Learning about Corner Kicks in Soccer by Analysis of Event Times Using a Frailty Model
Riley L Isaacs, X. Joan Hu, K. Ken Peng, Tim Swartz

TL;DR
This paper develops a frailty model for analyzing the timing of corner kicks in soccer, accounting for correlations within games, and applies it to Chinese Super League data to better understand attacking patterns.
Contribution
It extends previous models by incorporating correlations between corner kicks within the same game using a frailty approach and applies the Monte Carlo EM algorithm for estimation.
Findings
The frailty model better fits the data than previous models.
Significant correlations exist between corner kicks within the same game.
The model provides insights into team attacking behaviors.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSports Analytics and Performance · Statistical Methods and Inference · Probability and Statistical Research
