REPORT 3: PREDICTING
PROBLEM GAMBLERS:
Analysis of industry data
Gambling machines research program
28 November 2014
Authors: David Excell, Georgiy Bobashev, Daniel Gonzalez-Ordonez,
Heather Wardle, Tom Whitehead, Robert J. Morris, Paul Ruddle
www.featurespace.co.uk
Contents
Executive Summary .......................................................................................................... 5
Introduction....................................................................................................................... 6
About the research ....................................................................................................... 6
Policy context ........................................................................................................... 6
The research process ............................................................................................... 6
About this Report .......................................................................................................... 8
About the Research Organisations ............................................................................... 8
Unique Contribution ...................................................................................................... 9
Definitions & Assumptions .............................................................................................. 10
Gaming Machines in Great Britain .................................................................................. 12
Methodology ................................................................................................................... 15
Predictive Models ....................................................................................................... 15
Terminology ............................................................................................................ 15
Model Development Approach ............................................................................... 17
Target Variable ........................................................................................................... 18
Data Pre-processing ................................................................................................... 18
Model Complexity vs Model Information..................................................................... 19
Measuring Model Performance .................................................................................. 19
Data ................................................................................................................................ 22
Industry Data .............................................................................................................. 22
Data Request .......................................................................................................... 22
Data Received ........................................................................................................ 23
Data Quality ............................................................................................................ 24
Survey Data ................................................................................................................ 25
Proxy Sessions ........................................................................................................... 26
Measurement of Harm Markers ...................................................................................... 27
Between-Session Markers .......................................................................................... 27
Within-Session Markers .............................................................................................. 29
Data Analysis Results ..................................................................................................... 31
Baseline ...................................................................................................................... 31
Player Baseline ....................................................................................................... 33
Session Baseline .................................................................................................... 37
Summary................................................................................................................. 40
Player Analysis (Registered Play) ............................................................................... 41
Using Between Session Markers ............................................................................ 41
Incorporating Within-Session Markers .................................................................... 43
Session Analysis (Unregistered Play) ......................................................................... 48
Additional Experiments ............................................................................................... 51
Removing multiple loyalty cards
.................... 51
PGSI Problem Gambling Threshold ........................................................................ 52
..................................................... 54
Predicting PGSI Screening Question Responses ................................................... 55
Gambling Type Analysis ......................................................................................... 56
Factor Group Analysis ............................................................................................ 57
Debit Card Usage ................................................................................................... 58
Discussion ...................................................................................................................... 60
Can we identify harm? ................................................................................................ 61
Research Implications ................................................................................................ 62
Multiple Variables ................................................................................................... 62
Registered vs Non-Registered Play ........................................................................ 62
Mandatory ABB Limits ............................................................................................ 63
Research Limitations .................................................................................................. 63
Future Research ......................................................................................................... 63
Recommendations .......................................................................................................... 66
Conclusion ...................................................................................................................... 68
About Featurespace ....................................................................................................... 70
About RTI ........................................................................................................................ 71
References ..................................................................................................................... 72
Document Information .................................................................................................... 73
Document History ....................................................................................................... 73
Appendix A
Calculating Proxy Sessions ...................................................................... 74
Appendix B - Measurement of Harm Markers ................................................................ 77
Between Session Metrics............................................................................................ 77
1) Frequency of Play ............................................................................................... 78
2) Duration of Play .................................................................................................. 82
3) Net Expenditure .................................................................................................. 85
4) Levels of Play Engagement ................................................................................ 88
5) Number of Activities/Games Types Undertaken ................................................ 90
6) Chasing .............................................................................................................. 94
Within Session Metrics ................................................................................................ 97
1) Debit Card Payment Reloading and Switching .................................................. 97
2) Debit Card Payment Decline .............................................................................. 99
3) Variability In Staking Behaviour ........................................................................ 100
4) Use of Autoplay ................................................................................................ 103
5) Play of Multiple Machines Simultaneously ........................................................ 105
6) Stake Size ......................................................................................................... 106
7) Game Volatility .................................................................................................. 108
8) Way Game Played (e.g. number of bets per stake) ......................................... 111
9) Cash-Out .......................................................................................................... 114
Appendix C
Representativeness of Loyalty Card Data ............................................. 116
Appendix D
Candidate Predictive Modelling Approaches Explored by RTI ............. 121
Appendix E
Example of transformed between session variables. ............................. 123
Executive Summary
The Responsible Gambling Trust has been challenged by the Responsible Gambling
Strategy Board to answer the following two questions:
Is it possible to distinguish b etwe en harmful and non-harmful g aming ma chine
play?
If so, what me asures might limit harmful play without imp a cting those who do
not exhibit harmful b ehaviours?
Focusing on the first of these questions, this report confirms that it is possible to
distinguish between harmful-and non-harmful gaming machine play. The research effort
undertaken to deliver this report required the skilful processing and analysing of a large
dataset using machine learning methods. By focusing on problem gambling behaviour
survey data, this advanced technological approach has produced a step-change in the
way gambling behaviour and specifically problem gambling behaviour is
understood.
Furthermore, new insights into gambling behaviours have been identified and are
detailed in this report. For example, researchers have discovered which Problem
Gambling Severity Index questions are most predictive of problem gambling behaviour;
indicative of problem gambling; and
the need to consider a range of variables when attempting to distinguish between
problem and non-problem gamblers. This has fundamental implications for
operationalising the research and developing intervention strategies, the most critical of
which is that a focus on a single factor such as reduction of stake size will not
effectively prevent or reduce gambling harm.
From this research it is not possible to state categorically whether only gaming machine
play predominantly contributes to problem gambling status, or whether this is
accounted for by participation in multiple forms of gambling. Readers should not
assume that problem gambling status is causally and predominantly related to gaming
machine play.
Indeed, given the complexity of problem gambling and gambling behaviours in general,
the researchers have concluded that any corporate responsibility strategy must take a
balanced, rounded approach. That is, that by factoring in the environment, the
individual player, and the product being played to provide a complete view rather
than focusing on a single variable the gambling industry will be able to significantly
improve the detection rate of problem gamblers and the minimisation of gambling
related harm.
Finally, consideration has been given to the second question-- what me asures might
limit harmful play without imp a cting those who do not exhibit harmful b ehaviours?
although answering this does not contribute to a significant portion of this report. The
researchers conclude from their analysis that operators will face trade-offs in delivering
harm minimisation interventions, as some amount of non-problem-gamblers will
inevitably receive interventions which are unnecessary. Therefore all interventions must
be carefully evaluated in a live environment to measure their effectiveness.
