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Open Access 01-09-2021 | Original Paper

Online Problem Gambling: A Comparison of Casino Players and Sports Bettors via Predictive Modeling Using Behavioral Tracking Data

Authors: Ivan Ukhov, Johan Bjurgert, Michael Auer, Mark D. Griffiths

Published in: Journal of Gambling Studies | Issue 3/2021

Abstract

In this study, the differences in behavior between two groups of online gamblers were investigated. The first group comprised individuals who played casino games, and the second group comprised those who bet on sports events. The focal point of the study was on problem gambling, and the objective was to identify and quantify both common and distinct traits that are characteristic to casino and sports problem gamblers. To this end, a set of gamblers from the gaming operator LeoVegas was studied. Each gambler was ascribed two binary variables: one separating casino players from sports bettors, and one indicating whether there was an exclusion related to problem gambling. For each of the four combinations of the two variables, 2500 gamblers were randomly selected for a thorough comparison, resulting in a total of 10,000 participants. The comparison was performed by constructing two predictive models, estimating risk scores using these models, and scrutinizing the risk scores by means of a technique originating from collaborative game theory. The number of cash wagers per active day contributed the most to problem-gambling-related exclusion in the case of sports betting, whereas the volume of money spent contributed the most to this exclusion in the case of casino players. The contribution of the volume of losses per active day was noticeable in the case of both casino players and sports bettors. For casino players, gambling via desktop computers contributed positively to problem-gambling-related exclusion. For sports bettors, it was more concerning when the individual used mobile devices. The number of approved deposits per active day contributed to problem-gambling-related exclusion to a larger extent for sports bettors than casino players. The main conclusion is that the studied explanatory variables contribute differently to problem-gambling-related exclusion among casino players and sports bettors.

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Abbott, M., Stone, C., Billi, R., & Yeung, K. (2016). Gambling and problem gambling in Victoria, Australia: Changes over 5 years. Journal of Gambling Studies, 32(1), 47–78. https://doi.org/10.1007/s10899-015-9542-1.CrossRefPubMed

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (fifth edition). Arlington, VA: American Psychiatric Publishing. https://doi.org/10.1176/appi.books.9780890425596.

Auer, M., & Griffiths, M. D. (2013). Voluntary limit setting and player choice in most intense online gamblers: An empirical study of gambling behaviour. Journal of Gambling Studies, 29(4), 647–660. https://doi.org/10.1007/s10899-012-9332-y.CrossRefPubMed

Auer, M., & Griffiths, M. D. (2015). Testing normative and self-appraisal feedback in an online slot-machine pop-up in a real-world setting. Frontiers in Psychology, 6(339). https://doi.org/10.3389/fpsyg.2015.00339.

Auer, M., & Griffiths, M. D. (2016). Should voluntary “self-exclusion” by gamblers be used as a proxy measure for problem gambling? MOJ Addiction Medicine & Therapy, 2(2), 00019. https://doi.org/10.15406/mojamt.2016.02.00019.

Auer, M., Hopfgartner, N., & Griffiths, M. D. (2018). The effect of loss-limit reminders on gambling behavior: A real-world study of Norwegian gamblers. Journal of Behavioral Addictions, 7, 1056–1067. https://doi.org/10.1556/2006.7.2018.106.CrossRefPubMedPubMedCentral

Auer, M., Malischnig, D., & Griffiths, M. D. (2014). Is “pop-up” messaging in online slot machine gambling effective as a responsible gambling strategy? Journal of Gambling Issues, 29, 1–10. https://doi.org/10.4309/jgi.2014.29.3.CrossRef

Bonello, M., & Griffiths, M. D. (2017). Analyzing consumer protection for gamblers across different online gambling operators: A descriptive study. Gaming Law Review and Economics, 21(3), 278–285. https://doi.org/10.1089/glre.2017.2134.CrossRef

Braverman, J., & Shaffer, H. (2012). How do gamblers start gambling: Identifying behavioural markers for high-risk internet gambling. European Journal of Public Health, 22(2), 273–278. https://doi.org/10.1093/eurpub/ckp232.CrossRefPubMed

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In International conference on knowledge discovery and data mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785.

