Predicting Sports Scoring Dynamics with Restoration and Anti-Persistence

Professional team sports provide an excellent domain for studying the dynamics of social competitions. These games are constructed with simple, well-defined rules and payoffs that admit a high-dimensional set of possible actions and nontrivial scoring dynamics. The resulting gameplay and efforts to predict its evolution are the object of great interest to both sports professionals and enthusiasts. In this paper, we consider two online prediction problems for team sports: given a partially observed game Who will score next? and ultimately Who will win? We present novel interpretable generative models of within-game scoring that allow for dependence on lead size (restoration) and on the last team to score (anti-persistence). We then apply these models to comprehensive within-game scoring data for four sports leagues over a ten-year period. By assessing these models' relative goodness-of-fit we shed new light on the underlying mechanisms driving the observed scoring dynamics of each sport. Furthermore, in both predictive tasks, the performance of our models consistently outperforms baselines models, and our models make quantitative assessments of the latent team skill, over time.