Introduction
About the research
Policy context
This report forms part of series of research projects commissioned by the Responsible
Gambling Trust to explore the extent to which industry data generated by machines in
bookmakers can be used to identify harmful patterns of play. In recent years, there have
been increasing c
better understand how consumers play machines. It is hoped that, by analysing
transactional data, it will be possible to identify patterns of play that indicate someone is
experiencing problems or harm from their engagement in gambling. Industry and
regulators alike are keen to see if this is possible. If so, a potential new range of
responsible gambling measures, tailored towards and intervening with the individual,
could be developed.
To date, regulation of machines tends to be conducted at a high level, making
generalisations that focus on restrictions of stake, prize, speed and number of
machines. There is no regulation that is tailored towards individual players. The
Gambling Commission (the industry regulator) considers that a mix of macro (i.e.,
stakes and prizes) and micro (i.e., the individual) regulatory approaches may be
patterns of play and, if so, what types of interventions could be introduced that
intercede with gamblers experiencing problems. A further concern is to ensure that any
individual-led policies intervene with those experiencing problems, whilst allowing those
who are not experiencing problems to play without onerous intervention.
The objectives set by the Responsible Gambling Strategy Board (RGSB) for the broader
research programme were:
Can harmful and non-harmful gaming machine play be distinguished?, and
If so, what measures might limit harmful play without impacting those who do not
exhibit harmful behaviours?
The RGSB (Responsible Gambling Strategy Board) is the body responsible for setting
strategic objectives for gambling research and policy and advising the Gambling
Commission on these issues. To meet these objectives, a series of research projects
was planned by the research team, a consortium of NatCen Social Research,
Featurespace, Geofutures, and RTI International. These projects focus mainly on the
first objective, though consideration is also given to the second. Other research projects
(called contextual projects in the broader research programme) contribute to the
second objective; for example, by looking at how people understand certain types of
player messaging (see Collins et al, 2014).
The research process
To meet the objectives set by the RGSB, a number of project stages were undertaken
and three related reports have been published. The project stages are shown in Figure
1.
Figure 1 - Research project stages
In meeting the objective set by the RGSB, the first step was to consider what patterns of
play might indicate that someone was experiencing harm. This involved a theoretical
review, a rapid evidence review and consultation with key stakeholders to develop a set
of metrics (or markers) which may exist within industry data and might indicate that
someone was experiencing harm. The results of this stage are published in Wardle,
Parke and Excel (2014), called Report 1 in this series (See Wardle, Parke & Excell,
Report 1: Theoretical Markers of Harm).
The next step was to consider whether the markers of harm identified from the review
were actually evident in industry-collected data. This part of the research was
Markers
section of this report.
Early analysis suggested that some of the markers of harm identified in the review could
be measured using industry data and therefore further exploration of the data was
warranted. A critical question for this research centred on examining the potential
patterns of harm identified in theoretical markers of harm from existing academic
literature and expert opinion. It was necessary to determine if these theoretical markers
of harm were actually patterns of play exhibited by those experiencing harm from
gambling. A crucial aspect of this is determining the extent to which potential patterns
of harm differentiate between those experiencing harm and those who do not. To
explore this, more detail is needed about the player and the extent to which they are
experiencing gambling-related problems. This information can only be obtained by
communicating directly with players.
The study by NatCen, which is documented in Report 2, fills that gap. It reports survey
findings from individuals who have loyalty cards for Ladbrokes, William Hill, or Paddy
Power. Using loyalty card holders as a sampling frame for a survey meant that we could
link their survey responses with data collected and recorded for their loyalty card. The
loyalty cards for bookmakers operate in much the same way as other loyalty cards (like
Tesco Clubcards or Nectar cards) where every transaction (where the card is used) is
recorded for an individual. This means it is possible to track the frequency and duration
of time spent on machines, so long as individuals used their loyalty card when playing.
Using this data has considerable benefits over traditional survey approaches, as it is
widely accepted that estimates of gambling expenditure obtained through surveys are
inaccurate.
The study document in this report has one primary aim: to discover if, by combing the
survey results obtain by NatCen from loyalty card holders with the data held by the
industry, it is possible to distinguish between harmful and non-harmful play through the
identification of specific patterns in a player s behaviour. It is important to note that a
majority of surveyed loyalty card holders participated in multiple forms of gambling.
Therefore it is not possible to state categorically whether only gaming machine play
predominantly contributed to their problem gambling status, or whether that status can
be accounted for by participation in multiple forms.
Within that goal, the aims of this report are to:
provide a description of the data and context to the gambling environment from
which the data was obtained;
provide an overview of the methodology used to analyse the data;
report on the initial findings that lead to the conclusion that industry data was
suitable for further analysis to understand harmful play;
report on the analytics performed and provide a discussion on how the results
should be interpreted, and suggest recommendations on how the results could be
used and further explored.
Report 2 and Report 3 (this report) should be viewed together and it is these two reports
in combination which aim to meet the research objectives set out by the RGSB.
About this Report
The objective of this report is to describe the findings of the research undertaken and
ensure that the results can be understood by a wide range of interested stakeholders. A
significant proportion of the research effort undertaken to deliver this report has been
the sophisticated manipulation and analysis of a large volume of data using state-of-theart processing systems and statistical algorithms. A summary of the technical
review of the analytical techniques and processes described has been purposely
excluded as it would not enhance the understanding of the research for a majority of the
readers.
About the Research Organisations
The research documented in this report has principally been carried out by an
international team of researchers from Featurespace and RTI International, in tandem
with invaluable input and guidance from NatCen. Featurespace took on the role of
working with the industry to obtain and process their data, and then worked in
conjunction with RTI to analyse the patterns within the data. NatCen undertook the
loyalty card surveys and provided analysis of the responses to help guide the data
analysis.
With the time-limited nature of this research program, having two organisations working
in parallel with varying backgrounds in the application of data analytics enabled
different approaches to be explored independently. Results were then compared and
the differences and similarities understood. This also provided additional oversight in
terms of quality assurance to ensure valid results were produced.
Unique Contribution
A unique contribution to the understanding of problem gambling is provided in this
report. These contributions are summarised below:
It is the first time that the five largest operators in Great Britain have made their
data available for analysis by independent researchers.
It is the first time in the world where land-based industry data from multiple
operators has been analysed alongside a problem gambling screening score
obtained by interviewing individuals. This has provided an incredibility rich data
set which has the potential to unlock a whole range of new research initiatives. A
number of studies have been completed previous, but the size of these samples
has been significantly smaller.
