from this data.

Performance Analysts are like any other member in the backroom staff of a sporting team. On game day, analysts require to capture all the actions happening on the pitch to later tag and create video playlists of each player for the following day. They would then have one on one sessions with players led by the management team to present and discuss clips showing mistakes and any positives from the match. Team sessions are also held to show the team video analysis of the game as a group, discussing offensive and defensive formations, tactical analysis and any relevant actions that need consideration by coaches. Players and coaches may also ask for one-off clips and analysis as they find appropriate to review specific areas relevant to them to focus their development and learning.

Through both notational and motion analysis an analyst is able to provide stats and recording to coaching staff about areas such as position of the ball, player movement and involvement, fatigue, work rate, time of a particular action and the outcome of such action. In recent years, tools such as Sportcode, Dartfish or Coach Paint, as well as third-party data and statistics companies such as Opta, allow analysts to retrieve, capture, code and analyse the necessary data point in the most effective way for their reports. These tools and the vast data now available through different sources have allowed analysts to better observe their team’s performance and identify strengths and weaknesses, analyse opposition performance to counteract strengths and exploit weaknesses as well as evaluate effectiveness of training programs in improving match performance.

Within the context of football, most Performance Analyst roles will consist on responsibilities such as:

  • Record matches and training sessions

  • Pre-match team and opposition analysis production

  • Live match-day coding and editing of match footage post-game to produce post match reports

  • Update statistical and video databases for trend analysis

  • Update training databases and logs post each sessionProduce content for classroom sessions (usually for Academy) and team de-briefs

  • Ensure the upkeep of all filming and video capture equipment

  • Analytical ad hoc duties as requested by the team’s Management

  • Delivery of feedback to staff and players

  • Creation of reports on various aspect of performance

  • Interpretation, analysis and dissemination of performance data

As modern-day performance analysis departments grow within clubs and sporting organizations, analyst roles are becoming more and more specialized in a subset of the functions a traditional analyst would have undertaken in the past. Roles such as scouting analyst, tactical analyst, research analyst, technical scout, training analyst or even goalkeeper analyst are emerging positions in the world of performance analysis within a modern sporting organization and while the reflect the importance of such roles in competitive sporting teams, they are also fading the previously clearer definition of the role of a performance analyst and its duties.

Why is a Performance Analysis function important for a team or sporting organisation?

Research has shown that coaches and players, like any other humans, recall fewer than half the important actions and movements that happen on the pitch. Emotions may run high and the more extremely positive or negative events may overshadow other tactically relevant insights that occurred during the game. Collecting match information through video recording helps remove those biases and provide a more objective view of what happened on a game. Performance Analysts collect data from all the events happening on the pitch and create relevant metrics, either through coaches’ requests or by their own assessment, to show players and coaches on what went well and what went wrong.

The basis of coaching consists on assessing athlete performance, identifying areas of improvement, feeding back information to athletes, managing practices to convert the weaknesses into strengths and reassessing performance after a certain period of time or number of practices. With a Performance Analyst by their side, a coach can obtain the right level of information and performance insights in the early stages of the cycle to help them manage team and player development a lot more effectively. The work of a Performance Analyst can surface much sooner improvement areas and strengths of a player and team to allow coaches provide feedback to the team with a deeper level of understanding of how each individual in their team is performing. A good partnership between coaching staff, players and analysts can develop a player to their full potential and make a coach become a better coach through structured training sessions and more informed decision-making.

As technology and analytics advances in today’s society, so it does in the sporting industry. Data availability is growing rapidly with company’s like Opta offering third-party data and statistics on every game and player in major professional sports, allowing more and more teams to obtain access to their opponents recent performances, tactical decision, player profiles and more data points to enable them to generate a competitive advantage over opposition teams. In this new era of data, coaches and teams need analyst to help them navigate through all the information, manage and maintain the team’s databases and use performance analysis software to code the games, edit footages from the camera, extract data from providers like Opta and more technical skills that they would have imagined in the past. Failing to do so could mean that your team’s next rival might know more about your strengths and weaknesses that you might do about theirs. Your club could also fall behind in the advancements of performance optimization other clubs in the same competition are following to succeed.

The outcome of a good performance analysis means a well-defined coaching plan to improve a team’s or individual athlete’s performance. A coach can interpret a report or piece of analysis from its Performance Analyst to make adjustments to the team’s practices and tactical structure depending on the findings discovered. These pieces of analysis are intended to act as a valuable asset to coaches or players to make any decision for the following match, and have now become a strict requirement for any elite sporting organisation.

