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Former Arsenal analytics head Sarah Rudd and data scientist Luke Bornn have applied probability theory and movement studies to soccer. Both acknowledge that not every element of the game can be quantified. Research has quantified space creation and the value of player movement including Lionel Messi's walking patterns.
WiredThe role of advanced analytics in soccer remains a developing field as researchers work to quantify elements of a sport that often resists orderly statistical examination. Sarah Rudd, who once ran analytics for Arsenal, made her name applying the tenets of probability theory to movements on the pitch. Even she admits not everything can be solved with data.
Luke Bornn is a data scientist who specializes in movement studies. While at Los Alamos National Laboratory, he worked on ways to detect how much damage helicopter blades can sustain before it compromises the chopper’s ability to stay airborne. He has also mapped climate data to predict crop yield and studied how herds of massive land mammals move about the fruited plain.
Bornn realized his background analyzing complex bodies in motion suited him to explore soccer. ” The study examines the ways players without the ball can manipulate opponents’ positioning on the pitch. Player tracking technology has made this a quantifiable skill.
Research by Bornn and Fernández found that Lionel Messi is one of the most effective at this. Messi is prone to lollygagging, and common conjecture has been that he is either conserving energy or just can’t be bothered. Their study demonstrates that Messi’s slow saunters 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. “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. ” Sarah Rudd tends to agree. “It’s a little exhausting watching every game so analytically,” she says.
After graduating from Columbia University, she spent a few years living in Chile where she fell further in love with the sport. She 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. ” Rudd and her boyfriend at the time invented a game based on this challenge involving spotting Carlos Tevez by his running style when Boca Juniors played.
She took a gig doing data mining and machine learning for Microsoft in Seattle but continued to search for entry points into the sports industry. In 2011 she entered a research competition held by sports analytics company StatDNA. Using a spreadsheet of rudimentary player-location data, Rudd set out to devise a method for analyzing an individual’s performance in more complex ways than simple goals and assists.
She used Markov chains, a statistical tool first introduced in 1906 that helps determine the likelihood of something happening within a system based on its current state. The chains represent a departure from the principle of absolute independence seen in things like roulette wheels. The events are linked one to the next and they form a Markov chain.
Rudd applied this approach to evaluate how much value players add with every action.
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