The research is based on a significant sample size (n = 3,988) compared to
existing studies which have been limited to, at most, a few hundred individuals.
Definitions & Assumptions
These terms have the following meaning within this document:
Term
Definition
ABB
Association of British Bookmakers (http://www.abb.uk.com/)
AUC
Area Under the Curve. This is a measure of the accuracy of the
predictive model. A model which makes random guess would have
an AUC score of 0.5. A model which produces a 100% accuracy
would produce an AUC score of 1.0. A full description is provided
in the Methodology section.
When calculating the improvement of a model, the minimum value
of 0.5 for an AUC score is taken into consideration. Therefore if
Model A has an AUC score of 0.55 and Model B has an AUC score
of 0.60. We would calculate the improvement as ((0.60-0.50)-(0.550.50))/(0.55-0.50) = 100%
1
B2
B2 is a category of game available on the Gaming Machines
studied in this research project. The key characteristics of a B2
games are that the maximum stake is £100, the play cycle must last
at least 20 seconds and the maximum prize is £500.
B3
B3 is a category of game available on the Gaming Machines
studied in this research project. The key characteristics of a B3
games are that the maximum stake is £2, the play cycle must last at
least 2.5 seconds and the maximum prize is £500.
Baseline
Within this report we have developed baseline models which are
used as a reference point for comparing the improvements
generated by the models created in this report.
Bet
This relates to a single game on the Gaming Machine where the
player has staked a particular amount.
DCMS
Department of Culture, Media and Sport
False Negative
A false negative is a problem gambler who has been incorrectly
identified as a non-problem gambler.
False Positive
A false positive is a non-problem gambler who has been incorrectly
identified as a problem gambler.
Gaming Machine
A touch screen electronic gaming machine featuring both B2 and
B3 category games as defined by DCMS regulation. 1
High volatility game
A game in which the prizes are in-frequent and higher amounts
LBO
Licenced Betting Office
These Gaming Machines are colloquially and unofficially known as FOBTs, or Fixed
Odds Betting Terminals. A more detailed description of the type of games available on
Gaming Machines, and the differentiation between B2 and B3 games, will be provided
in the next report.
Low volatility game
A game in which the prizes are frequent and lower amounts than
Month(ly)
A calendar month, for example, 1-Sept to 30-Sept 2013
PGSI
Problem Gambling Severity Index
Playing Day
Day of the week in which a registered player has had at least one
session on a machine.
RGSB
Responsible Gambling Strategy Board (http://www.rgsb.org.uk/)
ROC
Receiver Operating Characteristic This is a commonly used
graphical method to show the performance of a predictive model. A
full description is provided in the Methodology section.
Sensitivity
that is the proportion of correctly identified problem gamblers.
Session
A continuous period of machine activity from a player.
Specificity
Equ
that is, the proportion of correctly identified non-problem gamblers.
Stake
Amount of money the customer is risking on a bet.
Time Periods
All time periods are in this report are either shown using the unit of
days, or in the format D.hh:mm:ss where D is the total number of
whole days and hh, mm, ss represent the number of hours, minutes
and seconds respectively.
True Negative
A true negative is a non-problem gambler who has been correctly
identified.
True Positive
A true positive is a problem gambler who has been correctly
identified.
Week(ly)
The period from Monday to Sunday.
Gaming Machines in Great Britain
The focus of the Machines Research Programme commissioned by the Responsible
Gambling Trust was to understand if harmful and non-harmful play could be
distinguished on the gaming machines operated by Licenced Betting Offices in Great
Britain (England, Scotland and Wales).
Great Britain has one of the most gambling diverse markets in the world, with a wide
range of gambling channels, including Retail (e.g. Casinos, Licenced Betting Offices,
Bingo Halls, and Arcades), Internet, Mobile, and Telephone betting. There is also a
wide range of products offered, including but not limited to, Sports Betting, Casino
Games, Poker, Bingo, Lottery and Scratch Cards.
Licenced Betting Offices (LBOs) are retail premises that offer facilities to place a bet;
that is, making or accepting a bet on the outcome of a race, competition, or other event.
As of October 2014, there are 9,508 registered LBO premises in Great Britain, a
majority of which are located on the high-street and residential areas. The five largest
operators of LBOs are Betfred, Gala Coral, Ladbrokes, Paddy Power and William Hill.
Within their retail premises, these operators generate
-TheLicence Betting Office is restricted to four physical Gaming Machines. Each of the five
operators also provides remote gambling services. In many town centres, it is not
uncommon to see a combination of LBOs located within a short distance from each
other. For a detailed analysis of the spatial distribution of the LBOs, please refer to the
contextual report generated by Geofutures published as part of this research
programme.
There are two primary gaming machine suppliers in the Great Brian LBO market:
Scientific Games and Inspired Gaming. The Gaming Machines are modern gaming
terminals offering graphically rich content across a number of different game types.
Images of machines from the two primary suppliers are shown in Figure 2.
Figure 2 - Gaming Machine terminals that generated the data studied in this
research. Inspired gaming terminals are shown on the left and a Scientific Games
terminal shown on the right.
The games offered by a Gaming Machine can fall into the following categories defined
by regulation: B2, B3, B4, C and D. A significant proportion of the stakes placed on the
gaming machines are from games that fall into the B2 or B3 categories. Full technical
standards for these categories can be found on the Gambling Commissions website 2,
but a summary of the salient points is provide below:
B2 Category Games. These games have a maximum stake of £100 and a
maximum prize of £500. The game cycle must last at least 20 seconds. The most
popular style of B2 Category game is Roulette.
B3 Category Games. These games have a maximum stake of £2 and a maximum
prize of £500. The game cycle must last at least 2.5 seconds. The typical B3 style
The analysis completed as part of this research did not explicitly look at the difference
between B2 and B3 playing characteristics. However the inputs into the predictive
models included metrics about the proportion of bets on the different content
categories.
One of the key contributors to the successful completion of this research programme
was access to player card data. There is no regulatory requirement for Gaming
Machine operators to monitor which players are using their gaming machine products.
Therefore, at the time when this research was commissioned, the player cards had
been implemented as loyalty card schemes to facilitate player insight and marketing.
This has meant that the operators have independently implemented their own schemes
with different degrees of data capture and data quality. However, there are some
commonalities when the same Gaming Machine supplier has been used between
been run by Ladbrokes since 2008. Both Paddy Power and William Hill introduced their
loyalty schemes in 2013, and Gala Coral introduced their Coral Connect scheme early
in 2014. An example of the player cards provided by the industry is shown below in
Figure 3. Some of the loyalty card programmes bridge the gap between retail and
remote gambling, enabling the players to transfer funds between the channels. For the
time period studied in this research, loyalty card data was only available from
Ladbrokes, Paddy Power and William Hill.
