This informal CPD article Why Has Football Data Become So Important? was provided by AnalyiSport, cutting-edge courses in football analysis created by experts working in the world’s top leagues.
Why Has Football Data Become So Important?
Football data is now everywhere in the sport, from the touchline to the TV studio. The expected goals stat (xG), for example, is used by performance analysts working behind the scenes at clubs like Liverpool, Man City and Arsenal, by companies including AnalyiSport, Wyscout and Stats Perform, and by broadcasters such as Sky Sports, BT Sports and the BBC. Unlike traditional stats, such as the number of shots on target, xG is a sophisticated metric that combines various factors to calculate the quality of a chance to score. Modern football data isn’t just about collecting numbers, it’s a way of better understanding the game.
Clubs use data in a variety of different, but related, ways. One of the major ways they use it is opposition analysis. A club’s opposition analyst will study data about the team’s up-coming opponents and watch video footage to identify their main strengths and weaknesses. They’ll focus particularly on times when the opponent has played teams that use a similar tactic to the analyst’s team, looking at how they function as a unit with and without the ball, and the roles that individual players perform. The opposition analyst will then present their key findings to the coaching staff and may even be invited to give a presentation to the players, preparing the team for the challenge they face.
Another way clubs use data is in the player recruitment process. A recruitment analyst will search through the large databases of player statistics created by companies such as Statsbomb and Wyscout, trying to discover potential transfer recruits. To find the right player, the analyst will create models of what an ideal player for their team would look like in each position, focusing on the abilities which the head coach values. They will also make adjustments for the relative strength or weakness of the league the player plays in, because a striker with great stats in the third tier of Scottish football is unlikely to perform as well in the top tier of German football.
Searching through a database is much quicker than sending out scouts to watch games and it can identify players who might otherwise be overlooked. It doesn’t replace traditional scouting though, as any player found in the data will then be watched to make sure they’re the right fit.
Data analysis has thrived in American sports, but it has taken time for it to become common in football. In the late 1990s and early 2000s, the odd coach, such as Sam Allardyce and Steve McClaren, showed an interest in it, but it’s really only been the last decade that has seen data analysis take off. One reason for this was the takeover of Liverpool by the Fenway Sports Group, whose co-founder John W. Henry had seen the success of data analysis first-hand in baseball as owner of the Boston Red Sox.
Liverpool have built an impressive analysis department which includes the likes of Will Spearman, who has a PhD from Harvard and worked at CERN. Man City have also invested heavily in data science. Further down the football ladder, most smaller clubs now have at least one or two analysts.
As well as opposition analysis and recruitment, clubs use data to study the performance levels of their own players in training and matches. Even academies now regularly use data to aid in player development. What links all these uses of data is the desire to analyze performance, whether the performance of an individual or a team. The data, when interpreted well, provides a way of seeing in much more detail, and with much greater clarity, exactly what is happening on the football pitch.
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