What skills are required to be a successful performance analyst?

The nature of a Performance Analyst in comparison to any other analyst role in a different industry is the sport element. Sports operate within extremes by default, and this is reflected in the day-to-day life of an analyst. The highs are higher and the lows are lower than many other  analytical positions. Long hours, short turnarounds, last minute requests and high standards and expectations are the norm in a field were everyone in a team, from players to any member of staff, is expected to give 110%.

Having said that, there are certain traits and hard skills a Performance Analyst is required to have to succeed in the field:

Knowledge of the sport. A key difference between a statistician and an analyst is the use of contextual information in order to generate insights. As a Performance Analyst you need to understand what’s important and what’s not in the sport you are analyzing, not only for the team to be successful but also for the particular coaching style of the team management. What is the coach aiming to get out his players? Tactical awareness, players, other coaches, club philosophies and history can help the Performance Analyst successfully contribute to the team’s success.

Building relationships with coaches. An analyst is part of the backroom staff of a team. This means that, like with any team, having a good relationship with coaches and players is crucial to gaining their trust and receiving credit for the work done, or even being heard. It may take up to 4 years for an analyst to appropriately settle in an elite professional team. An analyst needs to perfectly understand what the coaches want at every time and be able to accurately give them insights and information in a timely manner. Most of today’s analyst work consist on supplying whatever information the coaching staff is requesting for.

Effectively reacting to feedback. In line with building relationships with coaches is the ability to adapt the work produced to the needs, or even tastes, of the coaching staff and players. An analyst might spend hours immerse in in-depth data analysis and may produce very detailed pieces of analysis. However, coaches and other staff need insights in an understandable and useable way that is easy for them to apply during planning and decision-making.

Data consciousness. With numerous sources of data available, whether is from a third-party or collected internally, an analyst needs to be able to identify which data points are useful and which ones are redundant for every piece of analysis or report being produce. They also need to be able to assess the accuracy and reliability of such data by having an advanced level of knowledge of how the data used has been collected, stored or retrieved. Mishandling data sets may lead to inaccurate reports being produce that can mislead coaches and players.

Presenting your analysis reports. Depending on the club’s philosophy or coaches’ trust, analysts are required to provide a walkthrough explanation of their findings to coaches, players or team manager. Being able to clearly articulate the finding to a coach can give an opportunity for analysts to generate trust and establish themselves within the coaching team. Being a good communicator is essential for an analyst to demonstrate their work to players and coaches.

Analytical hard skills. Needless to say an analyst needs to be equip with enough technical knowledge on various analytics software and programming language, from basics like Excel to more advanced SQL, R or Python coding. Maths, IT and research and analytical skills to produce and understand complex data is essential Data visualization is also a key part in the role therefore proficiency in tools like Tableau are crucial to present findings in a easy-to-understand manner.

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.

What are Expected Goals (xG)?

What are Expected Goals (xG)?

Expected Goals, or xG, are the number of goals a player or team should have scored when considering the number and type of chances they had in a match. It is a way of using statistics to provide an objective view to common commentaries such as: ”He shouldn't miss that!” "He's got to score those chances!" "He should have had a hat-trick!”

Goals in football are rare events, with just over 2.5 goals scored on average per game. Therefore, the historical number of goals does not provide a large enough sample to predict the outcome of a match. This means that shots on target and total number of shots are now being used as the next closest stats to predict number of goals. However, not all shots have the same likelihood of ending up in the back of the net.

This is where xG comes into play. Expected Goals uses various characteristics of the shots being taken together with historical data of such types of shots to predict the likelihood of a specific shot being scored. Since xG is simply an averaged probability of a shot being scored, a team or player may outperform or underperform their xG value. This means that they could be scoring chances that the average player would miss or that they could be missing chances that are often scored.

xG is often used to analyse various scenarios:

  • To predict the score of an upcoming match using historical data of the teams involved.

  • Assess a team’s or player’s “true” performance on a match or season, regardless of their short-term form or one-off actions on a pitch. It provides a data point on the number and quality of chances being created regardless of the final result.

  • Identify performing players in underperforming teams, or those who receive less playing minutes, by assessing which ones are more effective than the quality of their chances they receive would suggest.

  • Understand the defensive performance of a team by assessing how effectively are they preventing the opponent team from scoring their chances.