2
http://www.gamblingcommission.gov.uk/shared_content_areas/gaming_machines_technical_stan.
aspx
Figure 3 - Example Loyalty Cards from Ladbrokes, William Hill, Gala Coral and
Paddy Power.
Methodology
In this section, a brief overview is provided on the methodology and techniques used to
analyse the data to answer the research question. The aim of this section is to provide
enough background knowledge to the reader to aid in the interpretation of the results
presented later in the report. We also explore some of the complexities of the data
modelling approach in the context of distinguishing between harmful and non-harmful
gaming machine play. The approach used to examine the performance of the predictive
models is provided and explains how different trade-offs can be made when applying
predictive models in an operational environment.
The data analysed in this process had some unique challenges that need to be
considered when designing the methodology. The key challenges were:
Data volume: Just under 10 billion data records were provided for this
analysis. Analysing this volume of data required consideration of how to store,
access, and process it efficiently and accurately.
Data Skewedness: When investigating the data there is often a significant
difference between the mean and median values of the data. This shows that
there is a small number of extreme values which can alter our perception of
what the majority of customers are doing.
Representativeness of Registered Players: When comparing sessions
generated by registered and non-registered players, we observed that
registered sessions provided an oversessions, compared to the entire data set. Detail of this analysis is provided in
Appendix C.
Predictive Models
In its simplest form, a predictive model takes a range of characteristics as inputs and
looks at how well a prediction can be made from them. A predictive model can be as
be male. This predictive model is making an assumption that all males have hair shorter
than 5cm. In the real-world, inaccurate predictions would be produced by only applying
this rule. Increasing the number of input characteristics and the complexity of
interpreting the relationships within the data allow accuracy to be improved.
Terminology
To measure the quality of a predictive model, the target that we are trying to predict
needs to be defined. For this research, we have defined our target as predicting
problem gamblers. In predictive modelling t
that we have correctly identified someone as a problem gambler and a negative
-problem gambler. When
we have four metrics to quantify the quality of the output:
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 nonproblem gambler.
In this report, these results are presented as a rate. This enables us to understand the
proportion of problem gamblers/non-problem gamblers that would be identified (either
correctly or incorrectly). The rates are defined as:
True Positive Rate (TPR)
True Negative Rate (TNR)
False Positive Rate (FPR)
False Negative Rate (FNR)
The objective of the predictive model is to maximise the true positive and true negative
rates while minimizing the false positive and false negative rates. It is useful to note that
there is a relationship between the rates, such that:
True Positive Rate = 100% False Negative Rate
True Negative Rate = 100% False Positive Rate
As these variables are related, if we achieve a high true positive rate we also have a low
false negative rate.
In Report 2 of this research programme, and also in other problem gambling literature,
definitions using the terminology introduced above:
Sensitivity = True Positive Rate
Specificity = True Negative Rate
Model Development Approach
To process of turning raw data into a predictive model takes a number of defined steps.
This process is described below:
1.
2.
3.
4.
5.
6.
7.
8.
Data Validation and Preparation In this step, the received data was
validated to ensure it is consistent with expectations (e.g. the format, number
of records, etc.) and converted into a common format so that the data from
each supplier could be analysed as a whole, rather than independently. In this
step the proxy session algorithm was also used to assign individual machine
events to a player session.
Variable Calculation In this step, a number of session and player level
variables were calculated. The majority of the variables calculated were based
on the theoretical makers of harm identified in Report 1. Analysis of each of
the variables is provided in Appendix B of this report.
Data Pre-processing In this step the variables were transformed using a
number of approaches to help the predictive models distinguish between
different types of behaviour. More detail of these transformation approaches
are provided below.
Dataset Selection When verifying or testing a predictive model it is
important that the data used to build the model is not included. The entire data
is therefore divided into three separate datasets for training, verification, and
testing. Depending on the analysis, players or sessions were randomly
allocated to the different datasets. In this research project, the training dataset
contained 50% of the data, and the remaining data was allocated to both the
verification and test datasets, which received 25% each.
Model Training In this step, the predictive modelling algorithms analyse the
available data and determine which player patterns are most likely to relate to
the target variable: in this case, our problem gambling label. In this process a
number of models were generated, relating to different algorithms, parameters
for those algorithms, input data, and transformations to the data. A range of
predictive modelling algorithms used by RTI is described in Appendix D.
Model Validation Within the data validation phase, we examined all of the
predictive models which had been trained to see which had the best
performance on the validation data set.
Model Testing To confirm the accuracy of the model and to help
understand its capabilities, it was testing on the final test dataset.
Cross validation Finally, to ensure that the original random allocation of data
repeated the training and testing phases using the model specification that
delivered the best accuracy. The details of the validation process was a 10fold cross validation. Cross validation is the process whereby the data is
of positive and negative examples. K-1 buckets are then used to train the
model and then the accuracy of the model is tested on the remaining bucket.
testing.
The process described above has been designed to avoid one of the key issues of
number of input variables, such as were present in this research project.
The concept of over-fitting when building a predictive model relates to the performance
of the model when it is applied to new data that was not used in the training process.
Ideally, when a model is built it will learn patterns which define a generalised
relationship between the inputs (gambling data) and the target output (problem
gambling). When generalised relationships are identified the model should produce
consistent performance across the test data and new data as it is processed. In
exist only within the training data. The model is then expecting to see these same
performance will be impacted.
Target Variable
In this report we examine two different outcomes:
Whether someone is a problem gambler
Whether a session of play comes from a problem gambler.
To define a problem gambler, we are utilising the Problem Gambling Screening Index
obtained from the survey results in the loyalty card data. Participants in the survey who
in the survey and how the score is derived is provided in the following section.
Determining whether someone is a problem gambler is important for understanding the
utility of using loyalty cards to collect data across multiple gambling sessions. To
determine whether someone is a problem gambler we have analysed all of the data
associ
measure the accuracy of this prediction, we compare the predicted label to the actual
label for that player.
Determining whether a session comes from a problem gambler is important for
understanding how well problem gambling can be identified when a loyalty card is not
used. A majority of the data currently being generated by the gaming machines is not
linked to a loyalty card, highlighting the need for this analysis. To determine whether a
session of play comes from a problem gambler we analyse the activity associated with
that session and generate a single prediction. To measure the accuracy of this
prediction we compare the predicted label to the problem gambling label for the player
who generated the session. The limitation with this approach is that we are making an
assumption that every time a problem gambler plays on the machines they are
exhibiting problem gambling behaviours.
