Event history data from sports competitions have recently drawn increasing attention in sports analytics to generate data-driven strategies. Such data often exhibit self-excitation in the event occurrence and dependence within event clusters. The conventional event models based on gap times may struggle to capture those features. In particular, while consecutive events may occur within a short timeframe, the self-excitation effect caused by previous events is often transient and continues for a period of uncertain time. This paper introduces an extended Hawkes process model with random self-excitation duration to formulate the dynamics of event occurrence. We present examples of the proposed model and procedures for estimating the associated model parameters. We employ the collection of the corner kicks in the games of the 2019 regular season of the Chinese Super League to motivate and illustrate the modeling and its usefulness. We also design algorithms for simulating the event process under proposed models. The proposed approach can be adapted with little modification in many other research fields such as Criminology and Infectious Disease.

翻译：体育竞赛中的事件历史数据近年来在体育分析领域日益受到关注，用于生成数据驱动的策略。此类数据通常在事件发生中表现出自激性，并在事件簇内存在依赖性。基于间隔时间的传统事件模型可能难以捕捉这些特征。具体而言，尽管连续事件可能在短时间内发生，但先前事件引发的自激效应往往是瞬时的，并会持续一段不确定的时间。本文引入了一种具有随机自激持续时间的扩展霍克斯过程模型，以描述事件发生的动态。我们展示了所提出模型的示例以及估计相关模型参数的程序。我们利用2019赛季中超联赛比赛中角球事件的集合来激发和说明该建模方法及其实用性。我们还设计了在所提模型下模拟事件过程的算法。所提出的方法只需稍作修改即可适用于犯罪学和传染病学等许多其他研究领域。