  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.