Data Pre-processing
Before producing the predictive models, a number of pre-processing tasks were tested
to see if they could improve the performance of the model. The pre-processing tasks
involved transforming the input values so that they could be compared in different ways.
This is often required, as it is in our case, when there is a non-linear relationship
between the input data and what is being predicted. As an example, a £2 increase in
stake might have a different predictive ability if the increase is from £2 to £4, compared
to an increase from £20 to £22. This effect is further confounded in our case where the
data is highly skewed with a small number of significant outliers.
A range of transformations have been tested in the model building process. Each of
these is listed below with a brief explanation:
Unmodified No modification is made to the underlying variable and it is fed
directly into the model.
Absolute Value When a variable takes on negative and positive values, the
absolute value can be taken to reduce the range from zero to a positive
can be taken to change the meaning of the value. For instance, if a player has
three sessions with net expenditure of -£200, -£5 and £250, taking the
absolute value transforms this data to £200, £5 and £250. For the original data
set we could interpret this as two losing sessions and one winning session.
With the transformation the interpretation changes to one session with minimal
change in financial outcome and two sessions with a large change in financial
outcome.
Winsorized This is a process where extreme values or outliers are removed
from the output to prevent those values from dominating the patterns learnt by
the model.
Log For variables where there is a large number of samples that take on
small values, and there is a small number of samples which take on high
values, taking the log of these values can provide a more informative scale for
a predictive algorithm. As an example, if we had the raw values 10, 100, 1000
and 3 as inputs into the predictive algorithm.
Zero Indicator For variables that have dominating proportion of zeroes (e.g.
number of games at the highest stake, amount won in a session, etc.), the
hat has a
category for zeroes, and the rest of the values are aggregated based on the
quartiles of the remaining data.
Grouped This is the process where a variable which can take on many
values (such as the amount won on a game) is reduced down into a smaller
number of groups (such as a small, medium and large win). In statistics, this
Model Complexity vs Model Information
When building a predictive model, there is often a trade-off to be made between the
underlying complexity of the patterns and the degree of explanation that can be
extracted from the model. In this research, we have used a hierarchy of models based
on different predictive algorithms that will lead us to a more accurate separation
between problem and non-problem gamblers.
Measuring Model Performance
To understand the performance of the predictive models we have used the Receiver
Operator Characteristic (ROC) Curve. The ROC curve was first used in World War II for
the analysis of radar signals and today is commonly used to evaluate the performance
of machine learning techniques. Figure 4 provides an example ROC curve with the
performance of three models included. The false positive rate is plotted on the
horizontal axis and the true positive rate is plotted on the vertical axis. This enables us
to compare the performance of correctly identifying problem gamblers (the true positive
rate) against the impact of incorrectly identifying non-problem gamblers as problem
gamblers (the false positive rate). The points on each of the curves correspond to the
performance of the model at different thresholds. A threshold is used to make the
discrimination between a problem and non-problem gambler (e.g. players scoring
higher than the threshold are labelled as problem gamblers). The higher thresholds are
on the left of the curve, and decrease as you follow each of the curves to the right. In an
ideal world, we would produce a model as close to possible to point A in the figure.
Generating models that perform near this point is very rare. This is a model operating
position which has a true positive rate of 100% and a false positive rate of 0%, perfectly
distinguishing between problem and non-problem gamblers. At points B and C, we are
operating at the two extremes of either predicting everyone to be a non-problem
gambler (point B) to predicting everyone to be a problem gambler (point C).
Model 3 has been included in this diagram to show what would be achieved by just
measuring the accuracy of a models whose output was a random guess. If a model has
a similar performance to Model 3, it shows that the input variables could not be used by
the predictive modelling algorithm to make an informed prediction. Ideally, we want our
models to deliver a performance as far away from this as possible. Models 1 and 2 are
two models that are able to make informed predictions. In this case Model 1 is
outperforming Model 2. This can be seen in the figure as the curve corresponding to
Model 1 is higher than the curve for Model 2.
By having Model 1 and 2 on the same ROC curve it is possible to see the impact of the
improved accuracy between the models. If previously we had been using Model 2 to
identify problem gamblers, we could have decided to operate at point D. By looking at
the vertical and horizontal axes we can see that this point generated a true positive rate
of 60% and a false positive rate of 30%. That is 60% of the problem gamblers where
correctly identified and 30% of our non-problem gamblers where incorrectly identified
as problem gamblers.
If we now want to move to our more accurate Model 1, we have two choices. Firstly, we
could decide to move to point E. This would enable us to identify the same proportion of
problem gamblers (60%) but instead we would reduce the incorrect classification of
non-problem gamblers from 30% to 10%. Alternatively, we could decide to move to
point F. This would enable us to keep the same false positive rate (30%), but we would
be correctly identifying a higher proportion of problem gamblers (improving from 60%
to 90%).
Alternatively, it would be possible to trigger one intervention for customers that have a
score at or above point E (where we are more confidently identifying problem gamblers)
and then a second, potentially softer intervention for customers who fall between the
boundaries of points E and F.
Finally, to compare the different models it is useful to have a single figure which
describes their performance. In this report we have used the Area Under the Curve
(AUC) metric. This value ranges from 1 (a model which produces no errors) to 0.5, the
performance of Model 3. In our example, Model 1 has an AUC value of 0.85 Model 2
has an AUC value if 0.70.
100%
A
F
C
True Positive Rate
80%
E
60%
D
40%
20%
B
0%
0%
20%
40%
60%
80%
False Positive Rate
Model 1
Figure 4
Model 2
Model 3 (Random)
Example Receiver Operator Characteristic Curve
100%
Data
In this section, a description of the data used to complete this research is provided.
This covers both the data obtained from the industry along with the loyalty card survey
One key conclusion about the data used as part of this project is that although it is
significant in volume (over 9.5 billion individual gaming machine events 3), the breadth of
the variables in each of these events is limited. For each event we only know the time
when the event took place, the location where it took place, the type of event (cash in,
cash out, bet, win), the game being played, and the value of the transaction (e.g. the
amount staked or the amount won).
Industry Data
The data used to generate this report was supplied by the five major Licensed Betting
Offices in the UK (Betfred, Coral, Ladbrokes, Paddy Power and William Hill), and their
gaming machines suppliers (Inspired Gaming and Scientific Games). The relationship
between the LBOs and the gaming machine suppliers is shown in the table below:
Gaming Machine Supplier
Inspired Gaming
Scientific Games
Licensed Betting Offices
1.
