In recent years, Analytics has influenced the tactics and decisions in professional sports, such as baseball and basketball, but traditionally football (also called soccer in the USA) was not one of the sports that heavily relied on analytics to make decisions, maybe because football was assumed to be unsuited to the analytical approach.
To illustrate this difference with baseball, an interesting case study is the Oakland Athletics, which began to use an analytics approach to baseball by focusing on sabermetric principles. This started when Billy Beane took over as general manager in 1997 and then hired Paul DePodesta as his assistant. Through the statistical analysis done by Beane and DePodesta in the 2002 season, the Oakland Athletics went on to win 20 games in a row. The success of the Oakland Athletics encouraged sports teams around the world to replicate the model pioneered by Billy Beane.
His approaches to baseball soon gained worldwide recognition when Michael Lewis published “Moneyball: The Art of Winning an Unfair Game” in 2003 to detail Beane’s use of Sabermetrics and how Oakland Athletics’s baseball team found a competitive advantage by evaluating players using a different criteria. In 2011, a film based on Lewis’ book also called “Moneyball”, was released to further provide insight into the techniques used in the Oakland Athletics. Sabermetrics is the analysis of baseball statistics that measure in-game activity, by collecting and summarizing the relevant data to answer specific questions. The term is derived from the acronym SABR, which stands for the Society for American Baseball Research, founded in 1971.
Unlike baseball, following conventional wisdom, football seemed apparently impossible to quantify. Much of the game involves moving the ball from player to player while waiting for an opportunity to create a situation to score. But this was proved wrong when Ian Graham, a PhD in physics from Cambridge University, built from scratch his own database to track the progress of more than 100,000 players worldwide, so that he could recommend which of them Liverpool F.C. should acquire, and then how these new players should be part of the strategy of the club. Graham’s main responsibility is helping Liverpool F.C. decide which players to acquire. He does that by feeding detailed data on games into his decision models and, contrary to what we would expect, he does not watch football games in order to create these models, because he thinks it helps to create a negative bias to make appropriate decisions.
Liverpool F.C. results in recent years are the tangible evidence that the strategies were working, being both a runner-up and a champion in the last two seasons of the UEFA Champions League and whatever their future outcomes are, Liverpool’s outstanding results have already started to make data-crunching a fashionable trend, not only in England but beyond. As a result more football clubs contemplate hiring data analysts with no soccer backgrounds to try to replicate this unique success.
Additionally, it is worth noticing that Graham recommended Liverpool F.C. to acquire Egyptian footballer Mohamed Salah in 2017, who in that moment was playing in Italy. That year, Liverpool F.C. paid Roma, an Italian football club, about USD 40 million for Salah. Graham’s data showed that Salah would make a good match to Brazilian player Roberto Firmino, another of Liverpool’s strikers, whose statistics show that he generates more expected goals from his passes than nearly anyone else in his position, and eventually that prediction turned out to be true: during the following season 2017-18, Salah turned those expected goals into actual ones and at the same time broke the Premier League record by scoring 32 times in a season.
Data analysts are now recording data from thousands of actions during games and training sessions. But it is not so much about collecting the data. It is more about making sense of this data. Analytics and big data are driving the strategies of major corporations around the world and these methods are now begin applied into football, from the boardroom to the boot room. Football clubs over the last decade have had to deal with a technological revolution and what that meant is that they now have started to collect lots of data. Sport data is basically a reconstruction of the match. But, why is it useful to collect all this data? The main reason is to have a way to tell a detailed story of how a specific match was played and have the possibility to look at it through various lenses, for example how many passes and shots were made, this is the event data, and if the tracking data is collected as well, for example using wearable GPS tracker vests, we can also see the detailed activity of each player or dots running around the field or heatmap visualizations, so that it is possible to tell the detailed story of a match in a better way, since everything a player does is recorded.