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Despite growing interest in quantifying and modeling the scoring dynamicswithin professional sports games, relative little is known about what patternsor principles, if any, cut across different sports. Using a comprehensive dataset of scoring events in nearly a dozen consecutive seasons of college andprofessional (American) football, professional hockey, and professionalbasketball, we identify several common patterns in scoring dynamics. Acrossthese sports, scoring tempo---when scoring events occur---closely follows acommon Poisson process, with a sport-specific rate. Similarly, scoringbalance---how often a team wins an event---follows a common Bernoulli process,with a parameter that effectively varies with the size of the lead. Combiningthese processes within a generative model of gameplay, we find they bothreproduce the observed dynamics in all four sports and accurately predict gameoutcomes. These results demonstrate common dynamical patterns underlyingwithin-game scoring dynamics across professional team sports, and suggestspecific mechanisms for driving them. We close with a brief discussion of theimplications of our results for several popular hypotheses about sportsdynamics.

Scoring dynamics across professional team sports: tempo, balance and predictability