Publication: Predicting injury risk using machine learning in male youth soccer players
Authors
Puerta Callejón, José M. ; Gámez, José A. ; De Ste Croix, Mark ; Ayala Rodríguez, Francisco ; Robles Palazón, Francisco Javier ; Cejudo Palomo, Antonio ; Santonja Medina, Fernando ; Sainz de Baranda Andújar, Pilar
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DOI
https://doi.org/10.1016/j.chaos.2022.113079
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info:eu-repo/semantics/article
Description
©2023. This manuscript version is made available under the CC-BY NC-ND 4.0 license http://creativecommons.org/licenses/by nc-nd /4.0/
This document is the, Published, version of a Published Work that appeared in final form in Chaos, Solitons and Fractals. To access the final edited and published work see https://doi.org/10.1016/j.chaos.2022.113079
Abstract
The aim of this study was twofold: a) to build models using machine learning techniques on data from an extensive screening battery to prospectively predict lower extremity soft tissue (LE-ST) injuries in non-elite male youth soccer players, and b) to compare models' performance scores (i.e., predictive accuracy) to select the best fit. A sample of 260 male youth soccer players from the academies of five different Spanish non-professional clubs completed the follow-up. Players were engaged in a pre-season assessment that covered several personal characteristics (e.g., anthropometric measures), psychological constructs (e.g., trait-anxiety), and physical fitness and neuromuscular measures (e.g., range of motion [ROM], landing kinematics). Afterwards, all LE-ST injuries were monitored over one competitive season. The predictive ability (i.e., area under the receiver operating characteristic curve [AUC] and F-score) of several screening models was analysed and compared to select the one with the highest scores. A total of 45 LE-ST injuries were recorded over the season. The best fit screening model developed (AUC = 0.700, F-score = 0.380) allowed to successfully identify one in two (True Positive rate = 53.7 %) and three in four (True Negative rate = 73.9 %) players at high or low risk of suffering a LE-ST injury throughout the in-season phase, respectively, using a subset of six field-based measures (knee medial displacement in the drop jump, asymmetry in the peak vertical ground reaction force during landing, body mass index, asymmetry in the frontal plane projection angle assessed through the tuck jump, asymmetry in the passive hip internal rotation ROM, and ankle dorsiflexion with the knee extended ROM). Given that these measures require little equipment to be recorded and can be employed quickly (approximately 5–10 min) and easily by trained staff in a single player, the model developed might be included in the injury management strategy for youth soccer.
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Citation
Chaos, Solitons and Fractals 167 (2023), 113079
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