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A generative model for predicting outcomes in college basketball

Author

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  • Ruiz Francisco J. R.

    (University Carlos III in Madrid – Signal Theory and Communications Department. Avda. de la Universidad, 30. Lab 4.3.A03, Leganes, Madrid 28911, Spain)

  • Perez-Cruz Fernando

    (University Carlos III in Madrid – Signal Theory and Communications Department. Avda. de la Universidad, 30, Leganes, Madrid 28911, Spain; and Bell Labs, Alcatel-Lucent, New Providence, NJ 07974 USA, e-mail: fernando@tsc.uc3m.es)

Abstract

We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.

Suggested Citation

  • Ruiz Francisco J. R. & Perez-Cruz Fernando, 2015. "A generative model for predicting outcomes in college basketball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(1), pages 39-52, March.
    • Handle: RePEc:bpj:jqsprt:v:11:y:2015:i:1:p:39-52:n:3
    DOI: 10.1515/jqas-2014-0055
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    References listed on IDEAS

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    1. Gianluca Baio & Marta Blangiardo, 2010. "Bayesian hierarchical model for the prediction of football results," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 253-264.
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    Cited by:

    1. Ludden Ian G. & Khatibi Arash & King Douglas M. & Jacobson Sheldon H., 2020. "Models for generating NCAA men’s basketball tournament bracket pools," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(1), pages 1-15, March.
    2. Santos-Fernandez Edgar & Wu Paul & Mengersen Kerrie L., 2019. "Bayesian statistics meets sports: a comprehensive review," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 289-312, December.
    3. Gavin A. Whitaker & Ricardo Silva & Daniel Edwards & Ioannis Kosmidis, 2021. "A Bayesian approach for determining player abilities in football," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 174-201, January.

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