2.
3.
1.
2.
Betfred
Paddy Power
William Hill
Coral
Ladbrokes
Data Request
Timeframe
The time period covered by the data used in the research is 10 months from 1
September 2013 to 30 June 2014. For the initial evaluation on the suitability of the
available (1 September 2013 to 30 November 2013).
It is important to note that the initial sample of loyalty card holders to survey was drawn
from the first 3 months of data used in the evaluation phase.
Data Attributes
The primary set of data requested from the industry (where available) was:
3
1.
2.
Players The attributes recorded for each registered player.
Shops The attributes for each store which contains a Gaming Machine,
3.
Machine Events The transactional data captured on each Gaming
Machine, for example: records relating to players putting money in and
taking money out of the machine, placing bets, and associated winnings.
A machine event refers to action which is recorded on the machine, such as a note
being inserted, a bet placed or money being won.
4.
Games The games available on each Gaming Machine, in particular the
legal category of the game, the type of game, and the theoretical RTP
(Return to Player) at different stake levels.
The secondary data request was also comprised of:
1.
Payment data Transactions relating to debit card transactions (both
failed and successful) which are used to fund activity on the machines.
2. Self-Exclusion Registered players who have self-excluded.
3. Player Limits Registered players who have specified limits on their play.
4. Responsible Gambling Any information relating to players
receiving/viewing literature related to problem gambling.
5. Online Transfer
account.
6. Sports Trading Data The number of bets, turnover and winnings per
day per shop.
7. Customer Contact Data associated with contact with registered players,
e.g. marketing material, complaints, etc.
8. Promotions Data relating to player bonuses, free bets, etc.
9. Player Surveys Info
players.
10. Market reviews Reports and literature relating to the impact of
promotions.
Data Received
Featurespace received varying levels of granularity in the data from each of the
operators based on the underlying sophistication of their Gaming Machine offering.
Rather than detail the specifics of the data received from individual operators, general
parameters are listed below. Overall, we received data relating to:
333,091 uniquely identifiable customers4
8,289 unique shops
32,650 unique Gaming Machines
9,550,448,367 analysed machine events, including 6,768,053,704 bets
661 different games5
Specifically for the surveyed customers we had
3,988 loyalty card holders
4,374 unique shops that these players gambled at
524,277 gaming machine sessions
35,668,298 bets placed
4
This does not mean that the study corresponds to 333,091 individuals. An individual
person may have relationships with more than one operator or may have used different
loyalty cards during the three month period.
5 This is the number of unique games across the suppliers, but they may have very
similar games across both (e.g. Roulette will be included twice, once for its Inspired
Gaming implementation and once for its Scientific Games implementation).
Some important notes about the received data:
During the period for which we obtained data, Betfred did not operate a
loyalty scheme. Therefore no Betfred customers were included in the
loyalty card survey.
During the initial 3 months for which data was requested, Coral did not
operate a loyalty scheme but had an internal system for recording repeat
activity of customers. These labels have been included when calculating
the between session metrics for the evaluation of the industry data. No
Coral customers were included in the loyalty card survey.
Paddy Power and William Hill introduced their loyalty card schemes in
2014 and therefore some of the behaviours associated with the early
period of received data may not be indicative of long term customer
usage. The loyalty scheme used by both Paddy Power and William Hill
collects minimal information about the player (e.g. only their mobile phone
and/
on registration,
so are not guaranteed to be accurate.
After receiving the data, Featurespace transformed the data from the gaming machine
suppliers and the operators into a common format so that data could be used to
calculate metrics across the entire data set.
Data Quality
In general, no significant data quality issues have been identified that would invalidate
the results produced as part of this research. Minor issues were experienced, and a
brief description is provided below. In most of the cases, we have been able to either
work around the issue or have excluded the problematic data from the analysis. In
future research, resolving these data issues may enable the performance of the
predictive models to be improved.
Debit Cards It was known at the start of the project that there was not a
precise method for matching transactions on the gaming machines to debit
card transactions recorded by the
electronic point of sale systems.
A precise match is not possible as these two systems work independently.
After a player has made a deposit, either the full or partial amount is manually
transferred onto the machine by a member of staff. To identify debit card
occurred in the same shop, at approximately the same time and for
approximately the same value. This requirement to search introduces a degree
of error within the data.
Online Transfers We needed to further clarify with the operators how the
transfers between funds used on the gaming machines to online accounts can
be accurately matched. Within the timeexplored.
Operator and Supplier Data Matching We have experienced issues with
matching identifiers for players and shops in the data supplied by the
machines suppliers and the operators. This meant for a small subset of the
data a full set of attributes could not be obtained.
Timestamps We were made aware by one of the suppliers that they
experience errors with the timestamps associated with the recorded machine
of the research presented in this report.
It is also worth noting that some attributes are likely to be over-represented by loyalty
card
contextual research completed as part of the overall research program, we know that
approximately one in three individuals has multiple loyalty cards. However, and more
importantl
when they play on the machines so we are only capturing a portion of their gaming
machine activity.
Survey Data
To complete the data set required to undertake the research, the Loyalty Card Survey
data generated by NatCen was merged with the industry data. Full details of the survey
process and a complete analysis of the survey data is provided in Report 2.
The most important component of the loyalty card survey data that is used in this report
is the Problem Gambling Screening Index (PGSI). The Problem Gambling Screening
Index is generated by asking the following nine questions to the loyalty card holder
about their gambling activity over the last 12 months:
1.
2.
3.
4.
5.
6.
7.
8.
9.
How often have you bet more than you could really afford to lose?
How often have you needed to gamble with larger amounts of money to get the
same feeling of excitement?
How often have you gone back another day to try to win back the money you
lost?
How often have you borrowed money or sold anything to get money to
gamble?
How often have you felt that you might have a problem with gambling?
How often have people criticized your betting or told you that you had a
gambling problem, regardless of whether or not you thought it was true?
How often have you felt guilty about the way you gamble or what happens
when you gamble?
How often has your gambling caused you any health problems, including
stress or anxiety?
How often has your gambling caused any financial problems for you or your
household?
For each question the participant can select from the answers in the table below. The
table also shows the score associate with each response.
Response
Score
Almost always
Most of the time
Sometimes
Never
Table 1 - PGSI Question Responses and
3
2
1
0
Scores
To generate the PGSI score, the individual scores from the responses to the questions
are summed together. The possible range of PGSI scores therefore ranges from 0 to 27.
The PGSI specification has provided thresholds with which to label different severities of
gambling related risk as listed in Table 2. This table also shows the number of survey
participants that fall into each category.
