Philip Maymin

Wage Against the Machine

How can you tell if a particular sports dataset really adds value? 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.

Available on SSRN and IJF.

Citation: Maymin, Philip Z. (2019), "Wage Against the Machine: A Generalized Deep-Learning Market Test of Dataset Value," International Journal of Forecasting, Special Issue on Sports Forecasting, 35:2, 776-782.

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  • When Deep Learning Met Vantage Data, December 27, 2015

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