Factors Associated with Match Result and Number of Goals Scored and Conceded in the English Premier League

Year 2022, , 227 - 236, 24.03.2022

Günal Bilek , Betul Aygun

https://doi.org/10.17798/bitlisfen.1015215

Abstract

The aim of this research is to identify the factors associated with match result and number of goals scored and conceded and in the English Premier League. The data consist of 17 performance indicators and situational variables of the matches in the English Premier League for the season of 2017-18. Poisson regression model was implemented to identify the significant factors in the number of goals scored and conceded, while multinomial logistic regression and support vector machine methods were used to determine the influential factors on the match result. It was found that scoring first, shots on target and goals conceded have significant influence on the number of goals scored, whereas scoring first, match location, quality of opponent, goals conceded, shots and clearances are influential on the number of goals conceded. On the other hand, scoring first, match location, shots, shot on target, clearances and quality of opponent significantly affect the probability of losing; while scoring first, match location, shots, shots on target and possession affect the probability of winning. In addition, among all the variables studied, scoring first is the only variable appearing important in all the analyses, making it the most significant factor for success in football.

Keywords

Support vector machine, Multinomial logistic regression, Poisson regression, Machine learning, Performance analysis, Football

Factors Associated with Match Result and Number of Goals Scored and Conceded in the English Premier League

Abstract

The aim of this research is to identify the factors associated with the match result and the number of goals scored and conceded in the English Premier League. The data consist of 17 performance indicators and situational variables of the football matches in the English Premier League for the season of 2017-18. Poisson regression model was implemented to identify the significant factors in the number of goals scored and conceded, while multinomial logistic regression and support vector machine methods were used to determine the influential factors on the match result. It was found that scoring first, shots on target and goals conceded have significant influence on the number of goals scored, whereas scoring first, match location, quality of opponent, goals conceded, shots and clearances are influential on the number of goals conceded. On the other hand, scoring first, match location, shots, shot on target, clearances and quality of opponent significantly affect the probability of losing; while scoring first, match location, shots, shots on target and possession affect the probability of winning. In addition, among all the variables studied, scoring first is the only variable appearing important in all the analyses, making it the most significant factor for success in football.

Keywords

Support Vector Machine, Multinomial Logistic Regression, Poisson regression, machine learning, performance analysis, football

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