Inside soccer’s data renaissance

Imagine tuning in to the opening kickoff of a World Cup match and seeing a player intentionally send the ball all the way down the pitch and right out of bounds on the opponent’s end. Casual fans might scratch their heads. Where’s the logic in surrendering possession seconds into a game? If you were Jesse Davis, though, you’d know that this play could be a prime setup to score. Davis is a professor of computer science at KU Leuven in Belgium and head of its Sports Analytics Lab , which has been at the vanguard of a data awakening in soccer since its inception more than a decade ago. Though the research group brings machine-­learning models to bear on a variety of sports—including basketball, volleyball, and field hockey—nowhere is its impact felt more than on the soccer pitch. Davis and his team of researchers employ advanced data analytics to reveal a range of (beg your pardon) game-changing findings that are shifting pro clubs’ decision-making. “His lab is the most influential sports analytics lab in soccer,” says Hugo Rios-Neto, data recruitment lead for Royal Sporting Club Anderlecht in Belgium. They’ve helped teams better evaluate their rosters, conceived ways to assess how efficient (or not) strategies are, and developed algorithms that uncover hidden tactical patterns. Like, for instance, the value of kicking the ball out of bounds close to the goal and letting your opponent throw it back into play—a move that’s been popping up in some of the world’s top leagues over the last few years. To make the statistical argument for this seemingly counterproductive move, Davis’s group built a training data set composed of more than 1.4 million passes and some 60,000 throw-ins—partly from the 2022 World Cup. They used tree ensemble models (essentially a mashup of decision trees) to simulate the tactic. The conclusion, which the researchers presented in a 2024 paper under the apt title “ Boot it ”: When the ball is in the middle third of the pitch, kicking it out of bounds on your opponents’ side of the field can put you within 10 actions (think passes and dribbles) of a goal. That can be a big deal in a game that has 1,500 or more actions per match and very little scoring. The idea, Davis explains, is that you’re setting yourself up to recover the ball in an advantageous situation. Beyond providing discrete game-day insights, Davis also occupies a unique niche in the world of sports analytics, where many clubs now hire their own internal data teams to maintain a competitive edge. He makes most of his research freely available via open-source analytics tools, but the academic life also affords him the freedom to tackle more complex problems—like standardizing in-game data, a project that will make it easier to parse game footage and come up with winning strategies. Davis, 45, grew up in Wisconsin and spent his childhood enraptured by basketball and (American) football. Soccer was largely a nonentity to him until college, when the 2002 World Cup—in which Brazil famously swept the tournament—reeled him in. But the notion of going on to dissect the sport never crossed his mind. His doctoral studies in computer science at the University of Wisconsin–Madison had him working with radiologists to analyze mammography reports. In October 2010, he joined KU Leuven as a computer science professor looking at the intersection of AI and health care, with a focus on monitoring athletic performance. His research team studied, for instance, combining things like heart rate with other metrics to determine whether someone was overtraining. They also dove into the biomechanics of running. The tactical and technical aspects of sports, and soccer specifically, became the subject of Davis’s professorial work when he hired Jan Van Haaren, an engineering student focused on artificial intelligence and a self-described soccer fanatic. He wondered if data analysis could be used to study things like passing, shooting, and ball progression—metrics the game was only just beginning to digitally crunch at the time. Davis realized that machine learning and other artificial-intelligence tools lent themselves well to the complexity, fluidity, and speed of soccer. You need not be well versed in the moneyball-ization of pro sports to see that it’s relatively easy to apply deep statistical work to baseball or basketball. You can isolate actions like jump shots and assign value to ones taken close or far away. Soon a basketball coach realizes that a player who can’t make a layup, but shoots roughly as well from the three-point line as on mid-range jumpers, might as well go for the shot that gets more points. Soccer, by comparison, seemed like a poor candidate for that kind of analysis. “The vast, vast majority of actions really don’t lead to the outcome of a goal or even a shot,” says Rios-Neto. “So it’s hard to elaborate or derive a winning strategy fr