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Data Mining Paths for Standard Weekly Training Load in Sub-Elite Young Football Players: A Machine Learning Approach

2024Journal article Open access
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dc.contributor.authorTeixeira, José Eduardo
dc.contributor.authorEncarnação, Samuel
dc.contributor.authorBranquinho, Luís
dc.contributor.authorMorgans, Ryland
dc.contributor.authorAfonso, Pedro
dc.contributor.authorRocha, João Pedro da Silva
dc.contributor.authorGraça, Francisco M.
dc.contributor.authorBarbosa, Tiago M.
dc.contributor.authorMonteiro, A.M.
dc.contributor.authorFerraz, Ricardo
dc.contributor.authorForte, Pedro
dc.date.accessioned2024-10-17T14:15:42Z
dc.date.available2024-10-17T14:15:42Z
dc.date.issued2024
dc.description.abstractThe aim of this study was to test a machine learning (ML) model to predict high-intensity actions and body impacts during youth football training. Sixty under-15, -17, and -19 sub-elite Portuguese football players were monitored over a 6-week period. External training load data were collected from the target variables of accelerations (ACCs), decelerations (DECs), and dynamic stress load (DSL) using an 18 Hz global positioning system (GPS). Additionally, we monitored the perceived exertion and biological characteristics using total quality recovery (TQR), rating of perceived exertion (RPE), session RPE (sRPE), chronological age, maturation offset (MO), and age at peak height velocity (APHV). The ML model was computed by a feature selection process with a linear regression forecast and bootstrap method. The predictive analysis revealed that the players’ MO demonstrated varying degrees of effectiveness in predicting their DEC and ACC across different ranges of IQR. After predictive analysis, the following performance values were observed: DEC (xpredicted = 41, β = 3.24, intercept = 37.0), lower IQR (IQRpredicted = 36.6, β = 3.24, intercept = 37.0), and upper IQR (IQRpredicted = 46 decelerations, β = 3.24, intercept = 37.0). The player’s MO also demonstrated the ability to predict their upper IQR (IQRpredicted = 51, β = 3.8, intercept = 40.62), lower IQR (IQRpredicted = 40, β = 3.8, intercept = 40.62), and ACC (xpredicted = 46 accelerations, β = 3.8, intercept = 40.62). The ML model showed poor performance in predicting the players’ ACC and DEC using MO (MSE = 2.47–4.76; RMSE = 1.57–2.18: R2 = −0.78–0.02). Maturational concerns are prevalent in football performance and should be regularly checked, as the current ML model treated MO as the sole variable for ACC, DEC, and DSL. Applying ML models to assess automated tracking data can be an effective strategy, particularly in the context of forecasting peak ACC, DEC, and bodily effects in sub-elite youth football training.pt_PT
dc.description.sponsorshipThis project was supported by the National Funds through the FCT Portuguese Foundation for Science and Technology (project UIDB04045/2021), under grant number UID/CED/04748/2020. Life Quality Research Center (LQRC-CIEQV), Santarém, Portugal.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationTeixeira, José E.; Encarnação, Samuel; Branquinho, Luís; Morgans, Ryland; Afonso, Pedro; Rocha, João Pedro da Silva; Graça, Francisco; Barbosa, Tiago M.; Monteiro, António M.; Ferraz, Ricardo; Forte, Pedro (2024). Data Mining Paths for Standard Weekly Training Load in Sub-Elite Young Football Players: A Machine Learning Approach. ISSN 2411-5142. 9:3, p. 1-12pt_PT
dc.identifier.doi10.3390/jfmk9030114pt_PT
dc.identifier.issn2411-5142
dc.identifier.urihttp://hdl.handle.net/10198/30445
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationLife Quality Research Centre
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectArtificial intelligence (AI)pt_PT
dc.subjectPeriodizationpt_PT
dc.subjectMaturationpt_PT
dc.subjectYouthpt_PT
dc.subjectBig datapt_PT
dc.subjectResearch Subject Categories::INTERDISCIPLINARY RESEARCH AREAS::Sportspt_PT
dc.titleData Mining Paths for Standard Weekly Training Load in Sub-Elite Young Football Players: A Machine Learning Approachpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLife Quality Research Centre
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04748%2F2020/PT
oaire.citation.endPage12pt_PT
oaire.citation.issue3pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleJournal of Functional Morphology and Kinesiologypt_PT
oaire.citation.volume9pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameTeixeira
person.familyNameEncarnação
person.familyNameBarbosa
person.familyNameMonteiro
person.familyNameForte
person.givenNameJosé Eduardo
person.givenNameSamuel
person.givenNameTiago M.
person.givenNameAntónio M.
person.givenNamePedro
person.identifier.ciencia-idD11C-9591-7A8A
person.identifier.ciencia-id9416-E2F5-E660
person.identifier.ciencia-id8B11-BDC4-F6FF
person.identifier.ciencia-idC41C-6CCD-A1F0
person.identifier.ciencia-id351B-B16B-79C7
person.identifier.orcid0000-0003-4612-3623
person.identifier.orcid0000-0003-2965-2777
person.identifier.orcid0000-0001-7071-2116
person.identifier.orcid0000-0003-4467-1722
person.identifier.orcid0000-0003-0184-6780
person.identifier.scopus-author-id10044856400
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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