Football is the most popular sport in the world, with a global audience of 3.5 billion, 20 million registered football players and more than 500 professional football clubs. Like any such huge creature, its health involves gathering vast quantities of data. Football data analytics has given sports analysts access to a variety of new statistics and metrics, which has led to significant developments in performance analysis and player scouting. It’s also essential to uncover trends in team dynamics and leadership styles on-the-fly. That way we can understand why things are happening and improve performance. In line with the widespread data-driven culture of today, this article will explore some of the ways analytics can be applied.
What is Data Analytics in Football?
Data analytics in football refers to the use of data to analyze performance and drive decision-making. In today’s world of analytics, coaches, managers, and players have access to more data than ever before. This data can be used to gain a better understanding of in-game behaviors, create more accurate assessments of performance, and drive decision-making based on real numbers. The importance of analytics in football is growing due to the increased availability and accessibility of this data. Stats can now be used to provide context for in-game performance and help identify potential opportunities for improvement.
Every aspect of football has undergone a change with the increase in data collection at various levels. Even before your football player enters the adult professional leagues, data is now being used to look for new additions to existing teams. Scouting has undergone a transformation. For example, the US Soccer Academy has captured over 2,000 boys and girls' football matches at the youth level. This gives them the ability to analyze how the game is played by this section of players. This information is fed to scouts and clubs looking for promising new players, increasing scouting efficiency, and further helping them inform their decisions when it comes to player development.
Data-Based Player Performance
One of the most important aspects of football coaching is accurate player performance assessment. After all, if you don’t understand how well your players are performing, you won’t know who to select for a given game or how to best improve their performance. There are a few key statistics that are closely linked to performance analysis in football. These include:
- Shots on goal (SOG)
- Shots on target (SOT)
- Shots blocked (SBLK)
- Corners (COR)
- Fouls committed (FC)
- Corner accuracy (CA) and more
These are some of the most important statistics in football, as they can give a more detailed picture of how a game was played. For example, the Expected Goal metric, shortened to xG, is a probability telling us the likelihood of a player scoring. Shots higher up on the xG scale should be taken more often because they have more power to convert into goals. When evaluating this metric, certain aspects need to be considered such as where the player would take the shot from, the distance from the goal, the angle that the ball will need to travel, the number of defenders on the player’s path to the goal, the position of the goalkeeper and previous performance of the player in question and more.
When it comes to how coaches integrate data analytics into their process, Ross Moses says, “Different coaches want different things. Some want complexity reduced to a minimum, but others are more engaged in the methodology behind it and also like to think in this way.” Despite recent focus on the analytical approach to analyzing the game, traditional scouting hasn’t been neglected. In fact, the two work together in harmony to provide game-related decision-making that is optimized holistically.
Data science is now a key part of the way Liverpool manager, Jürgen Klopp, approaches games. Klopp is a huge proponent of this new method of playing football, which combines science and sport, called pitch control. This fall under the idea that making the correct, data-driven decisions will enable the squad to make the most of the pitch.
A large group of statisticians and experts work behind the scenes to crunch the statistics so that Liverpool can present its best performance. In order to assess in real-time how each move on the pitch affects the likelihood of scoring a goal, they have combined event data and tracking data. These two branches of football data have existed for a very long time. The first is event data, which records each action that takes place on the field, including passes, shots, interceptions, and tackles. The other type of data is tracking data, which records every player's location on the field at a pace of 25 frames per second. By merging these two types of data, we can now gain contextualized event data which solves the problem of only tracking on-ball interactions while ignoring how the rest of the actions on field affect play.
Data-Based Player Development
Player development is another critical part of the football coaching process. It is the process by which players learn to be better on the field and improve their skill sets through various training sessions. With data analytics, coaches can create more accurate player development plans. This can be done by taking a look at things like the player’s average shooting distance, the number of shots they take per game, the accuracy of those shots, etc. With this data, coaches can create more targeted training plans that help their players improve in the areas they need to. At the professional level, data analytics has allowed individuals to create shooting formulas. This can be done regardless of position using different metrics like time-in-range, time-between-attempts, field goal percentage when in half court and more. These formulas help players improve their scoring efficiency and shot selection which leads to more wins.
Chelsea FC, makes use of data scientists who employ their expertise to improve individual and team performance both in practice and in games. The club most recently revealed that they make a unique report for each player in the previous game that includes total distance traveled, peak speed, number of sprints, etc.
Data-Based Match Day Strategies
On match day, coaches decide how to best manage their players based on a variety of factors. Weather conditions, travel distance, injuries, etc. all come into play when a coach is making these critical decisions. As data analytics has become more popular in the sport, teams have begun to analyze more factors when making these critical match day decisions. Some of the data that is commonly analyzed for match day strategies includes: distance travelled between the home and visiting team, previous playing surface from the previous match, previous weather conditions, previous opponents and their strength and weaknesses and more. Knowing this data can help coaches make more informed decisions about when to substitute players, what playing formation to use at any given time, and who is best suited to deal with any given situation.
The way fans experience football has also changed, and the sports experience continues to be more and more digitalized. Fans have plenty of interesting statistics and charts about their favorite teams and players to look at and many even spend their time crunching numbers to predict the outcomes of matches and tournaments. From match coverage to stadium operations, analytics and AI have made the process easier, faster, and more convenient.
LaLiga, Spain’s premier football association, announced in 2021, their partnership with Microsoft where they aimed to work on enhancing the football experience and to collaborate on technologies related to media and entertainment. This partnership would cover three broad areas: fans, commercial partners and broadcasters, and venue managers. They would use Microsoft Azure and Power BI to come up with interactive statistics and graphs on match information. The goal for Laliga in this move is to “package its full range of existing technologies into a commercial offering for the sports industry”.
As you can see, there are a number of ways that data analytics can be used to help drive better decision-making in football. From coaching to scouting to match-day strategies, data analytics has helped shape the modern game in many ways. One thing is certain: thanks to data analytics, decision-makers in the world of football now have more access to data than ever before. This data can be used to create more accurate player performance assessments, create more targeted player development plans, and make more informed match-day decisions.
It's crucial for sports organizations to keep up with the pace of play and data analytics is the most effective way to do so. The draft occurs on a set day, games occur almost every day during the season, and players are traded frequently. Analysis must be completed in a timely fashion in order to build this synthesis and to make decisions. These organizations also realize that any benefits they discover are likely to be short-lived, so they must find them, determine how to exploit them, disseminate the information, and move on to the next opportunity as quickly as possible. Data analytics steps in to aid sports organizations through this process and keep everything moving efficiently.