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

Whiteford, Seb; Hoon, Alice E.; James, Richard; Tunney, Richard; Dymond, Simon

Abstract

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.

Citation

Whiteford, S., Hoon, A. E., James, R., Tunney, R., & Dymond, S. (2022). Quantile regression analysis of in-play betting in a large online gambling dataset. Computers in Human Behavior Reports, 6, Article 100194. https://doi.org/10.1016/j.chbr.2022.100194

• Journal Article Type: Article
• Acceptance Date: Mar 30, 2022
• Online Publication Date: Apr 1, 2022
• Publication Date: May 1, 2022
• Deposit Date: Apr 6, 2022
• Publicly Available Date: Apr 8, 2022
• Journal: Computers in Human Behavior Reports
• Electronic ISSN: 2451-9588
• Publisher: Elsevier BV
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Article Number: 100194
• DOI: https://doi.org/10.1016/j.chbr.2022.100194
• Keywords: Polymers and Plastics; General Environmental Science
• Public URL: https://nottingham-repository.worktribe.com/output/7681790
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S2451958822000288