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Tilman [email protected]
The Quality of Quantity -
Behavioural Indicators of Risky Online Gambling
21th February 2017
The Centre for Gambling Research at UBC is supported by the British Columbia Lottery Corporation and the Province of BC Government. This project received additional support from the British Columbia Ministry of Finance Gambling Policy and Enforcement Branch.
Disclosure
Online Gambling in British ColumbiaPrevious research into online gamblingA trial-by-trial approach: ChasingChallenges & Outlook
Agenda
Online Gambling in British Columbia
Agenda
The PlayNow Platform: Online gambling platform from BCLC for BC and Manitoba.
14
Source: Online Gambling in British Columbia, Lesch, Limbrick-Oldfield, Clark, 2017
BC Only
Behavioural measures differ between games types for median & engaged users.
Source: Online Gambling in British Columbia, Lesch, Limbrick-Oldfield, Clark, 2017
The majority of players access PlayNow to place lottery bets.
3
Top 20% most engagement players
# of
Bet
s pl
aced
Hour of day
‘after-breakfast’ effect
‘end of work’ effect‘(Not) At-Work’
plateau
Daily pattern of gambling follow a working populations availability.
Previous research into online gambling
Agenda
Data Set– Daily aggregates from bwin based in Austria, website world wide
available (2005-2007)– Games Types: sports betting (most), internet poker, casino
Measures– Daily aggregates of
• number of bets, bets per day, Euros per bet, total wagered, net loss, percent lost
– Number of active days before the first deposit was made– Duration of play, days active– Monetary deposits to, and withdrawals from player’s account– Trajectory of first month wagers– Reason for account closure
7
Previous research on online gambling relied on daily aggregates & quantitative measures.
Behavioural clustering of account closures.
Source: How do gamblers start gambling: identifying behavioural markers for high-risk internet gambling (Braverman & Shaffer, 2010
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530 playersclosed their account
No interest in gambling
Due to gambling related problems
Not satisfied with service
378 (71%)
15 (3%)22 (4%)
115 (22%)
Clustering on first month behaviour
Moderate Betting
High Intensity, low variability
Low first month activityHigh intensity & variability
High accordance for closing due to gambling
related problems
33%
48%
19%
Reason for account closure
Decision Tree classification of RG-grouped vs. non-grouped customers.
Source: Using Cross-Game Behavioral Markers for Early Identification of High-Risk Internet Gamblers (Braverman et al. 2013, similar: Gray et al. 2012)
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All
> 138
> 45.5
Number of gambling
activities <2, 2 or >2
Live action staked
variability >138 <138
Casino stakes
variability <45.5>45.5
50/503037
% NControl 10 17
Target 90 158
Total 100 175
% NControl 9 12
Target 91 116
Total 100 128
2
>2Not high
riskNot high
risk
Not high risk
HIGH RISK
HIGH RISK
A trial-by-trial approach: Chasing
Agenda
Behavioural markers of online gambling: e.g. Chasing’Loss chasing’: Trying to ‘win’ back previously lost funds.Increased bet sizes or prolonged betting after a series of losses in an attempt to win back funds (Lesieur, 1984; American Psychiatric Association, 2013).
Operationalisations:- increase bet size- accelerate betting- play longer- play quicker again
Over the course of a gambling session, people increase their wager about 40%.
Relative time in session
Wag
er re
lativ
e to
ses
sion
sta
rt
Average amount bet throughout session
Correlation coefficient (chasing)
Freq
uenc
y
Distribution of Correlation Coefficients
Increase of wagerDecrease of wager
Zero Line (No Bias) Median (Bias)
Table Games only
On tables games, there is a bias towards increasing one’s bets.
On slots machines, on average there is no bias.
Freq
uenc
y
Increase of wagerDecrease of wager
Zero Line (No Bias)
Median (No Bias)
Distribution of Correlation Coefficients
Slot Machines only
Correlation coefficient (chasing)
Session outcome
Session wager
Correlation>.6
.6 < >.2
0 < >.2
0 > < -.2
-.2 > < -.6
< -.6
There is no simple relationship between chasing and accumulated wager or session outcome.
