THE EFFECTS OF DECOMPOSITION OF THE GOALS SCORED IN CLASSIFYING THE OUTCOMES OF FIVE ENGLISH PREMIER LEAGUE SEASONS USING MACHINE LEARNING MODELS

Iyiola, Tomilayo Promise and Okagbue, H. I. and Adedotun, Adedayo F. and Akingbade, Toluwalase J. (2023) THE EFFECTS OF DECOMPOSITION OF THE GOALS SCORED IN CLASSIFYING THE OUTCOMES OF FIVE ENGLISH PREMIER LEAGUE SEASONS USING MACHINE LEARNING MODELS. Advances and Applications in Statistics, 87 (1). pp. 13-27. ISSN 0972-3617

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Abstract

The English Premier League (EPL) is one of the best football championships in the world and thus, data generated from it is highly sought after by users of football data. One of the uses of the data is in the prediction of outcome of the league matches. This paper applies four machine learning (ML) models in classifying the outcome (home win, draw, and away win) of five consecutive seasons of EPL using only six independent variables. Information Gain Ratio (IGR) and ReliefF were the feature selection algorithms that reduced the independent variables from 16 to 6. Spearman rank correlation gave a high significant positive correlation between the ranks of the 2 feature selection algorithms. The Kruskal-Wallis H test indicated that there is a significant difference in the dependent variable between the different Seasons (Chi-square = 15.36, Degrees of freedom = 4, P = 0.004). Adaptive boosting (AB), gradient boosting (GB), logistic regression (LR) and random forests (RF) were used in the classification of the outcome using the six independent variables and the performance metrics showed a perfect classification in almost all the models. This paper concluded that the knowledge of the number of goals scored by the home and away teams, and the number of Goals scored by home and away teams in the first half and second half are all that is needed to correctly classify the outcomes of the English Premier League (EPL). Secondly, the knowledge of the own goals and goals scored by penalty, and yellow and red cards conceded by the home or away teams is not necessarily needed in the determination or prediction of the outcomes of the EPL.

Item Type:	Article
Uncontrolled Keywords:	algorithm, classification, cross-validation, English Premier League, feature selection, machine learning, statistics.
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Engineering, Science and Mathematics > School of Mathematics
Depositing User:	nwokealisi
Date Deposited:	13 Nov 2023 10:37
Last Modified:	13 Nov 2023 10:37
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/17593

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