Abstract
Nowadays predictive models or prediction of results in any sports become popular in data mining community, and particularly, English Premier League (EPL) in football gains way much attention in the past few years. There are three main approaches to predict the results: statistical approaches, machine learning approaches, and the Bayesian approaches. In this paper, the approach used is machine learning and evaluating all features that influences the results and attempts to choose the most significant features that lead a football team to win, lose, or draw and even considering the top teams. This predictive model basically helps in betting areas and also for managers to have a knowledge how to set up their team by analyzing the results also companies like StatsBomb which use these kinds of tools for setting up scouting networks for searching of hidden gems throughout the world. These features help predict the best possible outcome of the EPL matches using these classifiers logistic regression, support vector machine, random forest, and XGBoost; the data used for prediction is selected from the Web site: datahub.io, and the model is based on the data of last ten seasons of EPL. K-fold cross-validation is used to describe the accuracy of the model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ulmer B, Fernandez M, Peterson M (2013) Predicting soccer match results in the English Premier League. Doctoral dissertation, Ph. D. thesis, Doctoral dissertation, Ph. D. dissertation, Stanford
Igiri CP (2015) Support vector machine–based prediction system for a football match result. IOSR J Comput Eng (IOSR-JCE) 17(3):21–26
Miljković D, Gajić L, Kovačević A, Konjović Z (2010) The use of data mining for basketball matches outcomes prediction. In: IEEE 8th international symposium on intelligent systems and informatics. IEEE, pp 309–312
Baboota R, Kaur H (2019) Predictive analysis and modeling football results using machine learning approach for English Premier League. Int J Forecast 35(2):741–755
Acharya A, Sinha D (2014) Early prediction of student’s performance using machine learning techniques. Int J Comput Appl 107(1)
Khan W, Ghazanfar MA, Azam MA et al (2020) Stock market prediction using machine learning classifiers and social media, news. J Ambient Intell Human Comput
Razali N, Mustapha A, Yatim FA, Ab Aziz R (2017) Predicting football matches results using Bayesian networks for English premier league (EPL). In: Iop conference series: materials science and engineering, vol 226, no 1, pp 012099. IOP Publishing
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ranjan, A., Kumar, V., Malhotra, D., Jain, R., Nagrath, P. (2021). Predicting the Result of English Premier League Matches. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Ganzha, M., Rodrigues, J.J.P.C. (eds) Proceedings of Second International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems, vol 203. Springer, Singapore. https://doi.org/10.1007/978-981-16-0733-2_30
Download citation
DOI: https://doi.org/10.1007/978-981-16-0733-2_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-0732-5
Online ISBN: 978-981-16-0733-2
eBook Packages: EngineeringEngineering (R0)