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Publicação
Predicting soccer outcome with machine learning based on weather condition
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| 1.9 MB | Adobe PDF |
Autores
Resumo(s)
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.
Descrição
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
Palavras-chave
Weather Soccer Football Machine Learning K-nearest neighbors Support vector machine Random Forest