Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Upcoming SlideShare
Stacy Shaw & Janine Robinson
Next
Download to read offline and view in fullscreen.

Share

Dr. Tilman Lesch

Download to read offline

The Quality of Quantity: Behavioural Indicators of Risky Online Gambling

  • Be the first to like this

Dr. Tilman Lesch

  1. 1. Tilman Lesch Tilman.Lesch@psych.ubc.ca The Quality of Quantity - Behavioural Indicators of Risky Online Gambling 21th February 2017
  2. 2. 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
  3. 3. Online Gambling in British Columbia Previous research into online gambling A trial-by-trial approach: Chasing Challenges & Outlook Agenda
  4. 4. Online Gambling in British Columbia Agenda
  5. 5. The PlayNow Platform: Online gambling platform from BCLC for BC and Manitoba. 14 Source: Online Gambling in British Columbia, Lesch, Limbrick-Oldfield, Clark, 2017
  6. 6. Behavioural measures differ between games types for median & engaged users. Source: Online Gambling in British Columbia, Lesch, Limbrick-Oldfield, Clark, 2017
  7. 7. The majority of players access PlayNow to place lottery bets. 3 Top 20% most engagement players
  8. 8. #ofBetsplaced Hour of day ‘after-breakfast’ effect ‘end of work’ effect‘(Not) At-Work’ plateau Daily pattern of gambling follow a working populations availability.
  9. 9. Previous research into online gambling Agenda
  10. 10. 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.
  11. 11. Behavioural clustering of account closures. Source: How do gamblers start gambling: identifying behavioural markers for high-risk internet gambling (Braverman & Shaffer, 2010 11 530 players closed 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 activity High intensity & variability High accordance for closing due to gambling related problems 33% 48% 19% Reason for account closure
  12. 12. 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) 12 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 50/50 3037 % N Control 10 17 Target 90 158 Total 100 175 % N Control 9 12 Target 91 116 Total 100 128 2 >2 Not high risk Not high risk Not high risk HIGH RISK HIGH RISK
  13. 13. A trial-by-trial approach: Chasing Agenda
  14. 14. 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
  15. 15. Over the course of a gambling session, people increase their wager about 40%. Relative time in session Wagerrelativetosessionstart Average amount bet throughout session
  16. 16. Correlation coefficient (chasing) Frequency Distribution of Correlation Coefficients Increase of wagerDecrease of wager Zero Line (No Bias) Median (Bias) On tables games, there is a bias towards increasing one’s bets.
  17. 17. On slots machines, on average there is no bias. Frequency Increase of wagerDecrease of wager Zero Line (No Bias) Median (No Bias) Distribution of Correlation Coefficients Correlation coefficient (chasing)
  18. 18. Session outcome Correlation >.6 .6 < >.2 0 < >.2 0 > < -.2 -.2 > < -.6 < -.6 There is no simple relationship between chasing and accumulated wager or session outcome.
  19. 19. Chasing (wager increase) shows limited relationship to other measures of gambling. all slots tables Number 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 correlation All Slots Mixed Tables
  20. 20. Number of chasing sessions (>.5) by user NumberofUsers Distribution of chasing session (>.5) by user Some users show larger numbers of chasing sessions.
  21. 21. Percentage of chasing sessions (>.5) by user NumberofUsersA subset of users appear to show chasing on almost every gambling session. Distribution of chasing session (>.5) in % by user
  22. 22. Learning & next steps Learnings: • 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
  23. 23. Challenges & Outlook Agenda
  24. 24. 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.
  25. 25. 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.
  26. 26. Acknowledgements CGR Professor Luke Clark Dr Eve Limbrick-Oldfield www.cgr.psych.ubc.ca @CGR_UBC BCLC Dr Kahlil Philander Bradley Bodenhamer Michaela Becker Questions? Dr Tilman Lesch Tilman.Lesch@psych.ubc.ca Thank you for your attention!
  27. 27. 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
  28. 28. http://www.egba.eu/facts-and-figures/market-reality/ Global Market Share of online Gambling Types of Online Gambling 2015 The online Gambling Market
  29. 29. 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
  30. 30. 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.
  31. 31. Behavioural markers of online gambling II: Betting Speed Translation for online gambling: Time between one bet and the next bet within the same session.
  32. 32. Mean overall Betting Speed by individual
  33. 33. Mean Betting Speed difference between winning and losing by individual Zero Line (No Bias) Slower After WinsSlower after Losses 26 >0: Slots: ~75% Tables: ~65%

The Quality of Quantity: Behavioural Indicators of Risky Online Gambling

Views

Total views

2,427

On Slideshare

0

From embeds

0

Number of embeds

272

Actions

Downloads

10

Shares

0

Comments

0

Likes

0

×