Gambler Types
PGSI Score
Survey
Participants
Problem Gambler
8 or above
951
Moderate Risk
Between 3-7
1025
Low Risk
Between 1-2
923
Non-Problem Gambler
0
1089
Table 2 - Gambler Types as defined by the PGSI Score and the number of survey
participants that fall into each category.
Proxy Sessions
The gaming machines operated by the LBOs in Great Britain to do not require a player
to insert their loyalty card before they begin playing. It is possible for the player to insert
or withdraw their card at any point of their session. Therefore if a session is only defined
during the period when the card was actually inserted, it is possible for some player
activity to be excluded from our analysis. To overcome this problem, an algorithm was
developed to predict when sessions started and ended on the machine. A session
generated by this process is referred to as a proxy session. A loyalty card player is then
associated with each proxy session where their card was inserted for at least one event
within the calculated proxy session.
Using a proxy session to identify player sessions has limitations but on the whole we
research. While full details of how the proxy sessions are determined is provided in
Appendix A, the key variables used in this process are the balance of the machine, the
time since the last event on the machine and the type of event taking place. For
example, a cash-in event is more likely to indicate the start of a session than is a stake
event.
Measurement of Harm Markers
The first step in this research programme generated a list of the many theoretical
markers of harm or patterns of play that might indicate that someone had problems with
their gambling. The evidence review suggested that these were all plausible but it
wa
patterns look like. The second step of the research program was to complete a
preliminary analysis of industry data to see if it was possible to calculate these metrics
from industry data and if these metrics showed sufficient statistical variance to suggest
that by applying predictive modelling we would be able to distinguish between harmful
and non-harmful play.
The preliminary data analysis contained 2.6 billion data records of gaming data from the
period 1 September 2013 to 30 November 2013. The markers in this report are broken
down into two categories
term behaviour across multiple gaming sessions; and 2) within session markers which
From this preliminary analysis we did find that it was possible to calculate a large
proportion of the metrics and that they demonstrated sufficient statistical variance to
give us confidence that predictive modelling would be successful.
A summary of the characteristics found for each of the markers is provided below. A full
description of this analysis is provided in Appendix B. Within the scope of the
preliminary session we also examined the representativeness of registered sessions
(where a loyalty card is present) compared to unregistered sessions (where a loyalty
card is not present). We found that registered sessions oversessions, in that they are often longer and involve more money and bets. A full
description of this analysis is provided in Appendix C.
Between-Session Markers
These metrics included frequency and duration of play, net expenditure, levels of play
engagements, number of activities/games types undertaken, and chasing. Based on
these metrics, we can construct a view of the behaviour of a typical player compared to
that of a 90th percentile player (those who experience the most losses). This provides a
snap-shot view of both average and extreme play. Use of player loyalty cards, some of
which are employed only once, impacts the calculation of these figures downwards so
that values may seem low.
Marker
Results for a typical player in a 3-month period
Frequency of Play
5 sessions
Duration of Play
Average session length 0:12:53
Net Expenditure
Loss of £24.33
Number of Activities/Games Types
Undertaken
Usually 1 game per session, with 70% of bets placed
on a favourite game and 87% of bets placed on a B2
game.
Chasing
Average of 3 losing sessions, but correlation between
a winning/losing session and behaviours in subsequent
sessions not yet established.
Table 3 - Values for the Between Session Markers at the median.
Marker
Results for variables at the 90 th percentile in a 3month period
Frequency of Play
40 sessions
Duration of Play
Average session length 1:08:06
Net Expenditure
Loss of £776.09
Number of Activities/Games Types
Undertaken
Usually 3 unique games per session and 17 unique
games over the entire period
Chasing
Average of 26 losing sessions, but correlation between
a winning/losing session and behaviours in
subsequent sessions not yet established
Table 4 - Values for the Between Session Markers at the 90th percentile.
Examination of these markers has revealed:
A majority of the players exhibit minimal values (such as low values of stakes,
session length and games played). For example, a typical player plays 5 times a
month, while only 1 in 10 will play 40 times a month. The median player loss is
£24.33 over a three month period, whereas 1 in 10 will lose £776.09.
For all metrics, there are minimal circumstances where metrics have extreme
values. For some of these cases it is difficult to determine exact usage: the card
may be used by one person, or shared, or mistakenly left in the machine.
For most of the variables we generally see an exponential distribution as the
values increase. This means that small values are very common and large values
placed a long way from the average are very rare. In other words, values around
15 are five times more common than values around 25, and values around 25 are
5 times more common than those around 35.
Within-Session Markers
These metrics included debit card reloading and switching, variability in staking
behaviour, stake size, game volatility, and the way a given game is played. Again, we
can construct views of a typical and a 90th percentile player based on these metrics.
Marker
Typical results for individual sessions over a 3month period
Debit Card Reloading and
Switching
Approximately 2% of sessions involve a debit card.
Variability in Staking Behaviour
The median total value from 8 bets was £29
Stake Size
Average stake £3.53, with a median minimum of £1.80
and median maximum of £5.40. 16 bets typically
staked at lower amount.
Game Volatility
Low volatility games preferred (67% of sessions)
Way Game is Played
0:03:52 session length; player cashes in £12.30 and
loses £3.50 and is likely to play a single game only.
Table 5 - Values for the Within Session Markers at the median.
Marker
Typical results for individual sessions at the 90 th
percentile over a 3-month period
Debit Card Reloading and
Switching
Approximately 2% of sessions involve a debit card.
Variability in Staking Behaviour
The median total value from 86 bets was £400
Stake Size
Average stake £21.18, with a minimum of £10.00 and
maximum of £37.60.
Way Game is Played
0:23:36 session length; player cashes in £100.00 and
loses £60.00 and is likely to play a single game only.
Table 6 - Values for the Within Session Markers at the 90 th percentile.
For the Within Session Markers, player behaviour for both typical and 90th percentile
players has been constructed for those markers where results were conclusive. In
summary
Again, the majority of players exhibit minimum values for most variables. A typical
player places 8 bets over a session totalling £29 in stakes, while only 1 in 10
players will place 86 bets totalling £400 over a session. Examination of stake size
strengthens this observation: a typical stake for the majority of players is £3.53, or
at most £5.40, while 1 in 10 players will stake £21.18, or a maximum of £37.60.
By examining over 80 variables and the range of values observed for each,
Featurespace was able to construct a very detailed picture of the variety of activity
possible within the machines. This means that, for a large majority of the variables
measured for each of the markers, there is sufficient variation which enables the
behaviour of sessions to be differentiated and characterised.
