One-Match-Ahead Forecasting in Two-Team Sports with Stacked Bayesian Regressions

[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.3980040103Ouvrir le 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.2005.05.008Ouvrir le 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/1013203451Ouvrir le DOISearch in Google Scholar

[25] L. Breiman, Random Forests, Machine learning, 45(1), 5–32, 2001.10.1023/A:1010933404324Ouvrir le 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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