Predicting soccer outcome with machine learning based on weather condition
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/158176
comunitat-uji-handle2:10234/71345
comunitat-uji-handle3:10234/141145
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TFG-TFMMetadatos
Título
Predicting soccer outcome with machine learning based on weather conditionAutoría
Tutor/Supervisor; Universidad.Departamento
Ramos Romero, Francisco; Universitat Jaume I. Departament de Llenguatges i Sistemes InformàticsFecha de publicación
2019-03Editor
Universitat Jaume IResumen
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 ... [+]
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. [-]
Palabras clave / Materias
Màster Universitari Erasmus Mundus en Tecnologia Geoespacial | Erasmus Mundus University Master's Degree in Geospatial Technologies | Máster Universitario Erasmus Mundus en Tecnología Geoespacial | weather | soccer | football | machine learning | K-nearest neighbors | support vector machine | random forest
Descripción
Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2017-2018
Tipo de documento
info:eu-repo/semantics/masterThesisDerechos de acceso
info:eu-repo/semantics/openAccess
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