Predicting Football Match Outcomes with Machine Learning Approaches

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dc.contributor.authorChoi, Bing Shen
dc.contributor.authorFoo, Lee Kien
dc.contributor.authorChua, Sook-Ling
dc.coverage.issue2cs
dc.coverage.volume29cs
dc.date.accessioned2024-01-11T09:48:06Z
dc.date.available2024-01-11T09:48:06Z
dc.date.issued2023-12-31cs
dc.description.abstractThe increasing use of data-driven approaches has led to the development of models to predict football match outcomes. However, predicting match outcomes accurately remains a challenge due to the sport's inherent unpredictability. In this study, we have investigated the usage of different machine learning models in predicting the outcome of English Premier League matches. We assessed the performance of random forest, logistic regression, linear support vector classifier and extreme gradient boosting models for binary and multiclass classification. These models are trained with datasets obtained using different sampling techniques. The result showed that the models performed better when trained with dataset obtained using a balanced sampling technique for binary classification. Additionally, the models' predictions were evaluated by conducting simulation on football betting profits based on the 2022-2023 EPL season. The model achieved the highest accuracy is the binary class random forest, but the model provided the highest football betting profit is the binary class logistic regression.en
dc.formattextcs
dc.format.extent229-236cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2023 vol. 29, č. 2, s. 229-236. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2023.2.229en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttps://hdl.handle.net/11012/244250
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/263cs
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0en
dc.subjectClassificationen
dc.subjectMachine Learningen
dc.subjectSampling Techniquesen
dc.subjectMulticlassen
dc.subjectBinaryen
dc.subjectFootball Predictionen
dc.titlePredicting Football Match Outcomes with Machine Learning Approachesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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Vol. 29, No. 2