Wager Mage
Wager Mage
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Metrics for chasing losses. The authors developed five different metrics for chasing losses based on the single transactions and derived sessions. These were (i) within-session chasing, (ii) across-session chasing, (iii) across-days chasing, (iv) regular gambling account depletion, and (v) frequent session depositing.
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Read More »Internet gambling is a mode of gambling which has been facilitated by technology, including increased accessibility and availability of mobile devices such as smartphones and tablets (Lopez-Gonzalez et al., 2021). It should also be noted that internet gambling is not a type of gambling activity but a medium in which individuals gamble. The medium of the internet features many aspects that make it attractive to gamblers including easy accessibility, affordability, anonymity, convenience, immersion and dissociation qualities, facilitation of disinhibition, and interactivity (Griffiths, 2003). Empirical research has indicated that internet gamblers appear to be younger, engage in a greater number of gambling activities, and are more likely to bet on sports (e.g., Gainsbury et al., 2013) although research has also shown that most online gamblers also gamble offline (Wardle et al., 2011). Allami et al. (2021) conducted a meta-analysis evaluating 57 risk factors across 104 worldwide gambling prevalence studies with the number of participants in the studies ranging from 5327 to 273,946. The risk factors (socio-demographic, psychosocial, gambling activity, and substance use correlates) were ranked from largest to smallest with regard to their association with problem gambling and internet gambling was the risk factor with the highest odds ratio. They also concluded that the most frequently assessed problem gambling risk factors with the highest effect sizes were associated with continuous-play forms of gambling. In recent years, player tracking has been utilized as a potential tool to identify problem gambling by both gambling operators and researchers (Auer & Griffiths, 2013; Deng et al., 2019). For example, Ukohv et al. (2021) used predictive modeling to examine the differences between online sports bettors and online casino players. They found that gambling via desktop computers contributed positively to problem gambling-related exclusion among online casino players whereas for online sports bettors, problem gambling-related exclusion related more to the use of mobile devices (i.e., smartphones and tablets). Furthermore, the number of cash wagers per active day contributed the most to problem-gambling in the case of sports betting. For casino players, the most informative features related to problem gambling were shared between the volume of approved deposits and the duration of authentication sessions. Ukhov et al. (2021) concluded that the explanatory variables being considered contribute differently to problematic online casino players and problematic online sports bettors. Luquiens et al. (2016) surveyed online poker players who were administered the Problem Gambling Severity Index (PGSI) and compared their responses with their actual gambling behavior data. They found that the most important risk factors for problem gambling were being male, depositing at least three times in a 12-hour period, being younger than 28 years, making any new monetary deposit during the 30-day study period (compared to those players who carried their balance forward from previous months), having a mean loss per gambling session of more than €1.7 during the 30-day study period, losing a total of more than €45 during the 30-day study period, having a total stake of more than €298 during the 30-day study period, having more than 60 gambling sessions during the 30-day study period, and multi-tabling (players gambling on multiple tables simultaneously). Other studies have investigated the profile of players who have voluntarily self-excluded. For example, Percy et al., 2016 evaluated various machine learning techniques and found that the Random Forest method was the most accurate method in identifying self-excluders from gamblers who did not self-exclude. Braverman et al. (2012) analyzed the first month of data for a group of players who closed their account due to gambling-related problems. The characteristics of that group were frequent and intensive betting combined with high variability across wager amount and increased monetary wagering during the first month of betting. Given the increased incidence of problem gambling online, gambling operators have been using commercially available player tracking tools such as PlayScan (Griffiths, 2009) and mentor (Auer & Griffiths, 2020) to identify problem gambling (PG) using their players’ tracking data and informing their clientele about their gambling behavior. Individuals who engage in maladaptive patterns of gambling behavior are said to have gambling disorder (Grant et al., 2017). Gambling disorder was classed as a form of behavioral addiction in the most recent (fifth) edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association 2013). There are nine criteria and those individuals who endorse at least four of the criteria are diagnosed as having gambling disorder. There are three severity levels comprising mild (endorsing 4–5 criteria), moderate (endorsing 6–7 criteria), and severe (endorsing 8–9 criteria) (American Psychiatric Association, 2013; Grant et al., 2017). One of the nine DSM-5 criteria is ‘chasing losses’ which is defined as “after losing money gambling, often returns another day to get even” (p.585). Therefore, chasing losses refers to individuals increasing the amount of money that they gamble after they have lost their money gambling in an attempt to recoup the money lost. This behavior is almost omnipresent among individuals with gambling problems and has been identified by some as the most significant step or risk factor in the development of gambling disorder (Breen et al. 1999, Lesieur 1979).
