The Effect of Weather in Soccer Results: An Approach Using Machine Learning Techniques
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Título
The Effect of Weather in Soccer Results: An Approach Using Machine Learning TechniquesFecha de publicación
2020-09-26Editor
MDPIISSN
2076-3417Cita bibliográfica
IIskandaryan, D.; Ramos, F.; Palinggi, D.A.; Trilles, S. The Effect of Weather in Soccer Results: An Approach Using Machine Learning Techniques. Appl. Sci. 2020, 10, 6750Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.mdpi.com/2076-3417/10/19/6750/htmVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
The growing popularity of soccer has led to the prediction of match results becoming of interest to the research community. The aim of this research is to detect the effects of weather on the result of matches by ... [+]
The growing popularity of soccer has led to the prediction of match results becoming of interest to the research community. The aim of this research is to detect the effects of weather on the result of matches by implementing Random Forest, Support Vector Machine, K-Nearest Neighbors Algorithm, and Extremely Randomized Trees Classifier. The analysis was executed using the Spanish La Liga and Segunda division from the seasons 2013–2014 to 2017–2018 in combination with weather data. Two tasks were proposed as part of this study: the first was to find out whether the game will end in a draw, a win by the hosts or a victory by the guests, and the second was to determine whether the match will end in a draw or if one of the teams will win. The results show that, for the first task, Extremely Randomized Trees Classifier is a better method, with an accuracy of 65.9%, and, for the second task, Support Vector Machine yielded better results with an accuracy of 79.3%. Moreover, it is possible to predict whether the game will end in a draw or not with 0.85 AUC-ROC. Additionally, for comparative purposes, the analysis was also performed without weather data. [-]
Publicado en
Applied Sciences, 2020, vol. 10, no 19Proyecto de investigación
Universitat Jaume I: PREDOC/2018/61; Ministry of Science and Innovation-Spanish government: IJC2018-035017-I; Generalitat Valenciana: GV/2020/035Derechos de acceso
info:eu-repo/semantics/openAccess
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