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Identifying Harm Among Machine Players: Findings from a Multi-Component Research Study

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Heather Wardle and David Excell: Identifying Harm Among Machine Players: Findings from a Multi-Component Research Study
Joint Session Presented at the New Horizons in Responsible Gambling Conference in Vancouver, February 2-4, 2015

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Identifying Harm Among Machine Players: Findings from a Multi-Component Research Study

  1. 1. Ms. Heather Wardle, Mr. David Excell and Research Director, NatCen Co-Founder and CTO, Featurespace
  2. 2. Identifying Harm Among Machine Players: Findings From a Multi-Component Research Study Jan 2015
  3. 3. 4 Gambling landscape in Great Britain c32,000 machines in high street locations in Great Britain
  4. 4. Machines research programme – aims and objectives
  5. 5. 6 Objectives • Can we use industry held-data to distinguish between harmful and non-harmful patterns of play? • If we can, what measures might limit harmful play without impacting on those who do not exhibit harmful behaviours?
  6. 6. 7 Caveats • Use of harm: • Not defined • No agreed way to measure • Used problem gambling instead • Important first step towards exploring this fully
  7. 7. Programme design
  8. 8. 9 Core studies Step 1: Explore the theoretical markers of harm (report 1) Step 2: Preliminary investigation of industry data to explore if markers of harm exist within data (findings in report 3) Step 3: Survey of loyalty card holders to link survey data to industry data (report 2) Step 4: Analysis of industry data to examine patterns of play among different types of loyalty card holders (report 3)
  9. 9. 10 What data are we talking about?
  10. 10. Findings
  11. 11. 12 Method Estimate c. 2 million past year machine players c.180,000 loyalty card holders 4,727 survey participants Highly engaged gamblers Gambling engagement unknown
  12. 12. 13 Loyalty card survey - profile
  13. 13. 14 Loyalty card survey – gambling participation • Very engaged in gambling (4.8 activities in past four weeks) • 26% gambled every day/almost everyday; 10% gambled every day/almost everyday on machines in bookmakers • Those in more economically constrained circumstances more likely to gamble more often • Spectrum of gambling involvement within this group • Least engaged to heavily engaged in a range of activities
  14. 14. 15 Loyalty card survey – Problem gambling • Highly engaged group of people; not representative of all machine players • Problem gambling rates among survey participants = 23% • Moderate risk = 24% • Low risk = 24% • Non problem = 29% • Problem gambling estimates among BGPS monthly gamblers = 13.3% • Problems with machine play = 14% (most of the time that they gamble on machines)
  15. 15. 16 Loyalty card survey – use of industry data Variations in some key metrics: •Stake size higher among problem gamblers (£7.43 vs £4.27) •Average number of sessions per day higher among problem gamblers (2.2 vs 1.8) •Fewer days in between visits to a bookmakers •Cash in per session higher among problem gamblers vs £41.27 vs £22.76; median for problem gamblers = £25.70)
  16. 16. 17 How well measures distinguish between problem and non-problem gamblers Aim to maximise sensitivity and specificity (i.e., where the blue box is)
  17. 17. 18 An intervention based on a threshold of average stake of £3.51 or higher How well measures distinguish between problem and non-problem gamblers [1]
  18. 18. How well measures distinguish between problem and non-problem gamblers [2] 19 An intervention based on a threshold of average stake of £10 or higher
  19. 19. 20 Why is this? The behaviour of problem gamblers and non-problem gamblers overlap:
  20. 20. 21 What does this mean? • Looking at single metrics in isolation unlikely to give satisfactory results - needs to look at a combination of behaviours • Trade offs will need to be made • Likely to depend on how onerous the intervention is • Loyalty card holders themselves (under current schemes) likely to be at elevated risk • Any new policies need to be tested and evaluated, with evaluation built into process at the outset
  21. 21. If you want further information or would like to contact the author, Heather Wardle Research Director T. 020 7549 7048 E. heather.wardle@natcen.ac.uk Visit us online, natcen.ac.uk Thank you
  22. 22. David Excell, Featurespace 3 February 2015
  23. 23. featurespace.co.uk24 The goal of our research was to determine if it is possible to distinguish between harmful and non- harmful gaming machine play. To answer this question, a combination of industry held-data and the loyalty card survey data was made available. As a proxy for harm, the Problem Gambling Severity Index (PGSI) screen has been used. The loyalty card survey included the PGSI screening questions. The research has focused on predicting PGSI scores from player data. OBJECTIVE
  24. 24. featurespace.co.uk25 The approach used to achieve this research task has been: • Combine experience from both Featurespace and RTI. • Identify a benchmark from which to compare the results of our analysis. • Start with the theoretical markers of harm to distinguish between harmful and non-harmful play • Use industry data collected from 1-Sept-2013 to 30-June-2014. – Just under 10 billion gaming machine interactions have been supplied. – Data was supplied from 5 UK operators: Betfred, Coral, Ladbrokes, Paddy Power and William Hill; and 2 machines suppliers: Inspired Gaming and Scientific Games. APPROACH
