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

Whiteford, Sebastian; Hoon, Alice; James, Richard; Tunney, Richard; Dymond, Simon

doi:10.1016/j.chbr.2022.100194

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

Sebastian Whiteford, Alice Hoon

, 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...

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Published in:	Computers in Human Behavior Reports
ISSN:	2451-9588
Published:	Elsevier BV 2022
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URI:	https://cronfa.swan.ac.uk/Record/cronfa59770
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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. 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spelling	2022-12-08T11:22:58.3291419 v2 59770 2022-04-06 Quantile regression analysis of in-play betting in a large online gambling dataset 5bcf7b504f5cb2b2ad68192efc3983f5 Sebastian Whiteford Sebastian Whiteford true false 6ee42ad57b74f8941f4de3f02eed163f 0000-0002-9921-6156 Alice Hoon Alice Hoon true false 8ed0024546f2588fdb0073a7d6fbc075 0000-0003-1319-4492 Simon Dymond Simon Dymond true false 2022-04-06 HPS 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. Journal Article Computers in Human Behavior Reports 6 100194 Elsevier BV 2451-9588 In-Play; Live-action; Gambling; Quantile regression; Internet betting 1 5 2022 2022-05-01 10.1016/j.chbr.2022.100194 COLLEGE NANME Psychology COLLEGE CODE HPS Swansea University SU College/Department paid the OA fee International Center for Responsible Gaming 2022-12-08T11:22:58.3291419 2022-04-06T14:00:05.6933649 Faculty of Medicine, Health and Life Sciences School of Psychology Sebastian Whiteford 1 Alice Hoon 0000-0002-9921-6156 2 Richard James 3 Richard Tunney 0000-0003-4673-757x 4 Simon Dymond 0000-0003-1319-4492 5 59770__24062__1ffc1ce98faa40cda54c9669c90088f3.pdf 59770.pdf 2022-05-13T12:24:45.6460334 Output 2102459 application/pdf Version of Record true © 2022 The Authors. This is an open access article under the CC BY license true eng http://creativecommons.org/licenses/by/4.0/
title	Quantile regression analysis of in-play betting in a large online gambling dataset
spellingShingle	Quantile regression analysis of in-play betting in a large online gambling dataset Sebastian Whiteford Alice Hoon Simon Dymond
title_short	Quantile regression analysis of in-play betting in a large online gambling dataset
title_full	Quantile regression analysis of in-play betting in a large online gambling dataset
title_fullStr	Quantile regression analysis of in-play betting in a large online gambling dataset
title_full_unstemmed	Quantile regression analysis of in-play betting in a large online gambling dataset
title_sort	Quantile regression analysis of in-play betting in a large online gambling dataset
author_id_str_mv	5bcf7b504f5cb2b2ad68192efc3983f5 6ee42ad57b74f8941f4de3f02eed163f 8ed0024546f2588fdb0073a7d6fbc075
author_id_fullname_str_mv	5bcf7b504f5cb2b2ad68192efc3983f5_*_Sebastian Whiteford 6ee42ad57b74f8941f4de3f02eed163f__Alice Hoon 8ed0024546f2588fdb0073a7d6fbc075_**_Simon Dymond
author	Sebastian Whiteford Alice Hoon Simon Dymond
author2	Sebastian Whiteford Alice Hoon Richard James Richard Tunney Simon Dymond
format	Journal article
container_title	Computers in Human Behavior Reports
container_volume	6
container_start_page	100194
publishDate	2022
institution	Swansea University
issn	2451-9588
doi_str_mv	10.1016/j.chbr.2022.100194
publisher	Elsevier BV
college_str	Faculty of Medicine, Health and Life Sciences
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description	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.
published_date	2022-05-01T04:17:19Z
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score	11.037603

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

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