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

Razali, Muhammad Nazim (2017) An enhanced Bayesian Network prediction model for football matches based on player performance. Masters thesis, Universiti Tun Hussein Onn Malaysia.

Preview	Text 24p MUHAMMAD NAZIM RAZALI.pdf Download (314kB) | Preview
	Text (Full Text) MUHAMMAD NAZIM RAZALI WATERMARK.pdf Restricted to Registered users only Download (2MB) | Request a copy

Abstract

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%.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics
Divisions:	Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User:	Mrs. Sabarina Che Mat
Date Deposited:	06 Sep 2021 02:43
Last Modified:	06 Sep 2021 02:43
URI:	http://eprints.uthm.edu.my/id/eprint/832

Actions (login required)

View Item