A Continuous-Time Stochastic Process for High-Resolution Network Data in Sports

Technological advances have paved the way for collecting high-resolution tracking data in basketball, football, and other team-based sports. Such data consist of interactions among players of competing teams indexed by space and time. High-resolution tracking data on interactions among players are vital to understanding and predicting the performance of teams, because the performance of a team is more than the sum of the strengths of its individual players. We introduce a continuous-time stochastic process as a model of interactions among players of competing teams indexed by space and time, discuss properties of the continuous-time stochastic process, and learn the stochastic process from high-resolution tracking data by pursuing a Bayesian approach. We present an application to Juventus Turin, Inter Milan, and other Italian football clubs.