We propose a novel mixture model for football event data that clusters entire possessions to reveal their temporal, sequential, and spatial structure. Each mixture component models possessions as marked spatio-temporal point processes: event types follow a finite Markov chain with an absorbing state for ball loss, event times follow a conditional Gamma process to account for dispersion, and spatial locations evolve via truncated Brownian motion. To aid interpretation, we derive summary indicators from model parameters capturing possession speed, number of events, and spatial dynamics. Parameters are estimated through maximum likelihood via Generalized Expectation-Maximization algorithm. Applied to StatsBomb data from 38 Ligue 1 matches (2020/2021), our approach uncovers distinct defensive possession patterns faced by Stade Rennais. Unlike previous approaches focusing on individual events, our mixture structure enables principled clustering of full possessions, supporting tactical analysis and the future development of realistic virtual training environments.

翻译：我们提出了一种新颖的混合模型，用于分析足球事件数据，通过对完整控球过程进行聚类，揭示其时间、序列和空间结构。每个混合分量将控球过程建模为标记时空点过程：事件类型遵循具有吸收状态（代表失球）的有限马尔可夫链；事件时间遵循条件伽马过程以考虑离散性；空间位置通过截断布朗运动演化。为便于解释，我们从模型参数中推导出总结性指标，捕捉控球速度、事件数量和空间动态。参数通过广义期望最大化算法进行最大似然估计。应用于来自38场法甲联赛（2020/2021赛季）的StatsBomb数据，我们的方法揭示了雷恩足球俱乐部面临的独特防守控球模式。与以往关注单个事件的方法不同，我们的混合结构能够对完整控球过程进行有原则的聚类，支持战术分析以及未来开发逼真的虚拟训练环境。