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Read More »However, the definition of chasing losses provided in the DSM-5 is arguably generic and not necessarily accurate, especially as chasing losses can occur within-session rather than ‘returning another day’. In short, the DSM-5’s criterion for chasing losses does not provide the extent and over what period time the monetary losses and subsequent gambling should be observed. Gainsbury et al. (2014) collected self-reported information about chasing losses among a sample of 10,838 Australian internet gamblers. The results showed that online casino players had a greater tendency to report chasing losses than online poker players and gamblers who reported chasing losses were more likely to hold irrational beliefs about gambling and spend more time and money gambling than those who reported that they were unaffected by previous losses. Previous papers have claimed that chasing losses can easily be observed by gambling operators or researchers using account-based behavioral tracking data (e.g., Delfabbro et al., 2012; Griffiths & Whitty, 2010). However, only few studies (i.e., Catania & Griffiths 2021; Challet-Bouju et al., 2020; Perrot et al., 2018) have actually attempted to operationalize chasing losses using player tracking data. Catania & Griffiths (2021) compared the highest monetary deposit made by gamblers during the three-month study period to the initial monetary deposit after the player had first registered on the website. This ratio was argued by the authors to be an operational metric for chasing losses. However, the metric developed by Catania and Griffiths (2021) did not reflect short-term chasing which could potentially happen in a gambling session. Additionally, the metric also did not accurately reflect the DSM-5 criterion which simply defines chasing as increased gambling on a day after losing more on a previous day. Perrot et al. (2018) analyzed player account data of 10,000 French online lottery players. They operationalized chasing losses as either three or more deposits within a 12-hour period or a deposit less than one hour after a previous bet. Chasing losses was one variable used in a cluster analysis which identified seven distinct clusters of online lottery players. They found a small cluster (3% of the total sample) which was characterized by a high gambling activity and which comprised a higher proportion of female players with a high probability of chasing behavior, and a large proportion of their bets comprising instant lottery games (which is unsurprising given that instant lottery games are continuous games with high event frequencies unlike most other lottery products such as bi-weekly lotto games). In another study of French online lottery players, Challet-Bouju et al. (2020) used the same operationalization of chasing losses as Perrot et al. (2018). Challet-Bouju et al. performed a cluster analysis and identified one cluster which was characterized by medium to very high gambling activity, played a higher number of game types, and had a high number of episodes of chasing losses. However, the studies by Perrot et al. (2018) and Challet-Bouju et al. (2020) both comprised online lottery players only. Moreover, their participants could only purchase lottery tickets and/or online instant win games. Therefore, their operationalization of chasing losses and their findings are not directly applicable to other types of gamblers such as sports bettors, roulette gamblers, and slots players. Online casino products such as slots games typically have a much higher event frequency and are more elaborate with respect to audio-visual aspects. The present authors believe that the time period to observe chasing losses should be less than 12 h in the case of online casino gambling. Several other studies have used shorter time periods to study online casino gambling behavior (e.g., Auer et al., 2014; Hopfgartner et al., 2021).
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Read More »Given the paucity of research with respect to chasing losses among online casino players using account-based data, the present study developed a number of new metrics using tracking data to reflect short-term chasing behavior as well as chasing on a day after losing on the previous day. To the best of the authors’ knowledge, the present study is the first to compare different metrics for chasing losses using real-world players and player tracking data. Given that the present study was necessarily exploratory, there was no specific hypothesis, other than it was expected that chasing losses would be observed to be more likely among higher involved gamblers.
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