  25. 25. featurespace.co.uk26 • Definition of Harm: In this project, the PGSI screen has been used as a proxy to identify harm. • Defining the unit of analysis as a ‘Session’: The unit of continuous play used in the analysis has been a session. This does not capture a player’s entire visit to a venue, which could comprise multiple sessions. • Understanding Bet Selection and Gaming Machine Browsing: Understanding selection of bets on Roulette, or navigation between menus on a gaming machine, would provide further insight. • Defining a player and restricted card usage: Only data associated with a player’s card has been analysed. We know some players have multiple cards, and sometimes play without their card. • Multiple Gambling Product Engagement: The players surveyed engage with multiple gambling products. This analysis only looks at their gaming machine play. LIMITATIONS
  26. 26. featurespace.co.uk27 A problem gambler is identified by having a PGSI score of 8 or more. We have used this definition to define a positive and negative class for predictive modelling: • A ‘positive’ is defined as a problem gambler. • A ‘negative’ is defined as a non-problem gambler. When reviewing the results of the predictive model, we use the following terms: • True Positive: The correct identification of a problem gambler. • True Negative: The correct identification of a non-problem gambler. • False Positive: The incorrect identification of a non-problem gambler as a problem gambler. • False Negative: The incorrect identification of a problem gambler as a non-problem gambler. TERMINOLOGY
  27. 27. featurespace.co.uk28 AVERAGE PLAYER SESSION CASH-IN 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 50 100 150 200 250 300 TrueDetectionRate Average Session Cash-In Value (£) Detection Rates against Average Player Session Cash-In True Positive Rate True Negative Rate At £250, 1.3% of the problem gamblers and 99.3% of the non- problem gamblers are correctly identified.
  28. 28. featurespace.co.uk29 • Registered play is defined as a gaming session where a player card has been used. • When analysing registered play, we can look at the patterns of play over multiple sessions. • To analyse registered play: – All sessions from surveyed loyalty cards have been analysed. – A single prediction is made per loyalty card player. – The accuracy of the prediction is measured against the problem gambling score for that player. REGISTERED PLAY
  29. 29. featurespace.co.uk30 RESULTS USING REGISTERED PLAY 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% TruePositiveRate False Positive Rate Random Baseline (AUC=0.62)
  30. 30. featurespace.co.uk31 RESULTS USING REGISTERED PLAY 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% TruePositiveRate False Positive Rate Random Baseline (AUC=0.62) Featurespace Model (AUC = 0.77) Increase from 31% to 60% of problem gamblers correctly identified. Decrease from 20% to 6% of non-problem gamblers incorrectly identified.
  31. 31. featurespace.co.uk32 REGISTERED PLAY: INDICATIVE MARKERS 0 0.5 1 1.5 2 2.5 3 3.5 4 Number of Sessions Per Week Maximum Daily Total Win Maximum Session Different Games Average Player Loss (Session) Number of Losing Sessions Average Daily Player Loss Average Weekly Net Position Average Daily Player Total Stake Player Loss Average Session Total Win Average Daily Player Loss Maximum Weekly Total Winnings Number of Playing Days Mean Decrease in Model Accuracy
  32. 32. featurespace.co.uk33 TIME OF DAY 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 NumberofSessions Probability Hour of Day Non Problem Gambler Problem Gambler Non Problem Gambler Problem Gambler
  33. 33. featurespace.co.uk34 • Analysis results were based on ‘a’ model not necessarily ‘the’ model. Multiple models can have similar predictive power • Perfect predictive model for everyone (“one model fits all”) might not be attainable but a number of tailored models can provide a much better prediction in subgroups. • Understanding heterogeneity is important to understand who is most vulnerable • Challenges for policy that has to work on everyone in the same way POTENTIAL HETEROGENEITY AMONG PLAYERS Between session model 1 Between session model 2  Frequency of visits  Variability in stake levels  Hour of play  Average proportion cash out  Frequency of visits  Game variability  Total amount played in a session  Difference between deposits after win and loss
  34. 34. featurespace.co.uk35 It is possible to distinguish between harmful and non-harmful gaming machine behaviour. Furthermore, 1. It is possible to score individual players and sessions based on a harm-related risk score. These players can be added to a watch list or receive targeted interventions. 2. Gambling behaviours are complex. Identifying gambling related harm is complex. There isn’t a simple criteria that can be used to identify this behaviour. By applying predictive behavioural technology, a solution can be operationalised. SUMMARY
  35. 35. To provide session feedback: • Open New Horizons app • Select Agenda tile • Select this session • Select Take Survey at bottom of screen If you are unable to download app, please raise your hand for a paper version

Heather Wardle and David Excell: Identifying Harm Among Machine Players: Findings from a Multi-Component Research Study Joint Session Presented at the New Horizons in Responsible Gambling Conference in Vancouver, February 2-4, 2015

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