Jon L Oliver1, Francisco Ayala2, Mark B A De Ste Croix3, Rhodri S Lloyd4, Greg D Myer5, Paul J Read6. 1. 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. 2. Sports Research Centre, Miguel Hernadez University of Elche, Spain. 3. School of Physical Education, Faculty of Sport, Health and Social Care, University of Gloucester, UK. 4. 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. 5. Division of Sports Medicine, Cincinnati Children's Hospital, USA. 6. Athlete Health and Performance Research Centre, Aspetar Orthopaedic and Sports Medicine Hospital, Qatar.
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. DESIGN: Prospective cohort study. METHODS: 355 elite youth football players aged 10-18 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.
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. DESIGN: Prospective cohort study. METHODS: 355 elite youth football players aged 10-18 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.
Authors: Garrett S Bullock; Joseph Mylott; Tom Hughes; Kristen F Nicholson; Richard D Riley; Gary S Collins Journal: Sports Med Date: 2022-06-11 Impact factor: 11.928