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.

翻译：技术进步为在篮球、足球及其他团队运动中采集高分辨率追踪数据铺平了道路。这类数据由按空间和时间索引的竞争球队球员间的交互组成。理解并预测球队表现的关键在于这些高分辨率球员交互追踪数据，因为球队的整体表现远非个体球员能力之和。我们提出了一种连续时间随机过程，用于建模按空间和时间索引的竞争球队球员间的交互，讨论了该连续时间随机过程的性质，并采用贝叶斯方法从高分辨率追踪数据中学习该随机过程。我们将其应用于尤文图斯、国际米兰及其他意大利足球俱乐部。