Paper
Learning about Corner Kicks in Soccer by Analysis of Event Times Using a Frailty Model
Authors
Riley L Isaacs, X. Joan Hu, K. Ken Peng, Tim Swartz
Abstract
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) formulate the mixture feature of corner kick times caused by previous corner kicks, frame the commonly available corner kick data as right-censored event times, and explore patterns of corner kicks. This paper extends their modeling to accommodate the potential correlations between corner kicks by the same teams within the same games. We con- sider a frailty model for event times and apply the Monte Carlo Expec- tation Maximization (MCEM) algorithm to obtain the maximum like- lihood 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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.22684v1</id>\n <title>Learning about Corner Kicks in Soccer by Analysis of Event Times Using a Frailty Model</title>\n <updated>2026-02-26T06:58:39Z</updated>\n <link href='https://arxiv.org/abs/2602.22684v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.22684v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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) formulate the mixture feature of corner kick times caused by previous corner kicks, frame the commonly available corner kick data as right-censored event times, and explore patterns of corner kicks. This paper extends their modeling to accommodate the potential correlations between corner kicks by the same teams within the same games. We con- sider a frailty model for event times and apply the Monte Carlo Expec- tation Maximization (MCEM) algorithm to obtain the maximum like- lihood 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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='stat.ME'/>\n <published>2026-02-26T06:58:39Z</published>\n <arxiv:primary_category term='stat.ME'/>\n <author>\n <name>Riley L Isaacs</name>\n </author>\n <author>\n <name>X. Joan Hu</name>\n </author>\n <author>\n <name>K. Ken Peng</name>\n </author>\n <author>\n <name>Tim Swartz</name>\n </author>\n </entry>"
}