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Quantile regression analysis of in-play betting in a large online gambling dataset
,
Richard James,
Richard Tunney ,
Simon Dymond
Computers in Human Behavior Reports, Volume: 6, Start page: 100194
Swansea University Authors:
Sebastian Whiteford, Alice Hoon , Simon Dymond
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DOI (Published version): 10.1016/j.chbr.2022.100194
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 nece...
| Published in: | Computers in Human Behavior Reports |
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| ISSN: | 2451-9588 |
| Published: |
Elsevier BV
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa59770 |
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| 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. |
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| Keywords: |
In-Play; Live-action; Gambling; Quantile regression; Internet betting |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
International Center for Responsible Gaming |
| Start Page: |
100194 |