Ms. Heather Wardle, Mr. David Excell and
Research Director, NatCen
Co-Founder and CTO, Featurespace

Identifying Harm Among
Machine Players: Findings
From a Multi-Component
Research Study
Jan 2015

4
Gambling landscape in Great
Britain
c32,000 machines in high
street locations in
Great Britain

Machines research
programme – aims
and objectives

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?

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Caveats
• Use of harm:
• Not defined
• No agreed way to measure
• Used problem gambling instead
• Important first step towards exploring this fully

Programme
design

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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)

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What data are we talking about?

Findings

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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

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Loyalty card survey - profile

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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

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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)

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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)

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How well measures distinguish between
problem and non-problem gamblers
Aim to maximise
sensitivity and
specificity (i.e., where
the blue box is)

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]

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

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Why is this?
The behaviour of problem gamblers and non-problem gamblers overlap:

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

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

David Excell, Featurespace
3 February 2015

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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

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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

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• 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

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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

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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.

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• 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

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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)

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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.

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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

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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

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• 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

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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

To provide session feedback:
• Open New Horizons app
• Select Agenda tile
• Select this session
• Select Take Survey at bottom of screen
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