Using machine learning to improve our understanding of injury risk and prediction in elite male youth football players.

Journal: Journal of science and medicine in sport
Published Date: Nov 1, 2020

Abstract

OBJECTIVES: The purpose of this study was to examine whether the use of machine learning improved the ability of a neuromuscular screen to identify injury risk factors in elite male youth football players.

Authors

  • Jon L Oliver
    Youth Physical Development Centre, Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, UK; Sport Performance Research Institute New Zealand (SPRINZ), Auckland University of Technology, New Zealand. Electronic address: joliver@cardiffmet.ac.uk.
  • Francisco Ayala
    Sports Research Centre, Miguel Hernandez University of Elche, Alicante, SPAIN.
  • Mark B A De Ste Croix
    School of Physical Education, Faculty of Sport, Health and Social Care, University of Gloucester, UK.
  • Rhodri S Lloyd
    Youth Physical Development Centre, Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, UK; Sport Performance Research Institute New Zealand (SPRINZ), Auckland University of Technology, New Zealand; Centre for Sport Science and Human Performance, Waikato Institute of Technology, New Zealand.
  • Greg D Myer
    Division of Sports Medicine, Cincinnati Children's Hospital, USA.
  • Paul J Read
    Athlete Health and Performance Research Centre, Aspetar Orthopaedic and Sports Medicine Hospital, Qatar.

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