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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number100194
JournalComputers in Human Behavior Reports
Early online date1 Apr 2022
Publication statusPublished - May 2022

Bibliographical note

Creative Commons Attribution 4.0 International (CC BY 4.0)


  • Gambling
  • In-Play
  • Internet betting
  • Live-action
  • Quantile regression


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