{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T08:41:47Z","timestamp":1773304907626,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T00:00:00Z","timestamp":1762992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Foundation for Science and Technology","award":["2023.01187.BD"],"award-info":[{"award-number":["2023.01187.BD"]}]},{"name":"LARSyS\u2014the Portuguese national funding agency for science, research, and technology","award":["UIDB\/50009\/2020"],"award-info":[{"award-number":["UIDB\/50009\/2020"]}]},{"name":"CIPER\u2014Portuguese National Funding Agency for Science, Research, and Technology","award":["UID\/06349\/2025"],"award-info":[{"award-number":["UID\/06349\/2025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Professional football requires more attention in planning work regimens that balance players\u2019 sports performance optimization and reduce their injury probability. Machine learning applied to sports science has focused on predicting these events and identifying their risk factors. Our study aims to (i) analyze the differences between injury incidence during training and matches and (ii) build and classify different predictive models of risk based on players\u2019 internal and external loads across four sports seasons. Methods: This investigation involved 96 male football players (26.2 \u00b1 4.2 years; 181.1 \u00b1 6.1 cm; 74.5 \u00b1 7.1 kg) representing a single professional football club across four analyzed seasons. The research was designed according to three methodological sets of assessments: (i) average season performance, (ii) two weeks\u2019 performance before the event, and (iii) four weeks\u2019 performance before the event. We applied machine learning classification methods to build and classify different predictive injury risk models for each dataset. The dependent variable is categorical, representing the occurrence of a time-loss muscle injury (N = 97). The independent variables include players\u2019 information and external (GPS-derived) and internal (RPE) workload variables. Results: The Kstar classifier with the four-week window dataset achieved the best predictive performance, presenting an Area Under the Precision\u2013Recall Curve (AUC-PR) of 83% and a balanced accuracy of 72%. Conclusions: In practical terms, this methodology provides technical staff with more reliable data to inform modifications to playing and training regimens. Future research should focus on understanding the technical staff\u2019s qualitative vision of predictive models\u2019 in-field applicability.<\/jats:p>","DOI":"10.3390\/jcm14228039","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T16:46:22Z","timestamp":1763138782000},"page":"8039","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning-Based Prediction of Muscle Injury Risk in Professional Football: A Four-Year Longitudinal Study"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4838-4931","authenticated-orcid":false,"given":"Francisco","family":"Martins","sequence":"first","affiliation":[{"name":"University of Coimbra, CIPER, FCDEFUC, 3004-504 Coimbra, Portugal"},{"name":"Department of Physical Education and Sport, University of Madeira, 9020-105 Funchal, Portugal"},{"name":"LARSYS, Interactive Technologies Institute, 9020-105 Funchal, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8681-0642","authenticated-orcid":false,"given":"Hugo","family":"Sarmento","sequence":"additional","affiliation":[{"name":"University of Coimbra, CIPER, FCDEFUC, 3004-504 Coimbra, Portugal"},{"name":"CIPER, Faculty of Human Kinetics, University of Lisbon, Cruz-Quebrada-Dafundo, 1495-751 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0927-692X","authenticated-orcid":false,"given":"\u00c9lvio R\u00fabio","family":"Gouveia","sequence":"additional","affiliation":[{"name":"Department of Physical Education and Sport, University of Madeira, 9020-105 Funchal, Portugal"},{"name":"LARSYS, Interactive Technologies Institute, 9020-105 Funchal, Portugal"},{"name":"Swiss Center of Expertise in Life Course Research LIVES, 1227 Carouge, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7005-8457","authenticated-orcid":false,"given":"Paulo","family":"Saveca","sequence":"additional","affiliation":[{"name":"Superior School of Sports Sciences, Eduardo Mondlane University, 3453 Maputo, Mozambique"},{"name":"Mozambique Secondary Institute of Sport and Physical Education, 1071 Maputo, Mozambique"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2128-4116","authenticated-orcid":false,"given":"Krzysztof","family":"Przednowek","sequence":"additional","affiliation":[{"name":"Faculty of Physical Culture Sciences, Medical College, University of Rzesz\u00f3w, 35-959 Rzesz\u00f3w, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1007\/s40279-016-0492-2","article-title":"The mental health of elite athletes: A narrative systematic review","volume":"46","author":"Rice","year":"2016","journal-title":"Sports Med."},{"key":"ref_2","first-page":"752","article-title":"Modeling of relationships between physical and technical activities and match outcome in elite German soccer players","volume":"59","author":"MEDICA","year":"2019","journal-title":"J. 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