Using machine learning to improve our understanding of injury risk and prediction in elite male youth football players
De Ste Croix, Mark
Lloyd, Rhodri S.
MetadataShow full item record
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 of Science and Medicine in Sport;
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
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.
Cardiff Metropolitan University (Grant ID: Cardiff Metropolian (Internal))
- Sport Research Groups 
Showing items related by title, author, subject and abstract.
Dotcheva, Mariana (Cardiff Metropolitan University, 2006)End milling is a widely used cutting process involved in different types of finishing profile machining, where the geometry is complex, the tolerances are small and the cost of the operations is high. Despite tremendous ...
Tysoe, Alexander (2016-04-01)Abstract Objectives Objectives of the study were to investigate the relationship between periods that preceded injury and that did not precede injury in professional fast bowlers across a First Class County Cricket (FCCC) ...
Bitchell, Charlotte; Varley-Campbell, Jo; Robinson, Gemma; Stiles, Victoria; Mathema, Prabhat; Moore, Isabel (Springer, 2020-12-03)Background Injury surveillance in professional sport categorises injuries as either “new” or “recurrent”. In an attempt to make categorisation more specific, subsequent injury categorisation models have been developed, ...