Origin of the ExpectedGoals Model

In April 2012, Advanced Data Analyst Sam Green from sport statistics company Opta first explained his innovative approach to assessing the performance of Premier League goalscorers, inspired by similar models being used in American sports. However, it was not until the beginning of the 2017/18 season when BBC’s Match of The Day debut their use of xG by their popular football pundits to make xG a focal topic of conversation by many football fans. 

Over the years, Opta has collected numerous data points of in-game actions in all of the top football leagues. When creating the xG model, Sam Green and the Opta team analysed more than 300,000 shots and a number of different variables using Opta’s on-ball event data, such as angle of the shot, assist type, shot location, the in-game situation, the proximity of opposition defenders and distance from goal. They were then able to assign an xG value, usually as a percentage, to every goal attempt and determine how good a particular type of chance is. As new matches are played new data is collected to continuously refine the xG model.

There is no one specific model to calculate xG. When looking at xG it is important to consider that the xG value would depend on the factors that the analyst creating the xG model wants to incorporate in the calculations. Since its release to the public, the xG theory raised considerable attention in the analytics community, with many enthusiasts working and adjusting the model in their own ways in an attempt to perfect it. This means there are now several different xG models out there, each of them considering different factors. Some would consider whether it was a goal scored with their feet or with their head, other consider the situation that led to the shot and so on, but the final prediction each model outputs have shown to only vary slightly across different models.

How is xG calculated?

Opta’s xG model is based on the fact that the most basic requirement to score goals is to take shots. However, not all strikers score goals from the same number of shots. As Sam Green identified, in the 2011/12 season Van Persie only needed 5.4 shots to score a goal, while Luis Suarez took 13.8 shots for each goal he scored. However, they both shot the same number of times per game they played.

Cross

This is why Opta decided to look deeper into the quality of chances each striker received by adding the average location from which each shots was taken. However, they soon realized that location on its own was not enough. A penalty spot chance could come from a penalty kick, a header from a corner or a 1 on 1 against the goalkeeper, each with a very different likelihood of ending up in a goal. That is why Opta decided to incorporate additional data points to the model. Unfortunately, the exact model with all the factors considered by Opta has not been made public but a number of analyst have attempted to replicate or improve the model since its first release.

The xG model was designed to return an xG value for each player, team or chance depending on the dimension that the data is being analysed in: a full season, a particular match, a specific half in a game or group of goal attempts. Let’s say a player like Harry Kane takes 100 shots from chances that, based on historical Premier League data, have a probability of being scored of 0.202 (or 20.2%). Kane's xG value would be 20 expected goals scored (100 shots x 0.202). This xG number would contain an average of some ‘big scoring chances’ Kane took, such as penalties with 0.783xG, other non-penalty shots inside the box with varying xG values such as 0.387xG and maybe even shots outside the box with an 0.036xG value. The models attempts to balance the number of shots a player takes with the quality of these chances. For example, a player may get himself into very dangerous attacking positions inside the box in 23 occasions with high xG value and score the same number of goals than a player that continuously tries his luck from outside the box with 81 shots attempts that have a lower xG value.

Once an xG value has been calculated, a player or team’s performance can be evaluated on whether they are over or under-performing such value. In the above example, Harry Kane may actually score 25 goals during the full season, 5 goals above his 20 xG value, suggesting that his ability of converting chances is above-average and he can find the net in difficult scoring situations. Similarly, a player with a 20 xG value who has scored 15 goals suggests that he is missing chances that he probably should have scored.

Goalkeeper

Opta took xG a step further and assessed the impact the player had to a specific chance using their shot quality. They did so by factoring into the xG calculation the propensity to hit the target a shot taken by the player has and then comparing the former xG(Overall) value against this new xG(On Target) one. Their analysis showed that at the time Van der Vaart’s shooting saw his xG increase from 6.9xG to 10.3xG(On Target), suggesting that the type of shots he took were of higher quality than the average when xG was calculated before he took the shot. xG(OT) when compared to actual goals may also indicate how much a player was affected by the quality of goalkeeping he had to face. In the same season, Mikel Arteta scored 7 goals with just 3.5xG(OT) suggesting he got ‘luckier’ in front of goal as his shooting quality should have only given him just over 3 goals.

xG(OT) can be used to assess goalkeeping quality when used in reverse. Since it only takes into consideration shots on target, a keeper’s participation in these sort of chances is crucial to the final outcome of the play. De Gea conceding 22 goals with an 27xG(OT) suggests that he has blocked goals in situation were they are normally conceded.

