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      Inside the decentralised casino: A longitudinal study of actual cryptocurrency gambling transactions

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      Publication date (Electronic):
      Journal: PLoS ONE
      Publisher: Public Library of Science

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          Abstract

          Decentralised gambling applications are a new way for people to gamble online. Decentralised gambling applications are distinguished from traditional online casinos in that players use cryptocurrency as a stake. Also, rather than being stored on a single centralised server, decentralised gambling applications are stored on a cryptocurrency’s blockchain. Previous work in the player behaviour tracking literature has examined the spending profiles of gamblers on traditional online casinos. However, similar work has not taken place in the decentralised gambling domain. The profile of gamblers on decentralised gambling applications are therefore unknown. This paper explores 2,232,741 transactions from 24,234 unique addresses to three such applications operating atop the Ethereum cryptocurrency network over 583 days. We present spending profiles across these applications, providing the first detailed summary of spending behaviours in this technologically advanced domain. We find that the typical player spends approximately $110 equivalent across a median of 6 bets in a single day, although heavily involved bettors spend approximately $100,000 equivalent over a median of 644 bets across 35 days. Our findings suggest that the average decentralised gambling application player spends less than in other online casinos overall, but that the most heavily involved players in this new domain spend substantially more. This study also demonstrates the use of these applications as a research platform, specifically for large scale longitudinal in-vivo data analysis.

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          Assessing the playing field: a prospective longitudinal study of internet sports gambling behavior.

          Anja Schumann, J Shaffer, Roan LaPlante … (2007)
          Internet gambling is growing rapidly, as is concern about its possible effect on the public's health. This paper reports the results of the first prospective longitudinal study of actual Internet sports gambling behavior during eight study months. Data include recorded fixed-odds bets on the outcome of sporting contests and live-action bets on the outcome of events within contests for 40,499 Internet sports gambling service subscribers who enrolled during February 2005. We tracked the following primary gambling behaviors: daily totals of the number of bets made, money bet, and money won. We transformed these variables into measures of gambling involvement. We analyzed behavior for both fixed-odds and live-action bets. The median betting behavior of the 39,719 fixed-odds bettors was to place 2.5 bets of 4 euro (approximately $5.3 US) every fourth day during the median 4 months from first to last bet. This typical pattern incurred a loss of 29% of the amount wagered. The median betting behavior of the 24,794 live-action bettors was to place 2.8 wagers of 4 euro every fourth day during the median duration of 6 weeks at a loss of 18% of the amount wagered. We also examined the behavior of empirically determined groups of heavily involved bettors whose activity exceeded that of 99% of the sample.
          Article 0 comments Cited 56 times – based on 0 reviews     Review now
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            How do gamblers start gambling: identifying behavioural markers for high-risk internet gambling.

            Julia Braverman, Howard J Shaffer (2012)
            The goal of this study is to identify betting patterns displayed during the first month of actual Internet gambling on a betting site that can serve as behavioural markers to predict the development of gambling-related problems. Using longitudinal data, k-means clustering analysis identified a small subgroup of high-risk gamblers. Seventy-three percent of the members of this subgroup eventually closed their account due to gambling-related problems. The characteristics of this high-risk subgroup were as follows: (i) frequent and (ii) intensive betting combined with (iii) high variability across wager amount and (iv) an increasing wager size during the first month of betting. This analysis provides important information that can help to identify potentially problematic gamblers during the early stages of gambling-related problems. Public health workers can use these results to develop early interventions that target high-risk Internet gamblers for prevention efforts. However, one study limitation is that the results distinguish only a small proportion of the total sample; therefore, additional research will be necessary to identify markers that can classify larger segments of high-risk gamblers.
            Article 0 comments Cited 47 times – based on 0 reviews     Review now
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              Using cross-game behavioral markers for early identification of high-risk internet gamblers.

