1. bookVolumen 8 (2018): Edición 3 (July 2018)
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Revista
eISSN
2449-6499
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30 Dec 2014
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One-Match-Ahead Forecasting in Two-Team Sports with Stacked Bayesian Regressions

Publicado en línea: 09 Feb 2018
Volumen & Edición: Volumen 8 (2018) - Edición 3 (July 2018)
Páginas: 159 - 171
Recibido: 10 Feb 2017
Aceptado: 10 Apr 2017
Detalles de la revista
License
Formato
Revista
eISSN
2449-6499
Primera edición
30 Dec 2014
Calendario de la edición
4 veces al año
Idiomas
Inglés
Abstract

There is a growing interest in applying machine learning algorithms to real-world examples by explicitly deriving models based on probabilistic reasoning. Sports analytics, being favoured mostly by the statistics community and less discussed in the machine learning community, becomes our focus in this paper. Specifically, we model two-team sports for the sake of one-match-ahead forecasting. We present a pioneering modeling approach based on stacked Bayesian regressions, in a way that winning probability can be calculated analytically. Benefiting from regression flexibility and high standard of performance, Sparse Spectrum Gaussian Process Regression (SSGPR) – an improved algorithm for the standard Gaussian Process Regression (GPR), was used to solve Bayesian regression tasks, resulting in a novel predictive model called TLGProb. For evaluation, TLGProb was applied to a popular sports event – National Basketball Association (NBA). Finally, 85.28% of the matches in NBA 2014/2015 regular season were correctly predicted by TLGProb, surpassing the existing predictive models for NBA.

Keywords

[1] I. Bhandari et al., Advanced Scout: Data Mining and Knowledge Discovery in NBA Data, Data Mining and Knowledge Discovery, 1(1), 121–125, 1997.10.1023/A:1009782106822Search in Google Scholar

[2] D. B. Hausch & W. T. Ziemba, Handbook of Sports and Lottery Markets, Elsevier, 2011.Search in Google Scholar

[3] M. Ottaviani & P. N. Sørensen, Surprised by the Parimutuel Odds?, The American Economic Review, 99(5), 2129–2134, 2009.10.1257/aer.99.5.2129Search in Google Scholar

[4] M. Haghighat et al., A Review of Data Mining Techniques for Result Prediction in Sports, Advances in Computer Science: an International Journal, 2(5), 7–12, 2013.Search in Google Scholar

[5] M. Lázaro-Gredilla et la., Sparse Spectrum Gaussian Process Regression, Journal of Machine Learning Research, 11(Jun), 1865–1881, 2010.Search in Google Scholar

[6] C. E. Rasmussen & C. K. Williams, Gaussian Processes for Machine Learning, MIT Press, 2006.10.7551/mitpress/3206.001.0001Search in Google Scholar

[7] D. J. MacKay, Introduction to Gaussian Processes. NATO ASI Series F Computer and Systems Sciences, 168, 133–166, 1998.Search in Google Scholar

[8] D. Duvenaud, Automatic Model Construction with Gaussian Processes, Doctoral Dissertation, University of Cambridge, 2014.Search in Google Scholar

[9] R. M. Neal, Bayesian Learning for Neural Networks, Springer Science & Business Media, 118, 2012.Search in Google Scholar

[10] Y. Gal & R. Turner, Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs, In: 32nd International Conference on Machine Learning, 655–664, 2015.Search in Google Scholar

[11] N. Wiener, Generalized Harmonic Analysis, Acta mathematica, 55(1), 117–258, 1930.10.1007/BF02546511Search in Google Scholar

[12] A. Khintchine, Korrelationstheorie der Stationren Stochastischen Prozesse, Mathematische Annalen, 109(1), 604–615, 1934.10.1007/BF01449156Search in Google Scholar

[13] S. Bochner, Monotone Funktionen, Stieltjessche Integrale Und Harmonische Analyse, Mathematische Annalen, 108(1), 378–410, 1933.10.1007/BF01452844Search in Google Scholar

