Who self-excludes? A statistical analysis of Internet gambling behaviour and player demographics

Transcription

1 Who self-excludes? A statistical analysis of Internet gambling behaviour and player demographics Chris Percy, EASG Conference, Helsinki, September 2014

2 Research Collaboration Study 1: Analyse casino / - Overview of problem gambling literature - IT platform - Statistical researchers poker internet players using Harvard method Study 2: Describe internet - Multi-site gambling platform - Anonymised data - 240,000 accounts casino/poker self-excluders Study 3: Predicting selfexclusion events

3 Research Collaboration Focus of today Study 1: Analyse casino / - Overview of problem gambling literature - IT platform - Statistical researchers poker internet players using Harvard method Study 2: Describe internet - Multi-site gambling platform - Anonymised data - 240,000 accounts casino/poker self-excluders S.Dragicevic, C.Percy, A.Kudic & J.Parke Journal of Gambling Studies, Nov 2013 Study 3: Predicting selfexclusion events

4 Player Data Universe or Wish List Personal Data Personal Identification, Demographic (Lifestyle, Sex, Age, Education, Race, etc), Purchase Transactions, Affiliations, , Chat, Call Centre, Medical, Surveys You Have Filled, Photos, Videos, Credit Score, RG Data, etc Machine Generated Data Web Logs, Click Streams, Call Detail Records, Session Records, GPS Logs, Systems Monitoring Records Social Network Data Your Friends, Your Recommendations or Likes/Dislikes, Who you Communicate With, What You Communicate About, Who You Follow Source: Bet Buddy

5 Our Data Machine Generated Data Web Logs, Click Streams, Call Detail Records, Session Records, GPS Logs, Systems Monitoring Records Personal Data Personal Identification, Demographic (Lifestyle, Sex, Age, Education, Race, etc), Purchase Transactions, Affiliations, , Chat, Call Centre, Medical, Surveys You Have Filled, Photos, Videos, Credit Score, RG Data, etc Source: Bet Buddy Social Network Data Your Friends, Your Recommendations or Likes/Dislikes, Who you Communicate With, What You Communicate About, Who You Follow Demographics Gender Age Country (mostly DE / AT) Gambling behaviour Start/end of each session Amount bet per wager Amount won per wager Type of game Purchases/withdrawals transactions (not used) Self-exclusion Start date of self-exclusion Length (varies from 1 day to 1+ years, to unlimited)

6 What we did 2x datasets Self-excluding players (SE) 604 players with 716 self-exclusions Apr Jul 2011 Limit to single self-exclusion event > 30 days: 557 players Incidence of self-exclusion: % (5% > 1x SE) Gambling data from Jun 2008 Jul 2011 Control group players (CG) Stratified sample of 871 players (post data cleaning) Representative by age, gambling scale/frequency, gender, poker/casino and account status Gambling data from Jan 2009 Dec 2010 Data limitation: not yet self-excluded

7 What we did Creating comparable cohorts with max. N Length of time prior to first self-exclusion Self-excluding players with full gambling data from date account was created N=306 Compare gambling behaviour with control group Remove the month in which player self-excluded Use Jan 2009 Dec 2010 gambling data only N=347 [SE] N=871 [CG] Compare demographics with control group Use all self-excluders with demographic data available N=557 [SE] N=871 [CG]

8 Results Length of time prior to self-exclusion [N] % excluded within a day Age and gender? On average men self-exclude earlier (39 days vs 46) Not statistically significant Men under 25 self-exclude days faster than older men, particularly aged No age-based link for women Only 16% waited over 3 months to self-exclude 0 Same day 1-5 days 6-15 days days days days days days

9 Results Time spent gambling [percentile of cohort] Gambling hours / month SE Mean: 19.5 hrs CG Mean: 19.0 hrs P-value 0.79 No meaningful difference between the cohorts Similarly no difference 70 by # wager-sessions / active 60 gambling day (avg. 9) Self-excluders are slightly more homogeneous 30 than CG but not dramatic Minutes / wager-session SE Mean: 26.3 mins CG Mean: 30.1 mins P-value 0.17 Self-excluders Control group Self-excluders Control group

10 Results Losses [percentile of cohort] Avg loss / month [EUR] 1) SE Mean: 897 EUR CG Mean: 646 EUR P-value 0.00 Those who selfexcluded lost 250 EUR extra per month on average As well as losing more on average, the winners among selfexcluders also win more Perhaps they place riskier, higher wager bets? th 25th Median 75th 95% Self-excluders Control group 1) Excl. one lucky self-excluding player who won EUR 24 m in one bet, enough to skew overall averages

11 Results Variety of game play [percentile of cohort] Different games played [#] SE Mean: 13 CG Mean: 16 P-value 0.00 Self-excluders played slightly fewer games and were more focused on casino - Perhaps partly as they had played for a shorter period and experimented less, but they were also less loyal to their favourite game 5th 25th Median 75th 95% Self-excluders Control group % time on casino (vs poker) SE Mean: 97% CG Mean: 86% P-value 0.00 % time on top-played game SE Mean: 62% CG Mean: 73% P-value 0.00

12 Results Demographics of self-excluding players [% cohort] 35% 30% 25% 20% No difference on gender 78% male in self-excluders 79% male in control group Self-exclusion more common in Austria than Germany 15% 10% 5% 0% Under 30 years Self-excluders much younger than control group years years years Over 60 years Self-excluders Control group

13 Data takeaways Low incidence of self-exclusion (0.2%- 0.3%) Most self-excluders do so quickly after opening account (69% in a month) Some players serially self-exclude (5% of self-excluders do so more than once, just inside a two year period) Possible implications Only a small proportion of those at risk of problem-gambling self-exclude Uncertain how many potential problem gamblers could be identified by SE behavioural markers Limited scope for data to identify behavioural trends in lead up SE May not reflect long-term problem gambling, (but ~65% of online accounts close in 3 months) Serial self-excluders may merit particular analysis and intervention Youth, casino-gambling, riskier bets and loss making characterised selfexclusion well (similar to other research) Predictive models and player interventions should look particularly to these features Unlike others, we found little differentiation by time spent gambling or a preference for game variety, but this may reflect data cleaning activity

14 Limitations and further research What we want Contextualised view of what predicts selfexclusion Problem gambling identifiers for a large N All gambling in context (e.g. relative to income and game structure) Clean data What we have (so far) Descriptive analysis of self-excluders vs a control group Self-exclusion identifiers with limited sample size Single platform online gambling across different game types Incomplete gambling history, imperfect control group, session set-up (web data), Predictive analytics Logistic regression Artificial neural network Bayesian analysis Random forest decisions Supported by City University London s Machine Learning Group Larger samples of data Greater use of control groups to refine models Development of innovative deep learning models evaluated against a logistic regression baseline model

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