GoKawiilThe role of advanced analytics in sports is a contentious subject. To its defenders, data-driven pragmatism is a natural evolutionary step in the way we play and watch games. For detractors, the approach prioritizes results above all else and drains the soul from a pursuit that should be spontaneous and joyful.
As someone who is neither pragmatic nor spontaneous, I don’t qualify for either camp, though I find the very notion of applying this kind of research to soccer fascinating and even admirable. The game is resistant to orderly examination by design. Like preparing a tax return for a housecat, it takes a stupendous amount of ingenuity just to figure out which questions to ask, to say nothing of finding the answers.
While baseball can be a spreadsheet task, soccer matches amount to meandering free-verse written in 90-minute chunks. Luke Bornn is a data scientist who specializes in movement studies. Thanks to his background analyzing complex bodies in motion, he realized he was uniquely suited to explore the nature of such an evasive game. While at Los Alamos National Laboratory, Bornn worked on ways to detect how much damage helicopter blades can sustain before it compromises the chopper’s ability to stay airborne. He has mapped climate data to predict crop yield and studied how herds of massive land mammals move about the fruited plain. The ebb and flow of a soccer match, while mysterious, were not altogether unfamiliar, and he has pioneered ways to quantify some of the game’s amorphous spirit.
Along with frequent collaborator Javier Fernández, Bornn has published academic papers with titles like “Wide Open Spaces: A Statistical Technique for Measuring Space Creation in Professional Soccer.” In this study, the data scientists examine the ways players without the ball can manipulate opponents’ positioning on the pitch. Like the stylus of a Magna Doodle dragging metallic particles about the toy’s surface, seemingly uninvolved parties can contort the very geography of their foes to open new avenues of attack.
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Thanks to player tracking technology, this is now a quantifiable skill, and, like so many things, Lionel Messi is great at it. Through their research, Bornn and Fernández found that Messi is perhaps one of the best walkers in all of soccer. The Argentine legend is prone to lollygagging, and common conjecture has been that he’s either conserving energy or just can’t be bothered. While this may be part of it, their study demonstrates that Messi’s slow saunters about the pitch short-circuit defenses in unique ways. “That walking behavior is not a detachment from the match but a conscious action to move through empty spaces of value and claim the control of valuable space,” they write. “Messi does this very effectively, placing him near the top of players in terms of space gained during the whole match, despite the lack of active gain.”
In other words, Messi can achieve more on a stroll than most players do with an all-out sprint.
Ask the people who work deep inside soccer’s analytical engine rooms about how their work affects the way they view the game, and you’ll get some illuminating responses. “I watch in a strange way,” Bornn says. “I tend to watch with an eye toward what the tactical system could be, or whether the data that’s being collected is miscapturing what’s going on, or that the data might capture the core components but our models will miss what’s going on. It has kind of ruined sports for me.”
Sarah Rudd tends to agree. “It’s a little exhausting watching every game so analytically,” she says. “It’s hard to turn off that part of your brain, but you still want to be a fan and you want to enjoy.” Rudd got into soccer analytics so early, she essentially had to invent it from scratch. After graduating from Columbia University, she spent a few years living in Chile, where she fell further in love with her favorite sport. She fondly recalls squinting at her small, standard-definition television set to watch broadcasts of matches from Argentina. “You had to really know the teams,” she says. “If you weren’t really familiar with the teams, you couldn’t figure out who players were. It’s hard to read the numbers, and you couldn’t really see their faces.”
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