Big Data Finds What Football, Basketball and Hockey Have in Common

Sectors | Sports   |   
Published January 11, 2014   |   
Ricky Ribeiro

What do basketball, football and hockey have in common? On the surface, not very much. After all, one sport is played on ice and the other two are played on either a grassy field or a wooden court.

But according to University of Colorado computer science professor Aaron Clauset, when the games are analyzed from a Big Data perspective, patterns and similarities between the sports begin to emerge.

“These games look a lot less complicated than most people think,” Clauset says in a recent Slate article.

As a Big Data superstar, Clauset’s opinion carries weight, and his recent paper, “Scoring dynamics across professional team sports: tempo, balance and predictability,” which he submitted to the Journal of Quantitative Analysis in Sports in October 2013, reveals the game mechanics at work in each sport.

“[In all three sports], events occur randomly (a Poisson process). Which team wins the points is coin flip (a Bernoulli process) that depends on the relative skill difference of the teams on the field,” Clauset and his co-author, Sears Merritt, write.

Clauset and Merritt also do a bit of myth busting as they tackle the popular concept of teams gaining momentum in a game.

“[G]ameplay is largely [a] sequence of roughly independent, short-term optimizations aimed at maximizing near-term scoring rates, with little multi-play strategic efforts and few downstream consequences for mistakes or miscalculations,” he writes.

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