One-match-ahead forecasting in two-team sports with stacked Bayesian regressions

• Autorzy: Lam M. W. Y.

Identyfikatory

DOI

10.1515/jaiscr-2018-0011

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

sports analytics; one-match-ahead forecasting; winning probability; Gaussian process regression

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2018
• Tom: Vol. 8, No. 3
• Strony: 159--172
• Opis fizyczny: Bibliogr. 33 poz., rys.

Twórcy

autor

Lam M. W. Y.

• Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).