TY - GEN
T1 - Match outcomes prediction of six top english premier league clubs via machine learning technique
AU - Musa, Rabiu Muazu
AU - P. P. Abdul Majeed, Anwar
AU - Mohd Razman, Mohd Azraai
AU - Shaharudin, Mohd Ali Hanafiah
N1 - Funding Information:
Acknowledgement. The authors would like to gratefully acknowledge Universiti Malaysia Pahang for funding this study via RDU 180321.
Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2019.
PY - 2019
Y1 - 2019
N2 - The English Premier League (EPL) is one of the most widely covered league in the world. The prediction of football matches, particularly EPL has received due attention over the past two decades by means of both conventional statistical and machine learning approaches. More often than not, the predictions reported in the literature have rather been dissatisfactory in forecasting the outcome of the matches. This work offers a unique approach in predicting EPL match outcomes, i.e., win, lose or draw by considering top six teams in the league namely Manchester United, Manchester City, Liverpool, Arsenal, Chelsea and Tottenham Hotspur over the span of four consecutive seasons from 2013 to 2016. Fifteen features were selected based on their relevance to the game. Six different Support Vector Machine (SVM) model variations viz. linear, quadratic, cubic, fine radial basis function (RBF), medium RBF, as well as course RBF were developed to predict the match outcomes. A five-fold cross-validation technique was employed whilst, a separate fresh data was supplied to the best model developed in evaluating the predictive efficacy of the model. It was demonstrated from the study that the linear SVM model provided an excellent prediction accuracy of 100% on both the trained as well as untrained data. Therefore, it could be concluded that the selection of the relevant features, as well as the methodology employed, could yield a reliable prediction of top six EPL clubs match outcomes.
AB - The English Premier League (EPL) is one of the most widely covered league in the world. The prediction of football matches, particularly EPL has received due attention over the past two decades by means of both conventional statistical and machine learning approaches. More often than not, the predictions reported in the literature have rather been dissatisfactory in forecasting the outcome of the matches. This work offers a unique approach in predicting EPL match outcomes, i.e., win, lose or draw by considering top six teams in the league namely Manchester United, Manchester City, Liverpool, Arsenal, Chelsea and Tottenham Hotspur over the span of four consecutive seasons from 2013 to 2016. Fifteen features were selected based on their relevance to the game. Six different Support Vector Machine (SVM) model variations viz. linear, quadratic, cubic, fine radial basis function (RBF), medium RBF, as well as course RBF were developed to predict the match outcomes. A five-fold cross-validation technique was employed whilst, a separate fresh data was supplied to the best model developed in evaluating the predictive efficacy of the model. It was demonstrated from the study that the linear SVM model provided an excellent prediction accuracy of 100% on both the trained as well as untrained data. Therefore, it could be concluded that the selection of the relevant features, as well as the methodology employed, could yield a reliable prediction of top six EPL clubs match outcomes.
KW - Feature selection
KW - Football
KW - Match outcome
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=85065102748&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-7780-8_20
DO - 10.1007/978-981-13-7780-8_20
M3 - Conference Proceeding
AN - SCOPUS:85065102748
SN - 9789811377792
T3 - Communications in Computer and Information Science
SP - 236
EP - 244
BT - Robot Intelligence Technology and Applications - 6th International Conference, RiTA 2018, Revised Selected Papers
A2 - Kim, Jong-Hwan
A2 - Myung, Hyung
A2 - Lee, Seung-Mok
PB - Springer Verlag
T2 - 6th International Conference on Robot Intelligence Technology and Applications, RiTA 2018
Y2 - 16 December 2018 through 18 December 2018
ER -