The scowl on Cristiano Ronaldo’s face made international headlines last month when the Portuguese superstar was pulled from a match between Manchester United and Newcastle with 18 minutes left to play. But he’s not alone in his sentiment. Few footballers agree with a manager’s decision to substitute them in favour of a fresh replacement.
During the upcoming football World Cup tournament in Qatar, players will have a more evidence-based way to argue for time on the pitch. Within minutes of the final whistle, tournament organizers will send each player a detailed breakdown of their performance. Strikers will be able to show how often they made a run and were ignored. Defenders will have data on how much they hassled and harried the opposing team when it had possession.
It’s the latest incursion of numbers into the beautiful game. Data analysis now helps to steer everything from player transfers and the intensity of training, to targeting opponents and recommending the best direction to kick the ball at any point on the pitch.
Meanwhile, footballers face the kind of data scrutiny more often associated with an astronaut. Wearable vests and straps can now sense motion, track position with GPS and count the number of shots taken with each foot. Cameras at multiple angles capture everything from headers won to how long players keep the ball. And to make sense of this information, most elite football teams now employ data analysts, including mathematicians, data scientists and physicists plucked from top companies and labs such as computing giant Microsoft and CERN, Europe’s particle-physics laboratory near Geneva, Switzerland.
In return, insights from analysts are altering how the game is played: strikers shoot less frequently from a distance, wingers pass to a teammate rather than cross the ball and coaches obsess about winning possession high up the pitch — tactical shifts all backed up with hard evidence to support a coach’s intuition.
“Big data has ushered in a new era of football,” says Daniel Memmert, a sports scientist at the German Sport University Cologne. “It has changed the philosophy and behaviour of teams, how they analyse opponents and the way they develop talent and scout players.”
Covering all bases
One of the best known cases of how data is changing sports comes from a different game. In his 2003 book Moneyball, Michael Lewis detailed how Oakland Athletics’ manager Billy Beane relied on player statistics to deliver a winning baseball team on a shoestring budget in 2002. Beane recruited players on the basis of detailed data about their performance, including previously undervalued measurements, such as how often a batter made it to base.
Beane had an advantage over those trying to repeat the trick in football. “Football is much more complex than baseball,” Memmert says. Baseball is a natural stop–start game in which only one team at a time is trying to score, and baseball statistics had been collected routinely and studied on a large scale for decades. By contrast, football is a fluid and low-scoring ‘invasion’ game (one in which territory is regularly gained and surrendered), and it’s much harder to record who does what and how it affects the outcome. For decades, football statisticians tended to focus on goals scored and conceded, and to find a way to model them to make predictions.
Variants of this method are still used today to predict the outcomes of matches. A mathematical model that assumes goals scored and conceded are distributed around a mean value, developed by epidemiologists at the University of Oxford, UK, correctly predicted that Italy would beat England in the Euro 2020 international tournament. It also correctly called six of the eight quarter-finalists1.
Such success is not unusual. Statistical match predictions are more accurate than many people realize, says Matthew Penn, a PhD student at Oxford, who developed the Euro 2020 model.
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