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

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Author
Oliver, Jon
Ayala, Francisco
De Ste Croix, Mark
Lloyd, Rhodri S.
Myer, Greg
Read, Paul
Date
2020-05-18Acceptance date
2020-04-30
Type
Article
acceptedVersion
Publisher
Elsevier
ISSN
1440-2440
Embargoed until
2022-05-18
Metadata
Show full item recordAbstract
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.
Methods: 355 elite youth football players aged 10 to 18 years old completed a prospective pre-season neuromuscular screen that included anthropometric measures of size, as well as single leg countermovement jump (SLCMJ), single leg hop for distance (SLHD), 75% hop distance and stick (75%Hop), Y-balance anterior reach and tuck jump assessment. Injury incidence was monitored over one competitive season. Risk profiling was assessed using traditional regression analyses and compared to supervised machine learning algorithms constructed using decision trees.
Results: Using continuous data, multivariate logistic analysis identified SLCMJ asymmetry as the sole significant predictor of injury (OR 0.94, 0.92-0.97, p<0.001), with a specificity of 97.7% and sensitivity of 15.2% giving an AUC of 0.661. The best performing decision tree model provided a specificity of 74.2% and sensitivity of 55.6% with an AUC of 0.663. All variables contributed to the final machine model, with asymmetry in the SLCMJ, 75%Hop and Y-balance, plus tuck jump knee valgus and anthropometrics being the most frequent contributors.
Conclusions: Although both statistical methods reported similar accuracy, logistic regression provided very low sensitivity and only identified a single neuromuscular injury risk factor. The machine learning model provided much improved sensitivity to predict injury and identified interactions of asymmetry, knee valgus angle and body size as contributing factors to an injurious profile in youth football players.
Journal/conference proceeding
Journal of Science and Medicine in Sport;
Citation
Oliver, J.L., Ayala, F., Croix, M.B.D.S., Lloyd, R.S., Myer, G.D. and Read, P.J. (2020) 'Using machine learning to improve our understanding of injury risk and prediction in elite male youth football players', Journal of Science and Medicine in Sport. https://doi.org/10.1016/j.jsams.2020.04.021
Description
Article published in Journal of Science and Medicine in Sport on 18 May 2020, available at: https://doi.org/10.1016/j.jsams.2020.04.021.
Sponsorship
Cardiff Metropolitan University (Grant ID: Cardiff Metropolian (Internal))
Collections
- Sport Research Groups [1088]
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