A synthetic data-driven machine learning approach for athlete performance attenuation prediction.

Journal: Frontiers in sports and active living
Published Date: May 27, 2025

Abstract

INTRODUCTION: Athlete performance monitoring is effective for optimizing training strategies and preventing injuries. However, applying machine learning (ML) frameworks to this domain remains challenging due to data scarcity limitations. This study extends previous research by evaluating Tabular Variational Autoencoders (TVAE) for generating synthetic data to predict performance attenuation in Gaelic football athletes.

Authors

  • Mauricio C Cordeiro
    Department of Engineering & Informatics, Technological University of the Shannon, Athlone, Ireland.
  • Ciaran O Cathain
    Department of Sport & Health Sciences, Technological University of the Shannon, Athlone, Ireland.
  • Lorcan Daly
    Department of Sport & Health Sciences, Technological University of the Shannon, Athlone, Ireland.
  • David T Kelly
    Department of Sport & Health Sciences, Technological University of the Shannon, Athlone, Ireland.
  • Thiago B Rodrigues
    Department of Engineering & Informatics, Technological University of the Shannon, Athlone, Ireland.

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