Internet Gambling: A Critical Review of Behavioural Tracking Research

Abstract

This paper reviews and analyzes studies that are focused on Internet gambling with the use of behavioural tracking and big data to identify gambling behaviour. The behaviour of gamblers has been extensively studied and much has been published on the subject. The vast majority of research has relied on self-reported gambling behaviour or case study research. With the advent of the Internet, however, it has become possible for researchers to remotely study the real behaviour of gamblers. The goal has been to empirically describe playing behaviour in several conditions and contexts. Existing research, conducted since the 2000s, focuses on several forms of gambling such as sports betting, casino, poker, and lottery, but there is still only a concise body of research on gambling behaviour with the use of Internet gambling tracking data. Most studies are based on the same databases, meaning that a few companies and websites were the basis for most of the research produced so far. It is important to explore new sources of information, methodologies, and approaches to enrich discussion and contribute to a better understanding of this field. The empirical analysis of gambling behaviour with the use of tracking data was found to greatly contribute to the understanding of player behaviour, despite existing limitations and problems. Considering that Internet gambling behavioural tracking is still a fairly recent phenomenon, much can still be done to further develop this field of research.Cet article examine et analyse les études axées sur le jeu en ligne qui recourent au suivi comportemental et aux mégadonnées pour cerner le comportement lié au jeu. Or, on a souvent étudié le comportement des joueurs et on a beaucoup publié sur le sujet, mais jusqu’à présent, la majeure partie de la recherche repose sur le comportement autodéclaré ou la recherche fondée sur les études de cas. Avec l’avènement d’Internet, il est dorénavant possible pour les chercheurs d’étudier à distance le comportement réel des joueurs. L’objectif a donc consisté à décrire de manière empirique le comportement lié au jeu dans plusieurs conditions et contextes. La recherche existante, menée depuis les années 2000, se concentre sur plusieurs formes de jeux de hasard tels que les paris sportifs, le casino, le poker et la loterie. Mais à ce jour, il n’existe qu’un corpus de recherches très concis sur le comportement lié au jeu qui utilise des données de suivi sur le jeu par Internet. La plupart des études sont fondées sur les mêmes bases de données, car seulement quelques entreprises et sites Web ont servi de base à la plupart des recherches produites jusqu’à maintenant. Il est donc important d’explorer de nouvelles sources d’information, méthodologies et approches pour pouvoir enrichir les discussions et améliorer la compréhension de ce domaine. L’analyse empirique du comportement lié au jeu à l’aide de données de suivi a ainsi largement contribué à la compréhension du comportement du joueur en dépit des limites et problèmes existants. Si l’on tient compte du fait que le suivi comportemental du jeu sur Internet est un phénomène encore assez récent, il reste beaucoup à faire pour exploiter davantage ce domaine de recherche.

A Literature Review and Gap Analysis of Emerging Technologies and New Trends in Gambling

“…The area of Internet (or online) gambling is a key technological trend in gambling and was the focus of 46 peer-reviewed articles. The articles focused predominantly on the characteristics of Internet gamblers and their gambling behaviour in the online environment, with one literature review exploring the use of behavioural tracking and big data studies of Internet gambling [14]. The studies have various foci and findings, but overall demonstrate that Internet gamblers are not a homogeneous group [15].…”

A machine learning perspective on responsible gambling

“…This research question is motivated by the statistical concept of the "Gambler's Ruin," which shows how a gambler with negative expected returns will eventually lose all their money (Harik, Cantú-Paz, Goldberg, & Miller, 1999;Mohan, 1955). The 10% proportion was chosen heuristically by us, in the absence of us having any human betting data to compare against (Chagas & Gomes, 2017). Larger percentage bet sizes can compress the differences between betting strategies, for example, with a bet of 50% one losing bet hits the "half-life" definition.…”

Request-a-bet sports betting products indicate patterns of bettor preference and bookmaker profits

“…In addition, request-a-bet products can also provide a unique perspective on patterns of bettor preference and bookmaker profits compared to conventional sports betting. Data on how sports bettors actually bet are not routinely made available to researchers (Cassidy, Loussouarn, & Pisac, 2013;Chagas & Gomes, 2017). Researchers have occasionally used prediction contests (Brown & Reade, 2019;Butler, Butler, & Eakins, 2020;Levitt, 2004) or built their own experimental betting platforms (Simmons, Nelson, Galak, & Frederick, 2011), but these data sources may also fail to properly capture naturally-occurring sports betting behavior.…”