2013/10/16 by Sears Merritt, Aaron Clauset · 1 voice
Computer Science · Mathematics · Physics and Astronomy · #cs.CY #physics.data-an #physics.soc-ph #stat.AP
paper · pdf · doi:10.1140/epjds29
published as EPJ Data Science 3, 4 (2014) · 18 pages, 8 figures, 4 tables, 2 appendices
arxiv published 2013/10/16 · arxiv created 2014/03/20 · arxiv updated 2014/03/20
Despite growing interest in quantifying and modeling the scoring dynamics within professional sports games, relative little is known about what patterns or principles, if any, cut across different sports. Using a comprehensive data set of scoring events in nearly a dozen consecutive seasons of college and professional (American) football, professional hockey, and professional basketball, we identify several common patterns in scoring dynamics. Across these sports, scoring tempo---when scoring events occur---closely follows a common Poisson process, with a sport-specific rate. Similarly, scoring balance---how often a team wins an event---follows a common Bernoulli process, with a parameter that effectively varies with the size of the lead. Combining these processes within a generative model of gameplay, we find they both reproduce the observed dynamics in all four sports and accurately predict game outcomes. These results demonstrate common dynamical patterns underlying within-game scoring dynamics across professional team sports, and suggest specific mechanisms for driving them. We close with a brief discussion of the implications of our results for several popular hypotheses about sports dynamics.