  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.
