Predicting athletic performance from physiological parameters using machine learning: Example of bocce ball

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dc.authorwosidKesilmiş, İnci/AAU-4465-2020en_US
dc.contributor.authorSimsek, Mehmet
dc.contributor.authorKesilmis, Inci
dc.date.accessioned2024-08-23T16:03:39Z
dc.date.available2024-08-23T16:03:39Z
dc.date.issued2022en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractMachine learning (ML) is an emerging topic in Sports Science. Some pioneering studies have applied machine learning to prevent injuries, to predict star players, and to analyze athletic performance. The limited number of studies in the literature focused on predicting athletic performance have adopted the cluster-then-predict classification approach. However, these studies have used the independent variable to represent athletic performance at both the clustering and classification stages. In this study we used only physiological parameters in the classification of bocce athletes. Their performance classes were predicted with high accuracy, thus contributing new findings to the literature. The support vector machines-radial basis function (SVM-RBF) kernel correctly predicted all athletes from the high-performance bocce player (HPBP) cluster and 75% of the athletes in the low-performance bocce player (LPBP) cluster. Using machine learning to predict athletic performance from balance data was found to be a time-saving approach for selecting high-potential bocce athletes.en_US
dc.identifier.doi10.3233/JSA-200617
dc.identifier.endpage229en_US
dc.identifier.issn2215-020X
dc.identifier.issn2215-0218
dc.identifier.issue3en_US
dc.identifier.startpage221en_US
dc.identifier.urihttps://doi.org/10.3233/JSA-200617
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13845
dc.identifier.volume8en_US
dc.identifier.wosWOS:000856614500005en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIos Pressen_US
dc.relation.ispartofJournal of Sports Analyticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectY balanceen_US
dc.subjectstatic balanceen_US
dc.subjectbocceen_US
dc.subjectpetanqueen_US
dc.subjectmachine learningen_US
dc.subjectclassificationen_US
dc.subjectBalanceen_US
dc.titlePredicting athletic performance from physiological parameters using machine learning: Example of bocce ballen_US
dc.typeArticleen_US

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