Predictive modelling of training loads and injury in Australian football
| Autor: | Rod Whiteley, David Carey, Kok-Leong Ong, Justin Crow, Kay M. Crossley, Meg E. Morris |
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| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
General Computer Science Computer science Biomedical Engineering Workload Machine Learning (stat.ML) 030229 sport sciences Football Estimating equations Logistic regression Statistics - Applications Random forest 03 medical and health sciences 0302 clinical medicine Moving average Statistics - Machine Learning Statistics Injury prevention Applications (stat.AP) 030212 general & internal medicine Predictive modelling Uncategorized |
| DOI: | 10.26181/22769228 |
| Popis: | To investigate whether training load monitoring data could be used to predict injuries in elite Australian football players, data were collected from elite athletes over 3 seasons at an Australian football club. Loads were quantified using GPS devices, accelerometers and player perceived exertion ratings. Absolute and relative training load metrics were calculated for each player each day (rolling average, exponentially weighted moving average, acute:chronic workload ratio, monotony and strain). Injury prediction models (regularised logistic regression, generalised estimating equations, random forests and support vector machines) were built for non-contact, non-contact time-loss and hamstring specific injuries using the first two seasons of data. Injury predictions were generated for the third season and evaluated using the area under the receiver operator characteristic (AUC). Predictive performance was only marginally better than chance for models of non-contact and non-contact time-loss injuries (AUC$ Comment: 15 pages, 5 figures |
| Databáze: | OpenAIRE |
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