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Predictive Modeling of Injury Risk Based on Body Composition and Selected Physical Fitness Tests for Elite Football Players

2022Journal article Open access
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dc.contributor.authorMartins, Francisco
dc.contributor.authorPrzednowek, Krzysztof
dc.contributor.authorFrança, Cíntia
dc.contributor.authorLopes, Helder
dc.contributor.authorNascimento, Marcelo de Maio
dc.contributor.authorSarmento, Hugo
dc.contributor.authorMarques, Adilson
dc.contributor.authorIhle, Andreas
dc.contributor.authorHenriques, Ricardo
dc.contributor.authorGouveia, Élvio Rúbio
dc.date.accessioned2023-05-22T14:38:14Z
dc.date.available2023-05-22T14:38:14Z
dc.date.issued2022
dc.description.abstract: Injuries are one of the most significant issues for elite football players. Consequently, elite football clubs have been consistently interested in having practical, interpretable, and usable models as decision-making support for technical staff. This study aimed to analyze predictive modeling of injury risk based on body composition variables and selected physical fitness tests for elite football players through a sports season. The sample comprised 36 male elite football players who competed in the First Portuguese Soccer League in the 2020/2021 season. The models were calculated based on 22 independent variables that included players’ information, body composition, physical fitness, and one dependent variable, the number of injuries per season. In the net elastic analysis, the variables that best predicted injury risk were sectorial positions (defensive and forward), body height, sit-and reach performance, 1 min number of push-ups, handgrip strength, and 35 m linear speed. This study considered multiple-input single-output regression-type models. The analysis showed that the most accurate model presented in this work generates an error of RMSE = 0.591. Our approach opens a novel perspective for injury prevention and training monitorization. Nevertheless, more studies are needed to identify risk factors associated with injury prediction in elite soccer players, as this is a rising topic that requires several analyses performed in different contexts.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMartins, F.; Przednowek, K.; França, C.; Lopes, H.; de Maio Nascimento, M.; Sarmento, H.; Marques, A.; Ihle, A.; Henriques, R.; Gouveia, É.R. Predictive Modeling of Injury Risk Based on Body Composition and Selected Physical Fitness Tests for Elite Football Players. J. Clin. Med. 2022, 11, 4923. https:// doi.org/10.3390/jcm11164923pt_PT
dc.identifier.doi10.3390/jcm11164923pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5183
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationLaboratory of Robotics and Engineering Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSports injuriespt_PT
dc.subjectMachine learningpt_PT
dc.subjectInjury predictionpt_PT
dc.subjectSports monitorizationpt_PT
dc.subjectElite footballpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Sociaispt_PT
dc.titlePredictive Modeling of Injury Risk Based on Body Composition and Selected Physical Fitness Tests for Elite Football Playerspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLaboratory of Robotics and Engineering Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50009%2F2020/PT
oaire.citation.issue16pt_PT
oaire.citation.startPage4923pt_PT
oaire.citation.titleJournal of Clinical Medicinept_PT
oaire.citation.volume11pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNamePestana Martins
person.familyNamePrzednowek
person.familyNameFrança
person.familyNameLopes
person.familyNamede Maio Nascimento
person.familyNameBorges Sarmento
person.familyNameMarques
person.familyNameIhle
person.familyNameGouveia
person.givenNameJoão Francisco
person.givenNameKrzysztof
person.givenNameCíntia
person.givenNameHelder
person.givenNameMarcelo
person.givenNameHugo Miguel
person.givenNameAdilson
person.givenNameAndreas
person.givenNameÉlvio Rúbio
person.identifier2267308
person.identifier358612
person.identifier1360379
person.identifier.ciencia-id361B-84C0-EEDA
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person.identifier.ciencia-id5F18-F9C3-11CB
person.identifier.ciencia-id3B10-8BDF-0D67
person.identifier.orcid0000-0003-4838-4931
person.identifier.orcid0000-0002-2128-4116
person.identifier.orcid0000-0001-8364-6832
person.identifier.orcid0000-0002-1388-9473
person.identifier.orcid0000-0002-3577-3439
person.identifier.orcid0000-0001-9850-7771
person.identifier.orcid0000-0001-9845-5190
person.identifier.orcid0000-0003-0927-692X
person.identifier.ridK-4529-2014
person.identifier.scopus-author-id56416391600
person.identifier.scopus-author-id55082567000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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