Typology of online lotteries and scratch games gamblers' behaviours: A multilevel latent class cluster analysis applied to player account‐based gambling data

Perrot, Bastien; Hardouin, Jean-Benoît; Grall‐Bronnec, Marie; Challet-Bouju, Gaëlle

doi:10.1002/mpr.1746

Cited by 17 publications

(30 citation statements)

References 36 publications

(45 reference statements)

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“…The results indicated that individuals with high-activity and high-variability gambling were more likely to close their account due to gambling-related problems. Moreover, in previous work [ 36 ], we found that risky monthly behaviors were associated with larger deviations from the usual gambling activity in online lotteries. Finally, in a recent review, trajectory information has been reported as a possible method with good predictive performance in identifying problem gambling behavior through online gambling tracking data analysis [ 20 ].…”

Section: Introductionmentioning

confidence: 55%

“…An anonymized data set was used; this data set had also been used in a previous analysis [ 36 ]. The data were comprised of gambling‐related records from a random sample of 10,000 clients of the French national lottery and included the age, gender, and Playscan classification—a responsible gambling tool that provides a low, medium, or high risk assessment—of each client.…”

Section: Methodsmentioning

confidence: 99%

“…The data were comprised of gambling‐related records from a random sample of 10,000 clients of the French national lottery and included the age, gender, and Playscan classification—a responsible gambling tool that provides a low, medium, or high risk assessment—of each client. This operator is the only one in France that is permitted to offer online lotteries and scratch games, and thus, represents 100% of the market for online lotteries and scratch games in France [ 36 ]. The Playscan classification is a risk assessment based on an individual’s 5 previous weeks of gambling activity: green corresponds to a low risk of problem gambling, orange corresponds to a medium risk, and red corresponds to a high risk [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

“…Thus, a stream of gambling-related research has been undertaken using of gambling tracking data (naturalistic data) [ 20 ]. Large research programs [ 21 - 36 ] have been initiated including research [ 36 ] that focuses on rarely studied online lottery gambling tracking data (lottery draws, daily lotteries, and scratch cards). Indeed, such gambling is traditionally considered the least associated with gambling problems, but is the most prevalent form of gambling, leading to an overall sum of low-level harms that could be as important as the harm associated with more problematic but less prevalent forms of gambling [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

“…Researchers often use the modification of betting behavior depending on previous betting outcomes as a proxy to identify chasing episodes, as it is a behavior that is not directly observable [ 20 ]. In recent work [ 36 , 42 ], we proposed to approximate within-session chasing behavior by focusing on recurrent deposits within a short period of time or deposits that occurred immediately after a bet. When a deposit is made in these conditions, it likely means that the person has recently lost money; the deposit indicates the unplanned continuation of gambling in an attempt to recover losses.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Early Gambling Behavior Using Indicators from Online Lottery Gambling Tracking Data: Longitudinal Analysis

Challet-Bouju¹,

Hardouin²,

Thiabaud³

et al. 2020

J Med Internet Res

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Background Individuals who gamble online may be at risk of gambling excessively, but internet gambling also provides a unique opportunity to monitor gambling behavior in real environments which may allow intervention for those who encounter difficulties. Objective The objective of this study was to model the early gambling trajectories of individuals who play online lottery. Methods Anonymized gambling‐related records of the initial 6 months of 1152 clients of the French national lottery who created their internet gambling accounts between September 2015 and February 2016 were analyzed using a two-step approach that combined growth mixture modeling and latent class analysis. The analysis was based upon behavior indicators of gambling activity (money wagered and number of gambling days) and indicators of gambling problems (breadth of involvement and chasing). Profiles were described based upon the probabilities of following the trajectories that were identified for the four indicators, and upon several covariates (age, gender, deposits, type of play, net losses, voluntary self-exclusion, and Playscan classification—a responsible gambling tool that provides each player with a risk assessment: green for low risk, orange for medium risk and red for high risk). Net losses, voluntary self-exclusion, and Playscan classification were used as external verification of problem gambling. Results We identified 5 distinct profiles of online lottery gambling. Classes 1 (56.8%), 2 (14.8%) and 3 (13.9%) were characterized by low to medium gambling activity and low values for markers of problem gambling. They displayed low net losses, did not use the voluntary self-exclusion measure, and were classified predominantly with green Playscan tags (range 90%-98%). Class 4 (9.7%) was characterized by medium to high gambling activity, played a higher breadth of game types (range 1-6), and had zero to few chasing episodes. They had high net losses but were classified with green (66%) or orange (25%) Playscan tags and did not use the voluntary self-exclusion measure. Class 5 (4.8%) was characterized by medium to very high gambling activity, played a higher breadth of game types (range 1-17), and had a high number of chasing episodes (range 0-5). They experienced the highest net losses, the highest proportion of orange (32%) and red (39%) tags within the Playscan classification system and represented the only class in which voluntary self-exclusion was present. Conclusions Classes 1, 2, 3 may be considered to represent recreational gambling. Class 4 had higher gambling activity and higher breadth of involvement and may be representative of players at risk for future gambling problems. Class 5 stood out in terms of much higher gambling activity and breadth of involvement, and the presence of chasing behavior. Individuals in classes 4 and 5 may benefit from early preventive measures.

