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Deep Learning Is About to Revolutionize Sports Analytics. Here’s How

Imagine being able to model your opponents’ defense to optimize your attack in soccer, or to determine the best placement of players for grabbing rebounds in basketball.


These are some of the predictive systems that data scientists are just now starting to build, and they’re using deep learning techniques to do it.

Statistics has always played a role in sports, and we’re seen them become even more prevalent lately, with measures like wins above replacement (WAR) in baseball and expected point value (EPV) in basketball and similar measures in soccer and hockey. But today, the most interesting work in sports analytics is happening around the optimization of player positioning for team sports.

Much of this player movement data is collected by a Chicago-based company called STATS (Sports Team Analysis and Tracking Systems). Along with the Elias Sports Bureau, STATS is about as trusted a name in sports data as you can get. If you ask Google or Siri for the latest score in the Padres-Nationals game, those services will get that data from STATS.

Five years ago, STATS installed a series of cameras in each NBA arena, as well as a series of soccer stadiums in Europe. The cameras track the movement of each player and the ball as part of the firm’s SportsVU system, at a frequency of 25 frames per second.

With thousands of games per year, that data quickly adds up and turns into a big data problem. The responsibility for making sense of this mass of geospatial and time-series data falls to STATS chief data Patrick Lucey and his team of PhDs.

As Lucey explains, turning this raw data into actionable insights is not easy, and is something that STATS has invested considerable resources into solving.

“How do we make sense of all that data is a fundamental machine learning problem,” Lucey tells Datanami. “You have box scores in baseball and it’s really nicely segmented. But for continuous sports like basketball and soccer, how can we contextualize that data and ask very specific questions and get answers in understanding team play? We’re very good at doing that.”

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