<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Department of Systems Engineering and Engineering Management

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"One-Match-Ahead Forecasting in Two-Team Sports with Stacked Bayesian Regressions"}

There is a growing interest in applying machine learning algorithms to real-world examples by explicitly deriving models based on probabilistic reasoning....

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

Publié en ligne: 09 févr. 2018

Pages: 159 - 171

Reçu: 10 févr. 2017

Accepté: 10 avr. 2017

DOI: https://doi.org/10.1515/jaiscr-2018-0011

Mots clés
Sports analytics, one-match-ahead forecasting, winning probability, Gaussian process regression

© 2018 Max W. Y. Lam, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Publié en ligne: 09 févr. 2018

Pages: 159 - 171

Reçu: 10 févr. 2017

Accepté: 10 avr. 2017

DOI: https://doi.org/10.1515/jaiscr-2018-0011

Mots clésSports analytics, one-match-ahead forecasting, winning probability, Gaussian process regression

© 2018 Max W. Y. Lam, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
Sports analytics, one-match-ahead forecasting, winning probability, Gaussian process regression