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Section: Introductionmentioning

confidence: 55%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modeling Early Gambling Behavior Using Indicators from Online Lottery Gambling Tracking Data: Longitudinal Analysis

Challet-Bouju¹,

Hardouin²,

Thiabaud³

et al. 2020

J Med Internet Res

Self Cite

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The Effect of a Mandatory Play Break on Subsequent Gambling Behavior among British Online Casino Players: A Large-Scale Real-World Study

Auer¹,

Griffiths

2022

J Gambl Stud

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In recent years, various novel responsible gambling (RG) tools have been implemented to aid harm-minimization. One such RG tool has been the implementation of enforced mandatory play breaks. Despite many responsible gambling operators using mandatory play breaks, only three previous studies have examined their efficacy and the findings were mixed. Therefore, the present investigation was a large-scale real-world study which was designed to see whether a 60-minute mandatory play break influenced subsequent depositing and wagering. The authors were given access to 27 days of player data prior to the introduction of a mandatory play break and 27 days of player data after the mandatory play break was introduced. The study comprised British online gamblers from Skillonnet (a European online gambling operator). Between July 23 and September 15 (2021), 2,021 players deposited at least ten times or more on a calendar day, at least once. The 2,201 players generated 2,994 corresponding events (i.e., the depositing of money at least 10 times in one day). The percentage of players who stopped depositing money as a consequence of the mandatory play break rose from 27% to 68% on the day of a play break. Moreover, the percentage of players who stopped wagering as a consequence of the mandatory play break rose from 0.1% to 45% on the day of a play break. The findings of the present study demonstrated that a 60-minute mandatory play break impacts players’ depositing and wagering immediately after the play break. This means that a mandatory hour-long play break in an online casino setting appears to prevent overspending during a short period of time. The effects of a 60-minute mandatory break on the next day’s behavior were inconclusive.

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Using artificial intelligence algorithms to predict self-reported problem gambling with account-based player data in an online casino setting

Auer¹,

Griffiths

2022

J Gambl Stud

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In recent years researchers have emphasized the importance of artificial intelligence (AI) algorithms as a tool to detect problem gambling online. AI algorithms require a training dataset to learn the patterns of a prespecified group. Problem gambling screens are one method for the collection of the necessary input data to train AI algorithms. The present study’s main aim was to identify the most significant behavioral patterns which predict self-reported problem gambling. In order to fulfil the aim, the study analyzed data from a sample of real-world online casino players and matched their self-report (subjective) responses concerning problem gambling with the participants’ actual (objective) gambling behavior. More specifically, the authors were given access to the raw data of 1,287 players from a European online gambling casino who answered questions on the Problem Gambling Severity Index (PGSI) between September 2021 and February 2022. Random forest and gradient boost machine algorithms were trained to predict self-reported problem gambling based on the independent variables (e.g., wagering, depositing, gambling frequency). The random forest model predicted self-reported problem gambling better than gradient boost. Moreover, problem gamblers showed a distinct pattern with respect to their gambling based on the player tracking data. More specifically, problem gamblers lost more money per gambling day, lost more money per gambling session, and deposited money more frequently per gambling session. Problem gamblers also tended to deplete their gambling accounts more frequently compared to non-problem gamblers. A subgroup of problem gamblers identified as being at greater harm (based on their response to PGSI items) showed even higher values with respect to the aforementioned gambling behaviors. The study showed that self-reported problem gambling can be predicted by AI algorithms with high accuracy based on player tracking data.

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Typology of online lotteries and scratch games gamblers' behaviours: A multilevel latent class cluster analysis applied to player account‐based gambling data

Cited by 17 publications

References 36 publications

Modeling Early Gambling Behavior Using Indicators from Online Lottery Gambling Tracking Data: Longitudinal Analysis

Modeling Early Gambling Behavior Using Indicators from Online Lottery Gambling Tracking Data: Longitudinal Analysis

The Effect of a Mandatory Play Break on Subsequent Gambling Behavior among British Online Casino Players: A Large-Scale Real-World Study

Using artificial intelligence algorithms to predict self-reported problem gambling with account-based player data in an online casino setting

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