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Wage against the machine: A generalized deep-learning market test of dataset value

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  • Maymin, Philip Z.
Abstract
How can you tell whether a particular sports dataset really adds value, particularly with regard to betting effectiveness? The method introduced in this paper provides a way for any analyst in almost any sport to attempt to determine the additional value of almost any dataset. It relies on the use of deep learning, comprehensive historical box score statistics, and the existence of betting markets. When the method is applied as an illustration to a novel dataset for the NBA, it is shown to provide more information than regular box score statistics alone, and appears to generate above-breakeven wagering profits.

Suggested Citation

  • Maymin, Philip Z., 2019. "Wage against the machine: A generalized deep-learning market test of dataset value," International Journal of Forecasting, Elsevier, vol. 35(2), pages 776-782.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:2:p:776-782
    DOI: 10.1016/j.ijforecast.2017.09.008
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    References listed on IDEAS

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    1. Chaim Zins, 2007. "Conceptual approaches for defining data, information, and knowledge," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(4), pages 479-493, February.
    2. Peter Csapo & Markus Raab, 2014. "“Hand down, Man down.” Analysis of Defensive Adjustments in Response to the Hot Hand in Basketball Using Novel Defense Metrics," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-25, December.
    3. Dare, William H. & Dennis, Steven A. & Paul, Rodney J., 2015. "Player absence and betting lines in the NBA," Finance Research Letters, Elsevier, vol. 13(C), pages 130-136.
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    Cited by:

    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.

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