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Sports Injury Prediction Model based on Machine Learning

In: Economic Management and Big Data Application Proceedings of the 3rd International Conference

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  • Yudong Liang

Abstract

In competitive sports, players are always at high risk for injuries. Sports injuries in rugby sports are directly related to the team’s game performance, especially when the player has an old sports injury or psychological stress. By considering the athlete as a dynamic system and quantifying the features associated with sports injuries, machine learning can be used to predict and assess the associated risks. In this paper, a simplified GRU is proposed to construct the mapping relationship between sports injury features and rugby game results. The comparison experiments with other machine learning models show that this model has better robustness in the prediction tasks of sports injuries and competition results of teenage rugby players.

Suggested Citation

  • Yudong Liang, 2024. "Sports Injury Prediction Model based on Machine Learning," World Scientific Book Chapters, in: Sikandar Ali Qalati (ed.), Economic Management and Big Data Application Proceedings of the 3rd International Conference, chapter 86, pages 969-981, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789811270277_0086
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    Keywords

    Big Data; Information Management; Economic; Data Applications; Blockchain; E-commerce;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology

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