Why are Expected Goals important in today's football?

Luck and randomness influences results in football more often than any other sports. We have all seem teams being dominated throughout a match and manage to score a last minute winning goal while having a lower number of chances than their opposition. But how sustainable is that? We have also seen world class strikers become out-of-form and spend a few games without seeing the back of the net. Is the player not taking advantage of the chances being provided by his teammates? xG allows us to assess the process over the results of a match, or performance of a player or team, by rating the quality of chances instead of the actual outcome.

football-1274661_1280.jpg

The most used example to explain xG’s efficiency is the Juventus season of 2015/16. Juventus only won 3 out of their first 10 games but the difference between their actual goals and xG was considerably high. This meant that the had the chances but were not converting them, suggesting that their negative run of results might not last if they just get a bit luckier in front of goal. Sacking manager Massimo Allegri could have been a mistake, since after match day 12 their luck changed and ended up winning the league title with 9 games spare.

xG gives us a more accurate way of predicting match outcomes than by simply using individual stats. In the Premier League, only 71.6% of teams that had the most shots won the fixture, while close to 81% of teams that obtain a higher xG score win games. It eliminates historical assumptions that popular tradition in football has created and provides a statistically relevant point of argument to whether the performance of a player or team is above or below the average given a number of historical data points.

garry-mendes-rodrigues-2846045_1280.jpg

When using expected goals to see which players are hitting the target more or less than the numbers suggest they should, teams can scout promising prolific goalscorers if they consistently score more goals than the quality of chances they get. On the other hand if a player surpasses his expected goals for a few games but has no history of doing so in the past, it might come down to his form and luck rather than goalscoring talent, and he might struggle to sustain that over a long period of time.

Limitations of the Expected Goals model

The xG model is only as good as the factors being input into its calculations. These data inputs are limited by the data we possess today from companies such as Opta. Other factors, such as shot power, curl or dip on the shot or whether the goalkeeper is unsighted or off balance might not be considered in most xG models out there. Due to model being based on averages, the random nature of a football match and the rarity of goals in the sport makes it almost impossible to consider with enough statistical significance all historical factors that can cause a goal to be scored. xG should be used as indicative and supportive information for decision making purposes and generating opinions rather than a finite answer to the performance of a team or player.

As the model’s creator Sam Green puts it: “a system like this will also fail to predict a high scoring game. Since it is based on averages and with around half of matches featuring fewer than 2.5 goals, this is to be expected”. We also need to consider that a shot taken by a Manchester United striker should have a higher xG than one taken by a Stoke City player, suggesting that on average Man Utd would outperform their xG on a chance by chance basis while Stoke City would underperform it if the xG is calculated using averages from all English teams' shot history.

Criticism and the Future of xG models

The recent misuse of Expected Goals as a analysis metric during pundit commentary has encouraged numerous criticism. A team may score one or two difficult chances early in a game and sit back for the remaining of the 90 minutes, allowing their opponents to take many shots from different positions, thus increasing the opponents xG. One could then claim that the losing team achieved a higher xG therefore deserves the win. This is why xG should always be taken with additional context of the game before creating a verdict. Statistics can just tell us what happened in a game but a wider view is necessary to show you how it happened and give you a clearer idea on what’s yet to come. Certain in-game actions by players cannot be measured with a statistical model today, such as the ability of a defender in getting in front of a shot attempt despite never touching the ball.

There is also a strong resistance from the football community to the use of data. Football is a traditional and emotional sport by nature, with experience and accepted wisdom dominating people’s opinions. Most fans see the use of statistics as intrusive and challenging their popular and historic knowledge of “the beautiful game”. After experiencing their team lose, most of them are not interested in listening to television pundits discuss how their team performed against their expected goals. Despite analytics having plenty to offer to football performance analysis, there are still doubters. xG’s debut in Match of the Day shaked social media with instant mentions of “stat nerds” and claims that the numbers in football are “pointless” and “bollocks”. However, it has been made clear by Opta that xG is not intended to ever replace scouts and pundits but simply aid them in their analysis of a game.

Despite all this resistance and criticism by some pundits and football fans to accept this new era of football analysis, Opta and various sport analysts continue to evolve the use of statistics to analyse performance in numerous areas in football. Models such as xG are the first round of statistical systems and will soon be followed by upcoming ones such as Defensive Coverage, which will assess tackles, blocks, interceptions, man-marking and clearances. Football’s data revolution has started and will continue to see developments every season.