Quantile regression analysis of in-play betting in a large online gambling dataset


In-play betting involves making multiple bets during a sporting event and is an increasingly popular form of gambling. Behavioural analysis of large datasets of in-play betting may aid in the prediction of at-risk patterns of gambling. However, datasets may contain significant skew and outliers necessitating analytical approaches capable of examining behaviour across the spectrum of involvement with in-play betting. Here, we employ quantile regression analyses to investigate the relationships between in-play betting behaviours of frequency and duration of play, bets per day, net/percentage change, average stake, and average/percentage change across groups of users differing by betting involvement. The dataset consisted of 24,781 in-play sports bettors enrolled with an internet sports betting provider in February 2005. We examined trends in normally-involved and heavily-involved in-play bettor groups at the .1, .3, .5, .7 and .9 quantiles. The relationship between the total number of in-play bets and the remaining in-play betting measures was dependent on degree of involvement. The only variable to differ from this analytic path was the standard deviation in the daily average stake for most-involved bettors. The direction of some relationships, such as the frequency of play and bets per betting day, were reversed for most-involved bettors. Crucially, this highlights the importance of determining how these relationships vary across the spectrum of involvement with in-play betting. In conclusion, quantile regression provides a comprehensive account of the relationship between in-play betting behaviours capable of quantifying changes in magnitude and direction that vary by involvement.

Publication DOI: https://doi.org/10.1016/j.chbr.2022.100194
Divisions: College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences
Additional Information: Creative Commons Attribution 4.0 International (CC BY 4.0)
Uncontrolled Keywords: Gambling,In-Play,Internet betting,Live-action,Quantile regression,Human-Computer Interaction,Artificial Intelligence,Computer Science Applications,Neuroscience (miscellaneous),Applied Psychology,Cognitive Neuroscience
Publication ISSN: 2451-9588
Last Modified: 17 Jun 2024 08:03
Date Deposited: 08 Apr 2022 10:15
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Related URLs: https://www.sci ... 0288?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2022-05
Published Online Date: 2022-04-01
Accepted Date: 2022-03-30
Authors: Whiteford, Seb
Hoon, Alice E.
James, Richard
Tunney, Richard (ORCID Profile 0000-0003-4673-757X)
Dymond, Simon



Version: Published Version

License: Creative Commons Attribution

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