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Predicting soccer outcome with machine learning based on weather condition
dc.contributor.author | Palinggi, Denny Asarias | |
dc.contributor.other | Ramos Romero, Francisco | |
dc.contributor.other | Universitat Jaume I. Departament de Llenguatges i Sistemes Informàtics | |
dc.date.accessioned | 2019-04-10T07:04:13Z | |
dc.date.available | 2019-04-10T07:04:13Z | |
dc.date.issued | 2019-03 | |
dc.identifier.uri | http://hdl.handle.net/10234/182273 | |
dc.description | Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2017-2018 | ca_CA |
dc.description.abstract | Massive amounts of research have been doing on predicting soccer matches using machine learning algorithms. Unfortunately, there are no prior researches used weather condition as features. In this thesis, three different classification algorithms were investigated for predicting the outcomes of soccer matches by using temperature difference, rain precipitation, and several other historical match statistics as features. The dataset consists of statistic information of soccer matches in La Liga and Segunda division from season 2013-2014 to 2016-2017 and weather information in every host cities. The results show that the SVM model has better accuracy score for predicting the full-time result compare to KNN and RF with 45.32% for temperature difference below 5° and 49.51% for temperature difference above 5°. For over/under 2.5 goals, SVM also has better accuracy with 53.07% for rain precipitation below 5 mm and 56% for rain precipitation above 5 mm. | ca_CA |
dc.format.extent | 60 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Universitat Jaume I | ca_CA |
dc.rights | Atribución-NoComercial-CompartirIgual 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.subject | Màster Universitari Erasmus Mundus en Tecnologia Geoespacial | ca_CA |
dc.subject | Erasmus Mundus University Master's Degree in Geospatial Technologies | ca_CA |
dc.subject | Máster Universitario Erasmus Mundus en Tecnología Geoespacial | ca_CA |
dc.subject | weather | ca_CA |
dc.subject | soccer | ca_CA |
dc.subject | football | ca_CA |
dc.subject | machine learning | ca_CA |
dc.subject | K-nearest neighbors | ca_CA |
dc.subject | support vector machine | ca_CA |
dc.subject | random forest | ca_CA |
dc.title | Predicting soccer outcome with machine learning based on weather condition | ca_CA |
dc.type | info:eu-repo/semantics/masterThesis | ca_CA |
dc.educationLevel | Estudios de Postgrado | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
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TFM: Màster Universitari Erasmus Mundus en Tecnologia Geoespacial [79]
SIW013; SIK013