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A machine learning perspective on responsible gambling

Published online by Cambridge University Press:  29 April 2019

ARMAN HASSANNIAKALAGER Open the ORCID record for ARMAN HASSANNIAKALAGER [Opens in a new window]  and
PHILIP W.S. NEWALL Open the ORCID record for PHILIP W.S. NEWALL [Opens in a new window]
ARMAN HASSANNIAKALAGER
Affiliation:
University of Bath, Bath, UK
PHILIP W.S. NEWALL*
Affiliation:
University of Warwick, Coventry, UK
*
*Correspondence to: Philip W.S. Newall, Applied Psychology, WMG, University of Warwick, Coventry, CV4 7AL, UK. Email: philip.newall@warwick.ac.uk
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Abstract

Gamblers are frequently reminded to ‘gamble responsibly’. But these qualitative reminders come with no quantitative information for gamblers to judge relative product risk in skill-based gambling forms. By comparison, consumers purchasing alcohol are informed of product strength by alcohol by volume percentage (ABV%) or similar labels. This paper uses mixed logistic regression machine learning to uncover the potential variation in soccer betting outcomes. This paper uses data from four bet types and eight seasons of English Premier League soccer, ending in 2018. Outcomes across each bet type were compared using three betting strategies: the most-skilled prediction, a random strategy and the least-skilled prediction. There was a large spread in betting outcomes, with, for example, the per-bet average loss varying by a factor of 54 (from 1.1% to 58.9%). Gamblers’ losses were positively correlated with the observable betting odds across all bets, indicating that betting odds are one salient feature that could be used to inform gamblers about product risk. Such large differences in product risk are relevant to the promotion of responsible gambling.

Type
Article
Information
Behavioural Public Policy , Volume 6 , Issue 2 , April 2022 , pp. 237 - 260
Copyright
Copyright © Cambridge University Press 2019

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