Esports Betting and Skin Gambling: A Brief History
Bibliographic record
Abstract
The rising popularity of competitive video gaming (“esports”) has attracted the involvement of the gambling industry, with esports cash betting available from the majority of wagering operators. In addition, an unregulated gambling subculture around esports has arisen, with virtual game items known as “skins” being used as currency to place bets on esports and third-party sites that host games of chance. Little is presently known about these novel forms of gambling, although there are growing concerns that these products may place some vulnerable consumers (e.g., youth) at risk of gambling-related harm. The current paper provides a historical overview of esports betting and skin gambling globally, drawing on the limited research literature available, including academic journals, government publications, conference presentations, and media reports. Topics briefly covered in the review include esports, skins, history of the gambling products, gambling exposure and accessibility, research findings (e.g., prevalence, awareness, demographic characteristics, gambling behaviour, problem gambling), illegal activities, changes to skins and the skin gambling market, and industry and government responses to concerns arising from these new gambling products (e.g., underage gambling). The intention of this paper is to provide the general public, academics, governments, and other key stakeholders with an understanding of the evolving landscape around esports betting and skin gambling, the type of bettors that these forms of gambling attract, and the potential adverse consequences of these activities.Résumé La popularité croissante du jeu vidéo de compétition (« e-sport ») a attiré la participation du secteur du jeu qui y voit une occasion de pari d’argent « e-sport » auprès de la majorité des exploitants de jeux d’argent. De plus, une sous-culture du jeu non réglementée autour des e-sports est apparue, des objets virtuels appelés « skins » étant utilisés comme monnaie pour placer des paris sur des sites sportifs et de tiers hébergeant des jeux de hasard. Cependant, on en sait actuellement peu sur ces nouvelles formes de jeu et on craint de plus en plus que ces produits ne mettent certains consommateurs vulnérables (par exemple, les jeunes) en situation de risque de préjudice lié au jeu. Le présent document fournit un aperçu historique des paris sportifs et des paris d’objets virtuels (skin gambling) à l’échelle mondiale, en s’appuyant sur le peu de littérature de recherche qui existe, notamment des revues spécialisées, des publications gouvernementales, des présentations à des conférences et des reportages dans les médias. Les sujets brièvement abordés dans la revue incluent : les sports, les « objets virtuels », l’historique des produits de jeu, l’exposition et l’accessibilité au jeu, les résultats de recherche (c’est-à-dire, la prévalence, la sensibilisation, les caractéristiques démographiques, le comportement de jeu, le jeu compulsif), les activités illégales, l’évolution du marché du jeu de hasard et des jeux d’objets virtuels et les réponses de l’industrie et du gouvernement aux préoccupations découlant de ces nouveaux produits de jeu (par exemple, le jeu chez les mineurs). L’objectif de ce document est de fournir au grand public, aux universitaires, aux gouvernements et aux autres parties prenantes une compréhension de l’évolution des paris sportifs et des jeux de hasard, du type de parieurs qu’ils attirent et des conséquences néfastes potentielles de ces activités.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".