Abstract
Over the last number of years, sports analytics has become more popular in supporting personnel decisions, evaluating player and team performances, and predicting game results in various sports. One of the most traditional sports, football is also modernizing its ways based on sports analytics techniques. The purpose of this study is to propose a football match prediction model for Turkish Super League (TSL) using supervised machine learning techniques. To do this, based on the TSL data of last five years (2013 to 2018), game result prediction models were established using classification techniques including logistics regression, linear and quadratic discriminant analyses, K-nearest neighbors, support vector machines, and random forests. An ensemble of 10 models based on seven different techniques is suggested.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Loeffelholz, B., Bednar, E., Bauer, K.W.: Predicting NBA games using neural networks. J. Quant. Anal. Sport. 5(1), (2009)
Kumar, G: Machine learning for soccer analytics. University of Leuven (2013)
Ulmer, B., Fernandez, M., Peterson, M.: Predicting soccer match results in the english premier league. Doctoral dissertation Ph. D. dissertation, Stanford (2013)
Pettersson, D., Nyquist, R.: Football match prediction using deep learning (2017)
Torres, R.A.: Prediction of NBA Games Based on Machine Learning Methods. University of Wisconsin, Madison (2013)
Joseph, A., Fenton, N.E., Neil, M.: Predicting football results using Bayesian nets and other machine learning techniques. Knowl.-Based Syst. 19(7), 544–553 (2006)
Huang, K.Y., Chang, W.L.: A neural network method for prediction of 2006 world cup football game. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE, July 2010
Kampakis, S., Adamides, A.: Using Twitter to predict football outcomes. arXiv preprint arXiv:1411.1243. (2014)
Delen, D., Cogdell, D., Kasap, N.: A comparative analysis of data mining methods in predicting NCAA bowl outcomes. Int. J. Forecast. 28(2), 543–552 (2012)
James, G., Witten, D., Trevor, H., Robert, T.: An introduction to statistical learning-with Applications in R, pp. 1–440. Springer, New York, Heidelberg, Dordrecht, London (2013)
Lantz, B.: Machine Learning with R, pp. 65–248. Packt Publishing Ltd., Birmingham (2013)
Football-data historical data: http://www.football-data.co.uk/. Accessed 25 Oct 2018
Football Manager Games: https://www.footballmanager.com/. Accessed 15 Dec 2018
Stata: https://www.stata.com/links/examples-and-datasets/. Accessed 15 Dec 2018
Mackolik: https://www.mackolik.com. Accessed 20 Dec 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Saricaoğlu, A.E., Aksoy, A., Kaya, T. (2020). Prediction of Turkish Super League Match Results Using Supervised Machine Learning Techniques. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A., Sari, I. (eds) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-23756-1_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-23756-1_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23755-4
Online ISBN: 978-3-030-23756-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)