Journal Article

·2021 OPEN ACCESS

Modelling Sport Events with Supervised Machine Learning

İrem BARMAN YTU , İbrahim Demir YTU

Fundamental Journal of Mathematics and Applications

Abstract

It has been very important to understand the change of multivariable systems to make predictions accordingly. The goal of supervised machine learning is to build a model of changing classes of observations depending on various variables and to make predictions about the coming situations. Due to the fact that sports are followed by the whole world modelling sports events and studies about predicting the results of future matches have gained importance. In this study, match statistics of the teams in the Turkey Super League were used, and it was examined how successfully the outcome of the match was predicted using a decision tree, random forest, k-nearest neighbor, naive Bayes, support vector machine. According to the tests done in Turkey Super League, the support vector machine performs the best.

Keywords

Machine learning Decision tree Naive Bayes classifier Random forest Artificial intelligence Outcome (game theory) League Support vector machine Computer science Mathematics

Subject Areas

Sports Analytics and Performance ·Economics and Econometrics ·Social Sciences
Data Mining Algorithms and Applications ·Information Systems ·Physical Sciences
Sports Performance and Training ·Orthopedics and Sports Medicine ·Health Sciences

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