Development and validation of a prediction model for online gambling problems based on players' account data

Abstract

Background and aims Gambling disorder is characterized by problematic gambling behavior that causes significant problems and distress. This study aimed to develop and validate a predictive model for screening online problem gamblers based on players' account data. Methods Two random samples of French online gamblers in skill-based (poker, horse race betting and sports betting, n = 8,172) and pure chance games (scratch games and lotteries, n = 5,404) answered an online survey and gambling tracking data were retrospectively collected for the participants. The survey included age and gender, gambling habits, and the Problem Gambling Severity Index (PGSI). We used machine learning algorithms to predict the PGSI categories with gambling tracking data. We internally validated the prediction models in a leave-out sample. Results When predicting gambling problems binary based on each PGSI threshold (1 for low-risk gambling, 5 for moderate-risk gambling and 8 for problem gambling), the predictive performances were good for the model for skill-based games (AUROCs from 0.72 to 0.82), but moderate for the model for pure chance games (AUROCs from 0.63 to 0.76, with wide confidence intervals) due to the lower frequency of problem gambling in this sample. When predicting the four PGSI categories altogether, performances were good for identifying extreme categories (non-problem and problem gamblers) but poorer for intermediate categories (low-risk and moderate-risk gamblers), whatever the type of game. Conclusions We developed an algorithm for screening online problem gamblers, excluding online casino gamblers, that could enable the setting of prevention measures for the most vulnerable gamblers.

Characteristics and prediction of risky gambling behaviour study: A study protocol

“…Luquiens et al (2016) used player tracking data from poker players screened with the PGSI (Ferris & Wynne, 2001) developing an instrument that only uses player tracking data to predict problem gambling. Auer and Griffiths (2022) as well as Perrot et al (2022) have also connected player tracking data to PGSI-screened samples of online casino players to each develop algorithms that assess problem gamblers by their tracked online gambling behaviour.…”

“…Auer and Griffiths ( 2022 ) as well as Perrot et al. ( 2022 ) have also connected player tracking data to PGSI‐screened samples of online casino players to each develop algorithms that assess problem gamblers by their tracked online gambling behaviour.…”

Individual risk factors and prediction of gambling disorder in online sports bettors - the longitudinal RIGAB study

“…A different, clinically more valid approach has been taken by a few studies, which used player tracking data from provider based samples to predict clinically validated screenings of GD ( 26 – 30 ). Most of them used the Problem Gambling Severity Index (PGSI, 31 ) or the brief biosocial gambling screen (BBGS, 32 ) as outcomes.…”

The benefits and challenges of online player tracking

“…Current literature indicates that the incidence of these reliably differs between problem and non-problem gamblers. Examples include: declined deposits, reversed withdrawals, frequent in session top-ups or gambling at unusual hours [11][12][13][14][15]. Importantly such work suggests the importance of real-time changes in behaviour.…”