              Julia Braverman, Debi A LaPlante, Sarah E Nelson … (2013)
              Using actual gambling behavior provides the opportunity to develop behavioral markers that operators can use to predict the development of gambling-related problems among their subscribers. Participants were 4,056 Internet gamblers who subscribed to the Internet betting service provider bwin.party. Half of this sample included multiple platform gamblers who were identified by bwin.party's Responsible Gambling (RG) program; the other half were controls randomly selected from those who had the same first deposit date. Using the daily aggregated Internet betting transactions for gamblers' first 31 calendar days of online betting activities at bwin.party, we employed a 2-step analytic strategy: (a) applying an exploratory chi-squared automatic interaction detection (CHAID) decision tree method to identify characteristics that distinguished a subgroup of high-risk Internet gamblers from the rest of the sample, and (b) conducting a confirmatory analysis of those characteristics among an independent validation sample. This analysis identified two high-risk groups (i.e., groups in which 90% of the members were identified by bwin.party's RG program): Group 1 engaged in three or more gambling activities and evidenced high wager variability on casino-type games; Group 2 engaged in two different gambling activities and evidenced high variability for live action wagers. This analysis advances an ongoing research program to identify potentially problematic Internet gamblers during the earliest stages of their Internet gambling. Gambling providers and public policymakers can use these results to inform early intervention programs that target high-risk Internet gamblers.
              Article 0 comments Cited 33 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Oliver J. Scholten:
                ORCID: https://orcid.org/0000-0002-6724-0557
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: Project administrationRole: ResourcesRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                David Zendle: Role: MethodologyRole: SupervisionRole: Writing – review & editing
                James A. Walker: Role: MethodologyRole: Project administrationRole: SupervisionRole: Writing – review & editing
                He Debiao: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS One
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2020
                Publication date (Electronic): 28 October 2020
                Volume: 15
                Issue: 10
                Electronic Location Identifier: e0240693
                Affiliations
                [001] Department of Computer Science, University of York, York, Yorkshire, United Kingdom
                Wuhan University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-6724-0557
                Article
                Publisher ID: PONE-D-20-20436
                DOI: 10.1371/journal.pone.0240693
                PMC ID: 7592737
                PubMed ID: 33112917
                SO-VID: f6b93ac9-d0d2-4d23-97f5-48457995f51f
                Copyright © © 2020 Scholten et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 10 July 2020
                Date accepted : 1 October 2020
                Page count
                Figures: 4, Tables: 6, Pages: 18
                Funding
                This work was supported by the EPSRC Centre for Doctoral Training in Intelligent Games & Games Intelligence (IGGI) [EP/L015846/1] (OJS, PhD Scholarship) and the Digital Creativity Labs (digitalcreativity.ac.uk) (JAW, Research Fellow), jointly funded by EPSRC/AHRC/Innovate UK under grant no. EP/M023265/1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Biology and Life Sciences
                Subject: Psychology
                Subject: Behavior
                Subject: Recreation
                Subject: Gambling
                Subject: Social Sciences
                Subject: Psychology
                Subject: Behavior
                Subject: Recreation
                Subject: Gambling
                Subject: Biology and Life Sciences
                Subject: Psychology
                Subject: Behavior
                Subject: Recreation
                Subject: Games
                Subject: Social Sciences
                Subject: Psychology
                Subject: Behavior
                Subject: Recreation
                Subject: Games
                Subject: Social Sciences
                Subject: Economics
                Subject: Finance
                Subject: Research and Analysis Methods
                Subject: Mathematical and Statistical Techniques
                Subject: Statistical Methods
                Subject: Test Statistics
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Statistics
                Subject: Statistical Methods
                Subject: Test Statistics
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Probability Theory
                Subject: Statistical Distributions
                Subject: Biology and Life Sciences
                Subject: Psychology
                Subject: Behavior
                Subject: Social Sciences
                Subject: Psychology
                Subject: Behavior
                Subject: Medicine and Health Sciences
                Subject: Epidemiology
                Subject: Medical Risk Factors
                Subject: Science Policy
                Subject: Open Science
                Subject: Open Data
                Custom metadata
                Data Availability All files are available in the OSF repository available at https://osf.io/8bfyj/.

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                ScienceOpen disciplines: Uncategorized

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