Using machine learning to determine the positions of professional soccer players in terms of biomechanical variables

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dc.authoridEken, Ozgur/0000-0002-5488-3158
dc.authoridBadicu, Georgian/0000-0003-4100-8765
dc.authoridHasan, Uday/0000-0002-5809-3743
dc.authoridYAGIN, Fatma Hilal/0000-0002-9848-7958
dc.authoridClemente, Filipe Manuel/0000-0001-9813-2842
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorHasan, Uday C. H.
dc.contributor.authorClemente, Filipe Manuel
dc.contributor.authorEken, Ozgur
dc.contributor.authorBadicu, Georgian
dc.contributor.authorGulu, Mehmet
dc.date.accessioned2025-01-21T16:55:49Z
dc.date.available2025-01-21T16:55:49Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
dc.description.abstractThis study aimed to predict professional soccer players' positions with machine learning according to certain locomotor demands. Data from 20 male professional soccer players (five defenders, eight midfielders, and seven attackers) from the same team were tracked daily with a global navigation satellite system. A total of 1910 individual training sessions were recorded. The 10-fold cross-validation method was used. Soccer player positions were predicted using predictive models created with random forest (RF), gradient boosting tree, bagging classification, and regression trees algorithms, and the results were evaluated with comprehensive performance measures. Ratios and an importance plot were used to analyze the importance of the variables according to their contributions to the estimation. The findings show that the RF model achieved 100% accuracy, which means that RF can predict all player positions (100%). Running distance (26.5%), total distance (17.2%), and player load (15.8%) were the three variables that contributed the most to the estimation of the RF model and were the most important factor in distinguishing player positions. Consequently, our proposed machine learning approach (RF model) can reduce false alarms and player mispositioning.
dc.description.sponsorshipFundacxaopara a Ciencia e Tecnologia/Ministerio da Ciencia; Tecnologia e Ensino Superior through national funds; EU funds [UIDB/50008/2020]
dc.description.sponsorship& nbsp;The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is funded by Fundacxaopara a Ciencia e Tecnologia/Ministerio da Ciencia, Tecnologia e Ensino Superior through national funds and, when applicable, co-funded by EU funds under the project UIDB/50008/2020.
dc.identifier.doi10.1177/17543371231199814
dc.identifier.issn1754-3371
dc.identifier.issn1754-338X
dc.identifier.scopus2-s2.0-85171267817
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1177/17543371231199814
dc.identifier.urihttps://hdl.handle.net/20.500.12587/25852
dc.identifier.wosWOS:001066553200001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSage Publications Ltd
dc.relation.ispartofProceedings of The Institution of Mechanical Engineers Part P-Journal of Sports Engineering and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectBiomechanics; soccer; machine learning; modeling; global positioning system; random forest; gradient boosting tree; bagging classification; regression trees algorithms; First Portuguese League
dc.titleUsing machine learning to determine the positions of professional soccer players in terms of biomechanical variables
dc.typeArticle

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