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A Descriptive Analysis of Demographic and Behavioral Data from Internet Gamblers and Those Who Self-exclude from Online Gambling Platforms

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Abstract

As the popularity of internet gambling increases, the increased opportunities to participate serve to heighten concerns about the potential for gambling related harm. This paper focuses on self-exclusion as one of the main responsible gaming interventions, and is split into three sections. Firstly, we set out a three-tier model for assessing at-risk gambling behaviors which examines player exhibited, declared and inferred behavior. Secondly, we present a literature review relating to who self-excludes and whether self-exclusion is effective. Finally, we report the results of an analysis of the exhibited behavior of internet self-excluders as sampled from a research cohort of over 240,000 internet gaming accounts. Our analysis of self-excluders (N = 347) versus a control group (N = 871) of gamblers indicates self-excluders are younger than the control group, more likely to suffer losses and more likely to adopt riskier gambling positions. Unlike some previous studies, there was little difference in terms of mean gambling hours per month or minutes per session. Some self-excluders (N = 306) can be tracked from the date their account was created through their self-exclusion history, indicating a large number of very quick self-exclusions (e.g., 25 % within a day) and a small set of serial self-excluders. Younger and older males are likely to self-exclude faster than middle-aged males (N = 242), but there is no such age pattern across female self-excluders (N = 63).

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Notes

  1. This number does not include sites that only provide betting on financial markets.

  2. www.online.casinocity.com is a US-based gambling portal that provides continually updated listing and access to available online sites, as well as comprehensive listing of online gaming jurisdictions, online gaming site owners, online gambling software, and online gambling news.

  3. For instance, GamCare, the problem gambling treatment provider and responsible gambling accreditation service, and the Gambling Commission, the United Kingdom’s regulator, define self-exclusion in the context of player who excludes for 6 + months (see GamCare 2010; Gambling Commission 2010).

  4. 105 players were removed entirely from the self-excluding cohort as a result of this approach as they had self-excluded in their first month of gambling.

  5. The phrase wager-sessions is deliberately used as a non-standard term, since the different platforms which record data via GTECH are understood to transcribe gambling activity into the dataset in different ways for different games. For the majority of games, a single wager-session corresponds to a single bet, with an amount of money wagered (which may be zero, where a hand is sat out or a player gambles using bonus money from the program) and the potential for winnings. For a small number of games/platforms, it is possible for a single wager-session to correspond to multiple bets. We believe that this could be as a result of how the casino download client on the gaming site integrates with the operator back office system. See “Appendix” for more detail.

  6. The self-excluding cohort would be winning EUR 112 k on average in a winning month if we include the EUR 24 m referred to in this paragraph.

  7. Account closure refers to players who permanently close their accounts with the gambling operator and should not be confused with cooling-off or self-exclusion, which typically refers to suspending the account for a defined time period.

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Author information

Authors and Affiliations

  1. Bet Buddy, 4th Floor, Longbow House 14-20 Chiswell Street, London, EC1Y 4TW, UK

    Simo Dragicevic & Christian Percy

  2. GTECH, 70 Chancery Lane, London, WC2A 1AF, UK

    Aleksandar Kudic

  3. The Gambling Lab, 174 Hawton Road, Newark, Nottinghamshire, NG24 4RH, UK

    Jonathan Parke

Authors
  1. Simo Dragicevic
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  2. Christian Percy
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  3. Aleksandar Kudic
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  4. Jonathan Parke
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Correspondence to Simo Dragicevic.

Appendix 1: Wager-sessions by game

Appendix 1: Wager-sessions by game

Of 120 different games listed, 85 only register a single bet count on every wager-session, corresponding to 63% of all wager-sessions. A further 36.5% are accounted for by French Roulette, which has a bet count of 1 per wager-session in the vast majority of cases, with an average number of bets per wager-session of 1.00002. For this reason, this analysis treats a wager-session as largely equivalent to a single bet. The most common games with a bet count greater than 1 are listed below:

Game name

Average length of a wager-session (min)

Average number of bets/wager-session

Typical prevalence in raw dataset (%)

French Roulette

17

1

36.671

Tumbletons

14

1

0.561

Lucky’s Diner

25

1

0.253

French Roulette-Instant

197

42

0.021

Blackjack-Instant

7,799

31

0.009

Formula X-Instant

49

44

0.007

European Roulette-Instant

19

34

0.006

Fortunes of Egypt-Instant

6,240

80

0.005

Maya Gold-Instant

18

59

0.004

Bonus Madness-Instant

14

131

0.002

Fire Burner-Instant

16,871

47

0.002

Casino War-Instant

5

24

0.002

Jungle Jim-Instant

34,915

33

0.002

100 Reel Bonus Slot-Instant

10

24

0.002

Fun Fair-Instant

18

41

0.001

Kangaroo Zoo-Instant

6

41

0.001

Full House-Instant

8

9

0.001

Fast Poker-Instant

10

55

0.001

Casino Holdem-Instant

10

47

0.001

Relevant details on the most prevalent games are listed below:

Game name

Average length of a wager-session (min)

Average number of bets/wager-session

Typical prevalence in raw dataset (%)

Share of wager-sessions with zero-valued wagers (%)

French Roulette

16.9

1.000

36.7

7

Texas Holdem

0.1

1.000

27.9

42

Blackjack

13.0

1.000

6.2

4

European Roulette

15.8

1.000

3.7

5

Bonus Madness

6.6

1.000

1.8

4

Fortunes of Egypt

14.5

1.000

1.7

5

Racetrack Roulette

17.7

1.000

1.5

6

Casino War

5.9

1.000

1.3

3

Hot Cash

12.3

1.000

1.2

3

Aladdins Lamp

4.6

1.000

1.0

6

Bonus Poker

6.6

1.000

0.9

2

Fire Burner

11.0

1.000

0.8

5

Jungle Jim

7.1

1.000

0.8

5

Casino Holdem

8.6

1.000

0.8

2

Five Card Draw 7-A

0.0002

1.000

0.7

30

Extreme Games

4.9

1.000

0.7

8

Mystic Fortune

8.9

1.000

0.7

5

Formula X

13.3

1.000

0.6

5

Tumbletons

14.0

1.000

0.6

3

  1. Note that this appendix is based on ~465 k data records from the original GTECH player data source. As such it represents a reasonable average across games to enable an understanding of how the software records gaming behavior in aggregate, but is not directly linked with the data used throughout the rest of this paper

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Dragicevic, S., Percy, C., Kudic, A. et al. A Descriptive Analysis of Demographic and Behavioral Data from Internet Gamblers and Those Who Self-exclude from Online Gambling Platforms. J Gambl Stud 31, 105–132 (2015). https://doi.org/10.1007/s10899-013-9418-1

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