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dc.contributor.authorFiliz, Enes
dc.date.accessioned2023-11-07T08:09:45Z
dc.date.available2023-11-07T08:09:45Z
dc.date.issued2022en_US
dc.identifier.issn0270-1367 / 2168-3824
dc.identifier.urihttps://doi.org/10.1080/02701367.2022.2053647
dc.identifier.urihttps://hdl.handle.net/20.500.12462/13613
dc.description.abstractABSTARCT Purpose: Football, one of the most popular and loved sports branches, always keeps its excitement, ambition, passion, joy and sadness together. European football, the football capital, is an attraction for fans and footballers. In this study, the official match results (league, country cup, European cup) of five successful football clubs (Bayern Munchen, Barcelona, Juventus, Manchester City, Paris Saint Germain) in the five major leagues of European football (La Liga, Premier League, Serie A, Bundesliga, Ligue 1) were evaluated. Method: For this analysis, ensemble learning algorithms (AdaBoost, Bagging) and machine learning algorithms (Naive Bayes, artificial neural networks, K-nearest neighbor, C4.5/Random forest/Reptree decision tree) were used. In addition, the attributes that play an active role in the classification of the match results of five successful football clubs were determined with the Symmetrical Uncertainty feature selection algorithm. Results: As effective attributes, "Conceded goal," "Half time result," "Scoring first" and "Shooting accuracy" attributes revealed to be common for five successful football clubs. In general, it was observed that ensemble learning algorithms gave successful results and AdaBoost/ANN algorithm was determined as the most successful. On the basis of football clubs, the most successful classification result was achieved for Barcelona with a rate of 99.3%. Conclusions: Obtained outputs from Ensemble learning and feature selection help sport researchers and football club planners understand and revise the match results of current football match strategies. The study has mainly twofold: to find best performer ensemble and machine learning algorithm(s) for classifying match results and to extract important features on match results.en_US
dc.language.isoengen_US
dc.publisherRoutledge Journals, Taylor & Francis Ltden_US
dc.relation.isversionof10.1080/02701367.2022.2053647en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectEnsemble Learningen_US
dc.subjectEuropean Footballen_US
dc.subjectFeature Selectionen_US
dc.subjectMatch Resulten_US
dc.titleEvaluation of match results of five successful football clubs with ensemble learning algorithmsen_US
dc.typearticleen_US
dc.relation.journalResearch Quarterly for Exercise and Sporten_US
dc.contributor.departmentİktisadi ve İdari Bilimler Fakültesien_US
dc.contributor.authorID0000-0002-8006-9467en_US
dc.identifier.volume94en_US
dc.identifier.issue3en_US
dc.identifier.startpage773en_US
dc.identifier.endpage782en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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