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

1.

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?

7

Caveats

• Use of harm:

• Not defined

• No agreed way to measure

• Used problem gambling instead

• Important first step towards exploring this fully

Programme

design

2.

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)

10

What data are we talking about?

Findings

3.

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

13

Loyalty card survey - profile

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

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)

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)

17

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

20

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

featurespace.co.uk 24

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

featurespace.co.uk 27

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

Tru

e D

etec

tio

n R

ate

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.

featurespace.co.uk 29

• 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%

Tru

e Po

siti

ve R

ate

False Positive Rate

Random Baseline (AUC=0.62)

featurespace.co.uk 31

RESULTS USING REGISTERED PLAY

0%

20%

40%

60%

80%

100%

0% 20% 40% 60% 80% 100%

Tru

e Po

siti

ve R

ate

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.

featurespace.co.uk 32

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

featurespace.co.uk 33

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

Nu

mb

er

of

Sess

ion

s

Pro

bab

ility

Hour of Day

Non Problem Gambler Problem Gambler Non Problem Gambler Problem Gambler

featurespace.co.uk 34

• 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

featurespace.co.uk 35

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

If you are unable to download app,

please raise your hand for a paper version