An enhanced Bayesian Network prediction model for football matches based on player performance

In sports analytics, existing researches have showed that the Bayesian networks (BN) approach has greatly contributed to predicting football match results with considerably high accuracy as compared to other classical statistical and machine learning approaches. However, existing prediction model...

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Main Author:	Razali, Muhammad Nazim
Format:	Thesis
Language:	English English
Published:	2017
Subjects:	QA273-280 Probabilities Mathematical statistics
Online Access:	http://eprints.uthm.edu.my/832/1/24p%20MUHAMMAD%20NAZIM%20RAZALI.pdf http://eprints.uthm.edu.my/832/2/MUHAMMAD%20NAZIM%20RAZALI%20WATERMARK.pdf
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spelling	my-uthm-ep.8322021-09-06T02:43:48Z An enhanced Bayesian Network prediction model for football matches based on player performance 2017-12 Razali, Muhammad Nazim QA273-280 Probabilities. Mathematical statistics In sports analytics, existing researches have showed that the Bayesian networks (BN) approach has greatly contributed to predicting football match results with considerably high accuracy as compared to other classical statistical and machine learning approaches. However, existing prediction models rely solely on historical team features including the match statistical data as well as team statistical data, together with the historical features of team achievement such as ranking in FIFA, ranking in league and total number of points gained at the end of a season. There is no known work to date that has analysed individual player performance data as part of the parameters used to predict football match results. To address this gap, this research proposes a BN model for match prediction based on player performance data called the Player Performance (PP) model. To validate the performance of the proposed PP model, three existing prediction models were re-implemented and measured for prediction accuracy. The existing models are the General Individual (GI) model, Match Statistical (MS) model, and Team Statistical (TS) model. All BN models were constructed using the Tree Augmented Naive Bayes (TAN) for structural learning. The dataset used was data for the Arsenal Football Club in the English Premier League (EPL) for seasons 2014-2015 and 2015-2016. Apart from the proposed individual player performance data, the dataset includes individual player rating, absence or presence of players in a match, match statistics, and team statistics. Then, the PP model were re-constructed using other machine learning techniques such as k-Nearest Neighbour (kNN) and Decision Tree (DT) in order to compare with BN for prediction accuracy. The experimental results showed two fold; the proposed PP model using BN achieved a higher accuracy in predicting the outcomes for football matches with an overall average predictive accuracy of 63.76% compare to GI model, MS model and TS model as well as higher than PP model using kNN and DT by 1.64% and 6.02%. 2017-12 Thesis http://eprints.uthm.edu.my/832/ http://eprints.uthm.edu.my/832/1/24p%20MUHAMMAD%20NAZIM%20RAZALI.pdf text en public http://eprints.uthm.edu.my/832/2/MUHAMMAD%20NAZIM%20RAZALI%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English
topic	QA273-280 Probabilities Mathematical statistics
spellingShingle	QA273-280 Probabilities Mathematical statistics Razali, Muhammad Nazim An enhanced Bayesian Network prediction model for football matches based on player performance
description	In sports analytics, existing researches have showed that the Bayesian networks (BN) approach has greatly contributed to predicting football match results with considerably high accuracy as compared to other classical statistical and machine learning approaches. However, existing prediction models rely solely on historical team features including the match statistical data as well as team statistical data, together with the historical features of team achievement such as ranking in FIFA, ranking in league and total number of points gained at the end of a season. There is no known work to date that has analysed individual player performance data as part of the parameters used to predict football match results. To address this gap, this research proposes a BN model for match prediction based on player performance data called the Player Performance (PP) model. To validate the performance of the proposed PP model, three existing prediction models were re-implemented and measured for prediction accuracy. The existing models are the General Individual (GI) model, Match Statistical (MS) model, and Team Statistical (TS) model. All BN models were constructed using the Tree Augmented Naive Bayes (TAN) for structural learning. The dataset used was data for the Arsenal Football Club in the English Premier League (EPL) for seasons 2014-2015 and 2015-2016. Apart from the proposed individual player performance data, the dataset includes individual player rating, absence or presence of players in a match, match statistics, and team statistics. Then, the PP model were re-constructed using other machine learning techniques such as k-Nearest Neighbour (kNN) and Decision Tree (DT) in order to compare with BN for prediction accuracy. The experimental results showed two fold; the proposed PP model using BN achieved a higher accuracy in predicting the outcomes for football matches with an overall average predictive accuracy of 63.76% compare to GI model, MS model and TS model as well as higher than PP model using kNN and DT by 1.64% and 6.02%.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Razali, Muhammad Nazim
author_facet	Razali, Muhammad Nazim
author_sort	Razali, Muhammad Nazim
title	An enhanced Bayesian Network prediction model for football matches based on player performance
title_short	An enhanced Bayesian Network prediction model for football matches based on player performance
title_full	An enhanced Bayesian Network prediction model for football matches based on player performance
title_fullStr	An enhanced Bayesian Network prediction model for football matches based on player performance
title_full_unstemmed	An enhanced Bayesian Network prediction model for football matches based on player performance
title_sort	enhanced bayesian network prediction model for football matches based on player performance
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2017
url	http://eprints.uthm.edu.my/832/1/24p%20MUHAMMAD%20NAZIM%20RAZALI.pdf http://eprints.uthm.edu.my/832/2/MUHAMMAD%20NAZIM%20RAZALI%20WATERMARK.pdf
_version_	1747830689282129920

An enhanced Bayesian Network prediction model for football matches based on player performance

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