Predicting sports scoring dynamics with restoration and anti-persistence

TL;DR

This paper introduces interpretable generative models for predicting scoring and outcomes in team sports, accounting for lead size and scoring order, and demonstrates their superior predictive performance over baselines.

Contribution

It presents novel models capturing restoration and anti-persistence in scoring, applied to ten years of data across four sports, advancing understanding of scoring dynamics.

Findings

Models outperform baseline predictions.

Models reveal insights into scoring mechanisms.

Quantitative assessment of team skill over time.

Abstract

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.