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...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 語言: | English English |
| 出版: |
2017
|
| 主題: | |
| 在線閱讀: | http://eprints.uthm.edu.my/832/1/24p%20MUHAMMAD%20NAZIM%20RAZALI.pdf http://eprints.uthm.edu.my/832/2/MUHAMMAD%20NAZIM%20RAZALI%20WATERMARK.pdf |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | 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%. |
|---|