Chasing (wager increase) shows limited relationship to other measures of gambling.
all slots tablesNumber of bets .019 .074 -.065
Accumulated wager .102 .122 .050
Session outcome .105 .108 -.007
Correlation of Chasing with aggregated measures of gambling
session wager session Outcomenumber of bets
wager size correlationAll Slots MixedTables
Number of chasing sessions (>.5) by user
Num
ber o
f Use
rsTable Games only
Distribution of chasing session (>.5) by user
Some users show larger numbers of chasing sessions.
Percentage of chasing sessions (>.5) by user
Num
ber o
f Use
rsTable Games only
A subset of users appear to show chasing on almost every gambling session.
Distribution of chasing session (>.5) in % by user
Learning & next stepsLearnings:• No one size fits all
– Different games require different measures (e.g. bet size variance slots vs. tables)
– Varying consistency within and between people• Differentiate average and extreme effects• Limited relation of previous aggregate measures
Next steps:• Winning vs. losing• Look at subsets of players• Operationalisation of chasing:• Within vs. between players
- accelerate betting- play longer- play quicker again
Challenges & Outlook
Agenda
Identifying at risk players requires knowing who at risk players are.
- ’Let the data speak for itself’ (clustering – unsupervised learning)- What are the ‘right’ measures/markers for problematic gambling?- What do any method’s results have to say about real behaviour?
- ’Train the data to identify certain individuals’ (classification –supervised learning)- Who are individuals with problematic behaviours?- Samples of account closures, voluntary self-excluders, etc. can
provide some external confirmation.
At the moment, it’s all about the measures. How can we identify problematic play.
- Additional behavioural measures- Speed of play- Streak- & sequence effects- Predictive modelling
- Log-On and Log-Off- Choice of game, game switches- Wager size
- The People Dilemma- Getting the getting people with the right skills.
Acknowledgements
CGRProfessor Luke ClarkDr Eve Limbrick-Oldfield
www.cgr.psych.ubc.ca@CGR_UBC
BCLCDr Kahlil PhilanderBradley BodenhamerMichaela Becker
Questions?
Dr Tilman [email protected]
Thank you for your attention!
Areas of online gambling research
1. Descriptive analysis of online gambling– Prevalence study– Player segmentation– Comparative analysis within individuals, e.g. daily, monthly,
seasonal, yearly playing patterns
2. Identification of at risk players (e.g. Harvard’s Transparency Project)– Personalized interventions– Timely interventions– Predictive Analytics
• Determine likelihood of problem gambling event, e.g money upload, self exclusion, loss chasing
4
Confirmation of theory and laboratory findings in naturalistic data
http://www.egba.eu/facts-and-figures/market-reality/
Global Market Share of online Gambling
Types of Online Gambling
2015
The online Gambling Market
Motivation to study online gambling
• Rapid growth since early 2000s• Ubiquitous 24/7 availability• Different types of players• Much easier and quicker feedback to changes in
regulation• Easier access to playing data• Easy Implementation of additional measures such as
questionnaires, etc. possible
4
Cognitive Biases in Gambling“ A cognitive bias is a pattern of deviation in judgment and decision-making, whereby inferences about situations and other people may be drawn in an illogical fashion.” Hot Hand Fallacy:
fallacious belief that a person who has experienced success with a random event has a greater chance of further success in additional attempts.
Illusion of Control:tendency for people to overestimate their ability to control events.
Sequential/ Streak Effects: (“Gambler’s Fallacy”: mistaken belief that, if something happens more (less) frequently than normal during some period, it will happen less (more) frequently in the future - balancing).
Cognitive distortions play an important role in the development and maintenance of pathological gambling.
Behavioural markers of online gambling II: Betting Speed
Translation for online gambling:Time between one bet and the next bet within the same
session.
Mean overall Betting Speed by individual
Mean Betting Speed difference between winning and losing by individual
Zero Line (No Bias)
Slower After WinsSlower after Losses
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>0:Slots: ~75%Tables: ~65%
