Wage against the machine: A generalized deep-learning market test of dataset value
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Authors
Maymin, Philip Z.
Issue Date
2019-04
Type
Article
Language
en_US
Keywords
Machine learning , Deep learning , Sports forecasting , Gambling , Wagering , Data , Analytics
Alternative Title
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.
Description
Citation
Maymin, P.Z. (2019). Wage against the machine: A generalized deep-learning market test of dataset value. International Journal of Forecasting, 35(2).
Publisher
Elsevier