Predicting the Duration of Professional Tennis Matches Using MLR, CART, SVR and ANN Techniques

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dc.authoridPeker, Serhat/0000-0002-6876-3982
dc.contributor.authorDuen, Serdar
dc.contributor.authorPeker, Serhat
dc.date.accessioned2025-03-20T09:51:24Z
dc.date.available2025-03-20T09:51:24Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.descriptionInternational Conference on Intelligent and Fuzzy Systems (INFUS) -- JUL 16-18, 2024 -- Istanbul Tech Univ, Canakkale, TURKEY
dc.description.abstractThis research aims to predict the duration of professional tennis matches by utilizing a dataset that includes player statistics, match characteristics and court attributes. Various machine learning techniques, such as multiple linear regression (MLR), classification and regression trees (CART), support vector regression (SVR) and artificial neural networks (ANN), are applied for this purpose. The study involves a comprehensive dataset spanning professional tournaments from 1993 to 2022. Separate predictive models were developed for tournaments played over 3 and 5 sets employing the corresponding ML techniques and their performances were compared. The findings revealed that the predictive models with MLR and SVR methods excel in best-of-3 set matches, while the ones with SVR and ANN exhibit superior performance for best-of-5 set matches. This research contributes valuable insights into the factors influencing match duration and aids in developing more effective predictive models for tennis events.
dc.description.sponsorshipCanakkale Onsekiz Mart Univ
dc.identifier.doi10.1007/978-3-031-70018-7_37
dc.identifier.endpage340
dc.identifier.isbn978-3-031-70017-0
dc.identifier.isbn978-3-031-70018-7
dc.identifier.issn2367-3370
dc.identifier.issn2367-3389
dc.identifier.scopus2-s2.0-85203595499
dc.identifier.scopusqualityQ4
dc.identifier.startpage332
dc.identifier.urihttps://doi.org/10.1007/978-3-031-70018-7_37
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2530
dc.identifier.volume1088
dc.identifier.wosWOS:001331332200037
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer International Publishing Ag
dc.relation.ispartofIntelligent and Fuzzy Systems, Infus 2024 Conference, Vol 1
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250319
dc.subjectMachine Learning
dc.subjectRegression-based Prediction
dc.subjectSports Analytics
dc.subjectPredictive Modeling in Tennis
dc.subjectData Analytics in Tennis
dc.titlePredicting the Duration of Professional Tennis Matches Using MLR, CART, SVR and ANN Techniques
dc.typeConference Object

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