Dragičević, S., Tsogas, G., & Kudic, A. (2011). Analysis of casino online gambling data in relation to behavioural risk markers for high-risk gambling and player protection. International Gambling Studies, 11(3), 377–391. https://doi.org/10.1080/14459795.2011.629204.CrossRef

Gainsbury, S. (2015). Online gambling addiction: The relationship between internet gambling and disordered gambling. Current Addiction Reports, 2, 185–193. https://doi.org/10.1007/s40429-015-0057-8.CrossRefPubMedPubMedCentral

Griffiths, M. D. (2014). The use of behavioural tracking methodologies in the study of online gambling. SAGE Research Methods Cases. https://doi.org/10.4135/978144627305013517480.CrossRef

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer. https://doi.org/10.1007/978-0-387-21606-5.CrossRef

Hing, N., Russell, A., Vitartas, P., & Lamont, M. (2016). Demographic, behavioural and normative risk factors for gambling problems amongst sports bettors. Journal of Gambling Studies, 32(2), 625–641. https://doi.org/10.1007/s10899-015-9571-9.CrossRefPubMed

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185.CrossRef

LaBrie, R., & Shaffer, H. (2011). Identifying behavioral markers of disordered internet sports gambling. Addiction Research & Theory, 19(1), 56–65. https://doi.org/10.3109/16066359.2010.512106.CrossRef

Lopez-Gonzalez, H., Estévez, A., & Griffiths, M. (2018). Internet-based structural characteristics of sports betting and problem gambling severity: Is there a relationship? International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-018-9876-x.

Lundberg, S., Erion, G., & Lee, S.-I. (2018). Consistent individualized feature attribution for tree ensembles. arXiv. eprint: arXiv:1802.03888.

Lundberg, S., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.

Palmer, C. (2014). Sports betting research: Literature review. Tasmania: University of Tasmania.

Percy, C., França, M., Dragičević, S., & Garcez, A. D. (2016). Predicting online gambling self-exclusion: An analysis of the performance of supervised machine learning models. International Gambling Studies, 16(2), 193–210. https://doi.org/10.1080/14459795.2016.1151913.CrossRef

Philander, K. (2014). Identifying high-risk online gamblers: A comparison of data mining procedures. International Gambling Studies, 14(1), 53–63. https://doi.org/10.1080/14459795.2013.841721.CrossRef

PricewaterhouseCoopers, & Responsible Gaming Council of Canada. (2016). Remote gambling research: Interim report on phase I. London: GambleAware.

PricewaterhouseCoopers, & Responsible Gaming Council of Canada. (2017). Remote gambling research: Interim report on phase II. London: GambleAware.

Russell, A., Hing, N., Browne, M., Li, E., & Vitartas, P. (2018). Who bets on micro events (microbets) in sports? Journal of Gambling Studies. https://doi.org/10.1007/s10899-018-9810-y.

Sarkar, S., Weyde, T., Garcez, A. D., Slabaugh, G., Dragičević, S., & Percy, C. (2016). Accuracy and interpretability trade-offs in machine learning applied to safer gambling. In CEUR Workshop Proceedings (vol. 1773).

Shapley, L. (1953). A value for n-person games. In H. W. Kuhn & A. W. Tucker (Eds.), Contributions to the theory of games II (pp. 307–317). Princeton, NJ: Princeton University Press.

Stewart, M., & Wohl, M. (2013). Pop-up messages, dissociation, and craving: How monetary limit reminders facilitate adherence in a session of slot machine gambling. Psychology of Addictive Behaviors, 27(1), 268–273. https://doi.org/10.1037/a0029882.CrossRefPubMed

van der Maas, M., Shi, J., Elton-Marshall, T., Hodgins, D., Sanchez, S., Lobo, D., et al. (2019). Internet-based interventions for problem gambling: Scoping review. Journal of Medical Internet Research, 6(1), e65. https://doi.org/10.2196/mental.9419.CrossRef

Title: Online Problem Gambling: A Comparison of Casino Players and Sports Bettors via Predictive Modeling Using Behavioral Tracking Data
Authors: Ivan Ukhov
Johan Bjurgert
Michael Auer
Mark D. Griffiths
Publication date: 01-09-2021
Publisher: Springer US
Published in: Journal of Gambling Studies / Issue 3/2021
Electronic ISSN: 1573-3602
DOI: https://doi.org/10.1007/s10899-020-09964-z

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Online Problem Gambling: A Comparison of Casino Players and Sports Bettors via Predictive Modeling Using Behavioral Tracking Data

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

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