Training load prior to injury in professional Rugby League players: Analysing injury risk with machine learning

Author(s)
Welch, Mitchell
Cummins, Cloe
Thornton, Heidi
King, Douglas
Murphy, Aron
Publication Date
2018
Abstract
This study explores the application of Global Positioning System tracking data from field training sessions and supervised machine learning algorithms for predicting injury risk of players across a single National Rugby League season. Previous work across a range of sporting codes has demonstrated associations between training loads and increased incidence of injury in professional athletes. Most of the work conducted has applied a reductionist approach, identifying training load characteristics as risk factors using generalised models to show population trends. This study demonstrates promising results by applying processing techniques and machine learning algorithms to analyse the injury risk associated with complex training load patterns. The accuracy of the algorithms are investigated along with the importance of training load predictors and data window sizes.
Citation
International Symposium on Biomechanics in Sports, 36(1), p. 330-333
ISSN
1999-4168
Link
Publisher
International Society of Biomechanics in Sports (ISBS)
Title
Training load prior to injury in professional Rugby League players: Analysing injury risk with machine learning
Type of document
Conference Publication
Entity Type
Publication

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