Abstract
For many reasons, including sports being one of the main forms of entertainment in the world, online gambling is growing. And in growing markets, opportunities to explore it arise. In this paper, neural network ensemble approaches, such as bagging, random subspace sampling, negative correlation learning and the simple averaging of predictions, are compared. For each one of these methods, several combinations of input parameters are evaluated. We used only the expected goals metric as predictors since it is able to have good predictive power while keeping the computational demands low. These models are compared in the soccer (also known as association football) betting context where we have access to metrics, such as rentability, to analyze the results in multiple perspectives. The results show that the optimal solution is goal-dependent, with the ensemble methods being able to increase the accuracy up to +3 % over the best single model. The biggest improvement over the single model was obtained by averaging dropout networks.
This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020.
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References
Aggarwal, C.C.: Neural Networks and Deep Learning: eBook. Springer, Switzerland (2018)
Constantinou, A.C., Fenton, N.E., Neil, M.: Profiting from an inefficient association football gambling market: prediction, risk and uncertainty using Bayesian networks. Knowl. Based Syst. 50, 60–86 (2013)
Dzalbs, I., Kalganova, T.: Forecasting price movements in betting exchanges using cartesian genetic programming and ANN. Big Data Res. 14, 112–120 (2018)
FiveThirtyEight. fivethirtyeight.com. Accessed 21st June 2019
Football-Data.co.uk. football-data.co.uk. Accessed 21st June 2019
Harangi, B.: Skin lesion classification with ensembles of deep convolutional neural networks. J. Biomed. Inform. 86, 25–32 (2018)
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)
Liu, Y., Yao, X.: Ensemble learning via negative correlation. Neural Networks 12(10), 1399–1404 (1999)
Mccabe, A., Trevathan, J: Artificial intelligence in sports prediction. In: Fifth International Conference on Information Technology: New Generations, pp. 1194–1197. IEEE Computer Society, Las Vegas (2008)
Opta. optasports.com. Accessed 22nd June 2019
Rotshtein, A.P., Posner, M., Rakityanskaya, A.B.: Football predictions based on a fuzzy model with genetic and neural tuning. Cybern. Syst. Anal. 41(4), 619–630 (2005)
Ribeiro, G., Mariani, V., Coelho, L.: Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting. Eng. Appl. Artif. Intell. 82, 272–281 (2019)
Dubitzky, W., Lopes, P., Davis, J., Berrar, D.: The open international soccer database. Mach. Learn. 108, 9–28 (2019)
Berrar, D., Lopes, P., Davis, J., Dubitzky, W.: Guest editorial: special issue on machine learning for soccer. Mach. Learn. 108(1), 1–7 (2018). https://doi.org/10.1007/s10994-018-5763-8
Tsokos, A., Narayanan, S., Kosmidis, I., Baio, G., Cucuringu, M., Whitaker, G., Király, F.: Modeling outcomes of soccer matches. Mach. Learn. 108(1), 77–95 (2018). https://doi.org/10.1007/s10994-018-5741-1
Constantinou, A.C.: Dolores: a model that predicts football match outcomes from all over the world. Mach. Learn. 108(1), 49–75 (2018). https://doi.org/10.1007/s10994-018-5703-7
Hubáček, O., Šourek, G., Železný, F.: Learning to predict soccer results from relational data with gradient boosted trees. Mach. Learn. 108(1), 29–47 (2018). https://doi.org/10.1007/s10994-018-5704-6
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Mendes-Neves, T., Mendes-Moreira, J. (2020). 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. ISMIS 2020. Lecture Notes in Computer Science(), vol 12117. Springer, Cham. https://doi.org/10.1007/978-3-030-59491-6_13
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