Abstract
Predicting a match result is a very challenging task and has its own features. Automatic prediction of a football match result is extensively studied in last two decades and provided the probabilities of outcomes of a scheduled match. In this paper we proposed a deep neural network based model to automatically predict result of a football match. The model is trained on selective features and evaluated through experiment results. We compared our proposed approach with the performance of feature-based classical machine learning algorithms. We also reported the challenges and situations where proposed system could not predict the outcome of a match.
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
References
Abdelhamid, N., Ayesh, A., Thabtah, F., Ahmadi, S., Hadi, W.: Mac: a multiclass associative classification algorithm. J. Inf. Knowl. Manag. 11(02), 1250011 (2012)
Bunker, R.P., Thabtah, F.: A machine learning framework for sport result prediction. Appl. Comput. Inform. (2017)
Constantinou, A.C., Fenton, N.E., Neil, M.: PI-football: a Bayesian network model for forecasting association football match outcomes. Knowl.-Based Syst. 36, 322–339 (2012)
Davoodi, E., Khanteymoori, A.R.: Horse racing prediction using artificial neural networks. Recent. Adv. Neural Netw. Fuzzy Syst. Evol. Comput. 2010, 155–160 (2010)
Kahn, J.: Neural network prediction of NFL football games, pp. 9–15 (2003)
Koopman, S.J., Lit, R.: A dynamic bivariate poisson model for analysing and forecasting match results in the english premier league. J. R. Stat. Soc.: Ser. (Stat. Soc.) 178(1), 167–186 (2015)
McCabe, A., Trevathan, J.: Artificial intelligence in sports prediction. In: 2008 Fifth International Conference on Information Technology: New Generations, ITNG 2008, pp. 1194–1197. IEEE (2008)
Min, B., Kim, J., Choe, C., Eom, H., McKay, R.B.: A compound framework for sports results prediction: a football case study. Knowl.-Based Syst. 21(7), 551–562 (2008)
Prasetio, D., et al.: Predicting football match results with logistic regression. In: 2016 International Conference on Advanced Informatics: Concepts, Theory And Application (ICAICTA), pp. 1–5. IEEE (2016)
Purucker, M.C:: Neural network quarterbacking. IEEE Potentials 15(3), 9–15 (1996)
Stefani, R.T.: Football and basketball predictions using least squares. IEEE Trans. Syst. Man Cybern. 7, 117–121 (1977)
Tax, N., Joustra, Y.: Predicting the dutch football competition using public data: a machine learning approach. Trans. Knowl. Data Eng. 10(10), 1–13 (2015)
Thabtah, F., Hammoud, S., Abdel-Jaber, H.: Parallel associative classification data mining frameworks based mapreduce. Parallel Process. Lett. 25(02), 1550002 (2015)
Ulmer, B., Fernandez, M., Peterson, M.: Predicting Soccer Match Results in the English Premier League. Ph.D. thesis, Doctoral dissertation, Ph. D. dissertation, Stanford (2013)
Yezus, A.: Predicting outcome of soccer matches using machine learning. Saint-Petersburg University (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rudrapal, D., Boro, S., Srivastava, J., Singh, S. (2020). A Deep Learning Approach to Predict Football Match Result. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_9
Download citation
DOI: https://doi.org/10.1007/978-981-13-8676-3_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8675-6
Online ISBN: 978-981-13-8676-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)