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<p>Sports injuries can be considered as self-exciting, in that they can be linked to a previous injury or can be a new independently acquired injury. Disentangling which injuries are connected, either directly or indirectly, remains a persistent challenge, but is important for sport and athlete management. In this paper, we consider injuries as a type of Hawkes Process and view estimation as a branching process. We also propose a computationally efficient latent variable formulation in the context of clustering subsequent sporting injuries among professional Australian Football League (AFL) players in a small sample context. The results underscore the broader advantages of utilising such a point process for accurately modelling and estimating the occurrence of rare events. Our approaches are then assessed to demonstrate better estimation performance and compared to determine their suitability for injury forecasting.</p>

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Sports Injury is Self-exciting: Modelling AFL Injuries Using Hawkes Processes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月17日

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  • John Worrall,
  • Paul Pao-Yen Wu,
  • Liam A. Toohey,
  • Kerrie Mengersen

摘要

Sports injuries can be considered as self-exciting, in that they can be linked to a previous injury or can be a new independently acquired injury. Disentangling which injuries are connected, either directly or indirectly, remains a persistent challenge, but is important for sport and athlete management. In this paper, we consider injuries as a type of Hawkes Process and view estimation as a branching process. We also propose a computationally efficient latent variable formulation in the context of clustering subsequent sporting injuries among professional Australian Football League (AFL) players in a small sample context. The results underscore the broader advantages of utilising such a point process for accurately modelling and estimating the occurrence of rare events. Our approaches are then assessed to demonstrate better estimation performance and compared to determine their suitability for injury forecasting.