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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., (Copyright © 2020 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)

Background: The efficacy of robustness training for high- versus low-risk individuals within high-risk groups is currently unknown., Purpose: To explore the efficacy of robustness training on injury risk factors among female youth soccer players and to examine if high-risk athletes are greater responders to such training., Study Design: Controlled laboratory study., Methods: A total of 125 elite youth female soccer players on the English FA talent pathway were randomly selected into a training group (n = 71) or a control group (n = 54). Relative leg stiffness, 2-dimensional knee valgus and knee flexion range of motion from a single-legged countermovement jump, and probability of high knee abduction moment (pKAM) risk were all determined before and after a 16-week robustness training program. For further analysis, participants in the training group were split into groups based on risk: high risk (pKAM >0.80, n = 33) and low risk (pKAM <0.55, n = 33). Magnitude-based inferences were used to explore differences between the control and intervention groups and the high- and low-risk groups., Results: Magnitude-based inferences demonstrated significant beneficial effects in the training group for knee valgus, pKAM, and leg stiffness as compared with the control group. The control group demonstrated possible worthwhile differences in knee flexion range of motion as compared with the intervention group. The high-risk group demonstrated likely/very likely worthwhile differences versus the low-risk group for all parameters., Conclusion/clinical Relevance: Robustness training induces significant beneficial improvements in injury risk factors among female youth soccer players. The beneficial effects of this multidimensional program are greater for those classified as high risk.