Seb Whiteford
Quantile regression analysis of in-play betting in a large online gambling dataset
Whiteford, Seb; Hoon, Alice E.; James, Richard; Tunney, Richard; Dymond, Simon
Authors
Alice E. Hoon
RICHARD JAMES RICHARD.JAMES4@NOTTINGHAM.AC.UK
Assistant Professor
Richard Tunney
Simon Dymond
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 |
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 |
Files
1-s2.0-S2451958822000288-main
(2.1 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
You might also like
Crystal structure of the antimicrobial peptidase lysostaphin from Staphylococcus simulans
(2014)
Journal Article
On the latent structure of problem gambling: a taxometric analysis
(2014)
Journal Article
Activity profiles of elite wheelchair rugby players during competition
(2015)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search