Predicting Football Outcomes

with Bayesian Networks

Bachelor thesis (2019)

Authors

M. van Dijk Electrical Engineering, Mathematics and Computer Science

Contributors

G.F. Nane Applied Probability - EEMCS (mentor)
E.M. van Elderen Mathematical Physics - EEMCS (graduation committee member)
L.J.J. van Iersel Discrete Mathematics and Optimization - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2019 Max van Dijk
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Published Date

11-07-2019

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In this thesis Bayesian Networks are used to predict European football matches between the years 2008 and 2016. The goal of this research is to see how the structures learned by different Bayesian Network learning algorithms influences the predictions. First the data is explored and modified to be used for Bayesian Networks and secondly the theory is explained using examples. Finally the theory is applied on the data and the structures are learned with the help of a bootstrap method and the predictions are validated using 5-fold cross validation. We can conclude that the networks learned by the algorithms and with the help of an expert give a good representation of the underlying relationships, but are not very good in prediction the end result.

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