Prediction of soccer clubs' league rankings by machine learning methods: The case of Turkish Super League

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dc.contributor.authorTumer, Abdullah Erdal
dc.contributor.authorAkyildiz, Zeki
dc.contributor.authorGuler, Aytek Hikmet
dc.contributor.authorSaka, Esat Kaan
dc.contributor.authorIevoli, Riccardo
dc.contributor.authorPalazzo, Lucio
dc.contributor.authorClemente, Filipe Manuel
dc.date.accessioned2024-02-23T14:27:10Z
dc.date.available2024-02-23T14:27:10Z
dc.date.issued2022
dc.departmentNEÜen_US
dc.description.abstractThe aim of this research is to predict league rankings through various machine learning models using technical and physical parameters. This study followed a longitudinal observational analytical design. The SENTIO Sports optical tracking system was used to measure the physical demands and technical practices of the players in all matches. Then, the data regarding the last three seasons of the Turkish Super League (2015-2016, 2016-2017, and 2017-2018), was collected. In this research, league rankings were estimated using three machine learning methods: Artificial Neural Networks (ANN), Radial Basis Function (RBFN), Multiple Linear Regression (MLR) with technical and physical parameters of all seasons. Performances were evaluated through R-2, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Prediction results of the models are the following: ANN Model; R-2 = 0.60, RMSE = 3.7855 and MAE = 2.9139, RBFN Model; R-2 = 0.26, MAE = 3.6292 and RMSE = 4.5168, MLR Model; R-2 = 0.46, MAE = 3.4859 and RMSE = 4.2064. These results showed that ANN can be used as a successful tool to predict league rankings. In the light of this research, coaches and athletic trainers can organize their training in a way that affects the technical and physical parameters to change the results of the competition. Thus, it will be possible for teams to have a better place in the league-end success ranking.en_US
dc.description.sponsorshipFundacao para a Ciencia e Tecnologia/Miniestrerio/Ministerio da Ciencia, Tecnologia e Ensino Superior through national funds; EU funds [UIDB/50008/2020]en_US
dc.description.sponsorshipThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is funded by Fundacao para a Ciencia e Tecnologia/Miniestrerio/Ministerio da Ciencia, Tecnologia e Ensino Superior through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020.en_US
dc.identifier.doi10.1177/17543371221140492
dc.identifier.issn1754-3371
dc.identifier.issn1754-338X
dc.identifier.scopus2-s2.0-85144204436en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1177/17543371221140492
dc.identifier.urihttps://hdl.handle.net/20.500.12452/14466
dc.identifier.wosWOS:000899588000001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofProceedings Of The Institution Of Mechanical Engineers Part P-Journal Of Sports Engineering And Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLeague Rank Predictionen_US
dc.subjectSocceren_US
dc.subjectRanking Analysisen_US
dc.subjectMatch Analysisen_US
dc.subjectMatch Predictionsen_US
dc.subjectTurkish Super Leagueen_US
dc.subjectBig Dataen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectRadial Basic Functionen_US
dc.subjectMulti-Layer Regressionen_US
dc.titlePrediction of soccer clubs' league rankings by machine learning methods: The case of Turkish Super Leagueen_US
dc.typeArticleen_US

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