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Corner kicks are an important event in soccer because they are often the result of strong attacking play and can be of keen interest to sports fans and bettors. Peng, Hu, and Swartz (2024, Computational Statistics) frame the commonly available corner kick data as right-censored event times, formulate the mixture feature of corner kick times caused by previous corner kicks, and explore patterns of corner kicks associated with several factors. This paper extends their modeling to accommodate the potential correlations between corner kicks by the same teams within the same games. We consider a frailty model for event times and apply the Monte Carlo Expectation Maximization (MCEM) algorithm to obtain the maximum likelihood estimates for the model parameters. We compare the proposed model with the model in Peng, Hu, and Swartz (2024) using likelihood ratio tests. The 2019 Chinese Super League (CSL) data are employed throughout the paper for motivation and illustration.

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Learning About Corner Kicks in Soccer by Analysis of Event Times Using a Frailty Model

  • 会议论文

  • 发表日期：

    2026年

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  • Riley L. Isaacs,
  • X. Joan Hu,
  • K. Ken Peng,
  • Tim B. Swartz

摘要

Corner kicks are an important event in soccer because they are often the result of strong attacking play and can be of keen interest to sports fans and bettors. Peng, Hu, and Swartz (2024, Computational Statistics) frame the commonly available corner kick data as right-censored event times, formulate the mixture feature of corner kick times caused by previous corner kicks, and explore patterns of corner kicks associated with several factors. This paper extends their modeling to accommodate the potential correlations between corner kicks by the same teams within the same games. We consider a frailty model for event times and apply the Monte Carlo Expectation Maximization (MCEM) algorithm to obtain the maximum likelihood estimates for the model parameters. We compare the proposed model with the model in Peng, Hu, and Swartz (2024) using likelihood ratio tests. The 2019 Chinese Super League (CSL) data are employed throughout the paper for motivation and illustration.