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Predicting injury risk using machine learning in male youth soccer players

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dc.contributor.authorPuerta Callejón, José M.
dc.contributor.authorGámez, José A.
dc.contributor.authorDe Ste Croix, Mark
dc.contributor.authorAyala Rodríguez, Francisco
dc.contributor.authorRobles Palazón, Francisco Javier
dc.contributor.authorCejudo Palomo, Antonio
dc.contributor.authorSantonja Medina, Fernando
dc.contributor.authorSainz de Baranda Andújar, Pilar
dc.contributor.departmentActividad Física y Deporte
dc.date.accessioned2024-02-09T09:56:23Z
dc.date.available2024-02-09T09:56:23Z
dc.date.issued2023
dc.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.2022.113079es
dc.description.abstractThe 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.es
dc.formatapplication/pdfes
dc.format.extent11es
dc.identifier.citationChaos, Solitons and Fractals 167 (2023), 113079
dc.identifier.doihttps://doi.org/10.1016/j.chaos.2022.113079
dc.identifier.issn1873-2887
dc.identifier.issn0960-0779
dc.identifier.urihttp://hdl.handle.net/10201/139077
dc.languageenges
dc.relationFrancisco Javier Robles-Palazón was initially supported by the Program of Human Resources Formation for Science and Technology (20326/FPI/2017) from the Seneca Foundation: Agency for Science and Technology in the Region of Murcia (Spain) and subsequently by a Margarita Salas postdoctoral fellowship (UMU/R-1500/2021) given by the Spanish Ministry of Universities and funded by the European Union-NextGenerationEU. Francisco Ayala was supported by a Ramón y Cajal postdoctoral fellowship given by the Spanish Ministry of Science and Innovation (RYC2019-028383-I). This study is part of the projects entitled “Estudio del riesgo de lesión en jóvenes deportistas a través de redes de inteligencia artificial” (DEP2017-88775-P) and “El fútbol femenino importa: identificación del riesgo de lesión a través de la inteligencia artificial” (PID2020-115886RB-I00), funded by the Spanish Ministry of Science and Innovation, the State Research Agency (AEI) and the European Regional Development Fund (ERDF).es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titlePredicting injury risk using machine learning in male youth soccer playerses
dc.typeinfo:eu-repo/semantics/articlees
dspace.entity.typePublicationes
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