Download pdf
Sports Betting: An Application of Machine Learning to the Game Prediction
- 1 Tongji University, School of Economics and Management, Shanghai ,200000, China
- 2 Shanghai Ocean University, Aien institute, Shanghai, 201306, China
- 3 Jinan University, University of Birmingham Joint Institute, Guangdong, 511443, China
- 4 Hangzhou, China
* Author to whom correspondence should be addressed.
Abstract
The study investigates the use of machine learning to predict the results of football matches, with its main goal being to enhance the effectiveness of sports betting techniques.An assessment of diverse machine learning methods was carried out by analyzing a comprehensive dataset that included European league games spanning 2008 to 2016, such as Random Forest, Gaussian Process Regression, Logistic Regression, K-Nearest Neighbors, AdaBoost, XGBoost, and LightGBM. Our results revealed that the LightGBM and Ada model exhibited great performance, achieving an accuracy of 52.6% and 52.8% in predicting match outcomes.Moreover, we incorporated the concept of double chances into our analysis and a simulation-based betting strategy was used in our model, demonstrating a 3% profit margin. This study demonstrates a hopeful potential of machine learning. It is very useful in sports analysis and betting. At the same time, the study talks about some limitations. It also suggests directions for future research.
Keywords
Machine Learning, football, match prediction, odd, sports betting
[1]. ubitzky, W., Lopes, P., Davis, J. et al. The Open International Soccer Database for machine learning. Mach Learn, 2019 108, 9-28. https://doi.org/10.1007/s10994-018-5726-0
[2]. Etuk, R., Xu, T., Abarbanel, B., Potenza, M. N., & Kraus, S. W. Sports betting around the world: A systematic review. Journal of Behavioral Addictions 2022, 11,3,689-715. https://doi.org/10.1556/2006.2022.00064
[3]. Goes, F. R., Meerhoff, L. A., Bueno, M. J. O., Rodrigues, D. M., Moura, F. A., Brink, M. S. ElferinkGemser, M.T., Knobbe, A.J., Cunha, S.A., Torres, R.S., Lemmink, K. A. P. M., Unlocking the potential of big data to support tactical performance analysis in professional soccer: A systematic review. European Journal of Sport Science 2020, 21(4), 481-496.
[4]. Heuer, A. Der perfekte Tipp: Statistik des Fussballspiels, Wiley-VCH 2012, Weinheim.
[5]. Raquel Y.S. Aoki, Renato M. Assuncao, and Pedro O.S. Vaz de Melo. 2017. Luck is Hard to Beat: The Difficulty of Sports Prediction. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '17). Association for Computing Machinery, New York, NY, USA, 1367–1376. https://doi.org/10.1145/3097983.3098045
[6]. Yuan, L., Liu, A., Yeh, A., Kaufman, A., Reece, A., Bull, P., Franks, A., Wang, S., Illushin, D. & Bornn, L. (2015). A mixture-of-modelers approach to forecasting NCAA tournament outcomes. Journal of Quantitative Analysis in Sports, 11(1), 13-27. https://doi.org/10.1515/jqas-2014-0056
[7]. Wang, Junjian, [Retracted] Mining and Prediction of Large Sport Tournament Data Based on Bayesian Network Models for Online Data, Wireless Communications and Mobile Computing, 2022, 1211015, 8 pages, 2022. https://doi.org/10.1155/2022/1211015
[8]. Zhang Shaoliang, Yang Banban, Chen Lei. Research on the Prediction Model of Chinese Football Super League Driven by Ensemble Learning. Proceedings of the 13th National Conference on Sports Science - Special Reports (Sports Statistics Section), 2023.
[9]. Miao, Zhongli, Hu, Youhong, [Retracted] Research and Analysis of Combination Forecasting Model in Sports Competition, Journal of Sensors, 2022, 5945599, 10 pages, 2022. https://doi.org/10.1155/2022/5945599
[10]. Sun Chunjie. Analysis and Prediction of NBA Game Results Based on Explainable Machine Learning [D]. Jiangsu Province: Soochow University, 2023. DOI: 10.27351/d.cnki.gszhu.2023.001979.
[11]. Fischer M, Heuer A. Match predictions in soccer: Machine learning vs. Poisson approaches[J]. arXiv preprint arXiv:2408.08331, 2024.
[12]. Kumar, G. Machine Learning for Soccer Analytics, PhD Thesis 2013, 10.13140/RG.2.1.4628.3761
[13]. Constantinou, A.C. Dolores: a model that predicts football match outcomes from all over the world. Mach Learn 2019, 108, 49-75. https://doi.org/10.1007/s10994-018-5703-7
[14]. Mendes-Neves, T., Mendes-Moreira, J. Comparing State-of-the-Art Neural Network Ensemble Methods in Soccer Predictions. In: Helic, D., Leitner, G., Stettinger, M., Felfernig, A., Raś, Z.W. (eds) Foundations of Intelligent Systems 2020. ISMIS 2020. Lecture Notes in Computer Science(), vol 12117. Springer, Cham. https://doi.org/10.1007/978-3-030-59491-6_13
[15]. Stübinger, J.; Mangold, B.; Knoll J. Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics. Appl. Sci. 2022, 10, 46, https://doi.org/10.3390/app10010046
[16]. Berrar, D., Lopes, P., Dubitzky, W. Incorporating Domain Knowledge in Machine Learning For Soccer Outcome Prediction. Mach Learn 2018, 108, 97-126. https://doi.org/10.1007/s10994-018- 5747-8
[17]. Yeung, C; Bunker, R; Umemoto R.; Fujiii, K. Evaluating Soccer Match Prediction Models: A Deep Learning Approach and Feature Optimization for Gradient-Boosted Trees, arXiv 2023, https://doi.org/10.48550/arXiv.2309.14807
[18]. B. Ji and J. Li, "NBA All-Star Lineup Prediction Based on Neural Networks," 2013 International Conference on Information Science and Cloud Computing Companion, Guangzhou, China, 2013, pp. 864-869, doi: 10.1109/ISCC-C.2013.92.
[19]. Wenyu Liu, Xi Xu, Yongqiang Cui, and Di Bai. 2024. In Proceedings of the 2024 International Academic Conference on Edge Computing, Parallel and Distributed Computing (ECPDC '24). Association for Computing Machinery, New York, NY, USA, 90–95. https://doi.org/10.1145/3677404.3677420
[20]. Mazaheri B, Jain S, Cook M, et al. Distribution Re-weighting and Voting Paradoxes[J]. arXiv preprint arXiv:2311.06840, 2023.
[21]. Hovhannisyan A, Allahverdyan A E. Resolution of Simpson's paradox via the common cause principle[J]. arXiv preprint arXiv:2403.00957, 2024.
[22]. Hugo Mathien.2016.European Soccer Database[Data set].EA Sports' FIFA games.
Cite this article
Wang,E.;Yin,X.;Li,Y.;Wang,T. (2025). Sports Betting: An Application of Machine Learning to the Game Prediction. Applied and Computational Engineering,132,104-118.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).