Abstract
In this paper, we illustrate our study of using genetic algorithms and machine learning to create an ensemble technique, which is used to predict tennis games using limited amounts of data. The genetic algorithm was used to improve the game representations, derived from the players’ statistics differences, to be utilized by the machine learning algorithms. The use of genetic algorithms also reduced the dependence on human expertise in creating the game representations. The majority of the ensemble models we generated were either as good or performed higher than the predictions based on just the player’ official rankings.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Randrianasolo, A.S., Pyeatt, L.D.: Comparing Different Data Representations and Machine Learning Models to Predict Tennis. In: Arai, K. (eds) Advances in Information and Communication. FICC 2022. Lecture Notes in Networks and Systems, vol. 439. Springer International Publishing, pp. 488–500 (2022) https://doi.org/10.1007/978-3-030-98015-3_34
Huang, M.-L., Li, Y.-Z.: Use of machine learning and deep learning to predict the outcomes of major league baseball matches. Appl. Sci. vol. 11(10), 4499 (2021)
Candila, V., Palazzo, L.: Neural networks and betting strategies for tennis, Risks vol. 8(3) 2020
Randrianasolo, A.S., Pyeatt, L.D.: Predicting head-to-head games with a similarity metric and genetic algorithm. In: Arai, K., Bhatia, R., Kapoor, S. (eds.) FTC 2018. AISC, vol. 880, pp. 705–720. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02686-8_53
Bunker, R.P., Susnjak, T.: The application of machine learning techniques for predicting results in team sport: A review, CoRR, vol. abs/1912.11762 (2019)
Khan, S., Kirubanand, V.B.: Comparing machine learning and ensemble learning in the field of football. Int. J. Electr. Comput. Eng. (IJECE), textbf95, 4321 (2019)
Gu, W., Saaty, T.: Predicting the outcome of a tennis tournament: Based on both data and judgments. J. Syst. Sci. Syst. Eng. 28 317–343 (2019)
Wilkens, S.: Sports prediction and betting models in the machine learning age: The case of tennis, SSRN Electr. J. (2019)
Pretorius, A., Parry, D.A.: Human decision making and artificial intelligence: A comparison in the domain of sports prediction,” In: Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists ser. SAICSIT ’16. New York, NY, USA: ACM, pp. 32:1–32:10 (2016)
Brooks, J., Kerr, M., Guttag, J.,: Developing a data-driven player ranking in soccer using predictive model weights, In: Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’16. New York, NY, USA: ACM, pp. 49–55 (2016)
Knottenbelt, W.J., Spanias, D., Madurska, A,M,: A common-opponent stochastic model for predicting the outcome of professional tennis matches. Computers & Mathematics with Applications theory and Practice of Stochastic Modeling, vol. 64, no.12, pp. 3820–3827 (2012)
McHale, I., Morton, A.: A Bradley-Terry type model for forecasting tennis match results. Int. J. Forecast. 27(2), pp. 619–630 (2011)
del Corral, J., Prieto-Rodríguez, J.: Are differences in ranks good predictors for grand slam tennis matches. Int. J. Forecast. 26(3), 551–563 (2010)
Newton, P.K., Keller, J.B.: Probability of winning at tennis i. theory and data. Studies Appl. Math. 114(3), 241–269 (2005)
Barnett, T., Clarke, S.R.: Combining player statistics to predict outcomes of tennis matches. IMA J. Manag. Math. 16(2) 113–120 (2005)
Klaassen, K.J., Magnus, J.R.: Forecasting the winner of a tennis match. Europ. J. Operat. Res. 148(2), 257–267 (2003)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. In: 1st ed. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc., (1989)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Randrianasolo, A.S., Pyeatt, L.D. (2023). Using Genetic Algorithm to Create an Ensemble Machine Learning Models to Predict Tennis. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_45
Download citation
DOI: https://doi.org/10.1007/978-3-031-18461-1_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-18460-4
Online ISBN: 978-3-031-18461-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)