[14] J. Quiñonero-Candela et la., A Unifying View of Sparse Approximate Gaussian Process Regression, Journal of Machine Learning Research, 6(Dec), 1939–1959, 2005.Search in Google Scholar

[15] J. S. Simonoff, Smoothing Methods in Statistics, Springer Science & Business Media, 2012.Search in Google Scholar

[16] E. S. Gardner, Exponential Smoothing: The State of the Art, Journal of Forecasting, 4(1), 1–28, 1985.10.1002/for.3980040103Abierto DOISearch in Google Scholar

[17] D. Oliver, Basketball on Paper: Rules and Tools for Performance Analysis, Potomac Books, Inc., 2004.Search in Google Scholar

[18] W. L. Winston, Mathletics: How Gamblers, Managers, and Sports Enthusiasts Use Mathematics in Baseball, Basketball, and Football, Princeton University Press, 2012.10.1515/9781400842070Search in Google Scholar

[19] D. Kingma & J. Ba, Adam: A Method for Stochastic Optimization, In: International Conference on Learning Representations 2014, 1–13, 2014.Search in Google Scholar

[20] C. Saunders et la., Ridge Regression Learning Algorithm in Dual Variables. In: 15th International Conference on Machine Learning, 515–521, 1998.Search in Google Scholar

[21] S. An et la., Face Recognition Using Kernel Ridge Regression. In: Computer Vision and Pattern Recognition 2007, IEEE, 1–7, 2007.10.1109/CVPR.2007.383105Search in Google Scholar

[22] M. Xu et la., Decision Tree Regression for Soft Classification of Remote Sensing Data, Remote Sensing of Environment, 97(3), 322–336, 2005.10.1016/j.rse.2005.05.008Abierto DOISearch in Google Scholar

[23] Y. Freund, & R. E. Schapire, A Desicion-Theoretic Generalization of On-line Learning and An Application to Boosting, In: European Conference on Computational Learning Theory 1995, Springer Berlin Heidelberg, 23–37, 1995.10.1007/3-540-59119-2_166Search in Google Scholar

[24] J. H. Friedman, Greedy Function Approximation: a Gradient Boosting Machine, Annals of Statistics, 1189–1232, 2001.10.1214/aos/1013203451Abierto DOISearch in Google Scholar

[25] L. Breiman, Random Forests, Machine learning, 45(1), 5–32, 2001.10.1023/A:1010933404324Abierto DOISearch in Google Scholar

[26] F. Pedregosa et la., Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12(Oct), 2825–2830, 2011.Search in Google Scholar

[27] J. Quinonero-Candela et la., Evaluating Predictive Uncertainty Challenge. In: Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment, Springer Berlin Heidelberg, 1–27, 2006.10.1007/11736790_1Search in Google Scholar

[28] J. Kohonen & J. Suomela, Lessons Learned in the Challenge: Making Predictions and Scoring Them. In: Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment, Springer Berlin Heidelberg, 1–27, 2006.Search in Google Scholar

[29] D. Miljković et la., The Use of Data Mining for Basketball Matches Outcomes Prediction, In: 8th International Symposium on Intelligent Systems and Informatics (SISY), IEEE, 309–312, 2010.10.1109/SISY.2010.5647440Search in Google Scholar

[30] C. Cao, Sports Data Mining Technology Used in Basketball Outcome Prediction, Masters Dissertation, Dublin Institute of Technology, 2012.Search in Google Scholar

[31] M. Beckler et la., NBA Oracle, https://www.mbeckler.org/coursework/2008 – 2009/10701report.pd f, 2013.Search in Google Scholar

[32] E. Zdravevski, & A. Kulakov, System for Prediction of the Winner in a Sports Game, In: ICT Innovations, Springer Berlin Heidelberg, 55–63, 2009.10.1007/978-3-642-10781-8_7Search in Google Scholar

[33] B. Loeffelholz et la., Predicting NBA Games Using Neural Networks, Journal of Quantitative Analysis in Sports, 5(1), 1–15, 2009.10.2202/1559-0410.1156Search in Google Scholar

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