In summary, we successfully demonstrated in this preliminary analysis that:
It is possible to use industry data to measure markers of theoretical harm
The distribution of values derived from these markers shows potential for being
able to differentiate between harmful and non-harmful gaming machine play.
Data Analysis Results
In this section results are presented in three main parts:
1.
Baseline We establish baseline models so we can use this to compare our
new predictive models against, measuring how well they perform in predicting
problem gamblers or problem sessions.
2.
Player Analysis We present predictive models developed to identify whether
someone is experiencing gambling problems or not
3.
Session Analysis We present predictive models showing whether a session
is likely to be from a problem gambler or not.
4.
Additional experiments We present the results of further investigations into
the data to understand what elements of the problem gambling are more
predictive than others.
A discussion of the results is contained in the next section.
-problem gambler. Therefore a true
positive implies the correct identification of a problem gambler and a false-positive
implies the incorrect classification of a non-problem gambler as a problem gambler.
Likewise, a true negative implies the correct identification of a non-problem gambler
and a false-negative implies the incorrect classification of a problem gambler as a nonproblem gambler.
When contextualising the results presented, it is important to remember that the players
analysed in this research represent a heavily skewed subset of very engaged loyalty
card holders. Therefore the performance of the models is conservative, and if models of
this type are operationalised higher accuracy rates would be expected.
Baseline
To be able to interpret the results of the analysis a baseline is required with which a
comparison can be made. The baseline has been established using principles from the
Code of Conduct rolled out by the Association of British Bookmakers (ABB) in March
20146.
This Code itself consists of multiple elements of harm minimisation, one of which is
reiterated here:
Providing customers with new tools such as mand atory time and money b ase d
remind ers, the a bility to set spend and time limits on g aming ma chines and to re quest
ma chine session d ata;
have been set when a player exceeds £250 of spend or 30 minutes of session length.
6
http://www.abb.uk.com/code-of-conduct/
Spend is defined as the total amount of money which has been deposited into the
-up is generated, but the player can still
continue playing. In the remainder of this subsection we define a player to have
received this intervention if they experience this mandatory pop-up.
refers to the total amount of cash that has been loaded into the machine. In the figures
below we will refer to Cash-In rather than spend so that is consistent with the
terminology used throughout the report.
The selection of this Code as a baseline is advantageous as it has been implemented
with the same gambling products and environment in which this analysis took place. It
is acknowledged that the Code of Conduct was derived from best practice rather than a
quantitative analysis similar to that which has been conducted as part of this research
programme. Furthermore the analysis performed on the Code is not intended as an
evaluation of the C
-in-theFeaturespace have been engaged by the Responsible Gambling Trust in a separate
project to complete an early impact study of the Code. The outcome of this evaluation
will be published after the release of this research.
The baseline has been established on both a player and session to match the two types
of models described in this report. For both models we have examined the performance
of session cash-in and length, independently and in combination. When combining the
two variables, Logistic Regression has been used to produce a single score to which a
threshold can be applied to distinguish between problem gamblers and non-problem
gamblers.
There are also some key differences to bear in mind when reviewing the results below in
the context of the ABB implementation:
The player baseline figures are based on average values over all of the
essions. The ABB pop-up is triggered when one session goes above
the £250 limit. As an example, if a player had two sessions, one at £200 and
the other at £275 the average cash in value would be £237.50. In our baseline
analysis it would be inferred that this player would not receive the pop-up,
when in fact they would have received the pop-up on one of their sessions.
The session baseline figures look at the results of each session independently
and therefore report on the percentage of sessions that would receive the
pop-up message. Within this analysis presented it is not possible to infer the
proportion of problem gamblers that received the pop-up. For example it
might be possible that a higher proportion of problem gamblers triggers the
pop-up, just which
Each of these points can be further clarified within the scope of the data available, but
this analysis is outside the scope of this particular report. The subtle nuances of the
implementation of the baseline and its interpretation are important to understand when
contextualising the results presented in this report.
To benchmark the analytical models developed we will look at comparative detection
rates of the new models compared to the baseline (e.g. we will be able to see how
many more problem gamblers could be detected at the same false positive rate). As
each model to enable an immediate comparison. Models that generate higher AUC
values are more accurate than those with lower values.
Player Baseline
To generate the baseline model for players we initially looked at the session cash-in
value and its predictive power. We then took the second element, session length,
investigating its predictive power. Finally the two elements were combined to see what
performance could be obtained.
To investigate session cash-in amount
over the entire length of their available history. Figure 5 shows the true positive and true
negative rates for different average session cash-in values. This figure enables us to
assess the accuracy of correctly identifying problem (the true positive rate) and nonproblem (true negative rate) gamblers at different threshold values. To obtain the best
performance for this model, we want the two detection rates to be at the highest this is
the point where the lines cross. The cross over point is a useful metric to compare the
predictive power of individual variables. For this variable, the cross-over point occurs at
a detection accuracy just below 60% and for an average session cash-in value of £30.
Detection Rates against Average Player Session Cash-In
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
50
100
150
200
250
Average Session Cash-In Value (£)
true positive rate
Figure 5
true negative rate
Analysis of average Session Cash-In to identify problem gamblers.
The mandatory limit set by the ABB of £250 shows that 1.3% of problem gamblers
would receive the pop-up and 99.2% of non-problem gamblers not receive the pop-up,
however 0.8% of the non-problem gamblers would receive the pop-up. In contrast if a
threshold of £100 was selected the proportion of correctly identified problem gamblers
would increase from 1.3% to 10.7% the proportion of correctly identified non-problem
gamblers would reduce from 99.2% to 94.3%. Therefore this means that we are able to
accurately identify more problem gamblers, but at the same time we are also incorrectly
labelling more non-problem gamblers as problem gamblers.
The next step in looking at the baseline model for players was to examine session
length. This is shown in Figure 6. In this case we see that mandatory threshold of 30
minutes identifies a higher proportion of problem gamblers (14.5%), however a lower
proportion (86.4%) of non-problem gamblers would avoid being treated, giving rise to a
13.6% false positive rate. The cross-over point for this variable occurs at around 13
minutes, but at a much lower overall detection rate compared to the average session
300
cash-in amount as presented above (60% compared to 55%). This tells us that average
session cash-in is a better indicator of problem gamblers than session length.
Detection rates and Average Session Length
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
10
20
30
40
50
Time (minutes)
true positive rate
Figure 6
true negative rate
Analysis of the average session length to identify problem gamblers.
So far we have looked at the performance of each measure individually, whereas
logistic regression allows us to look at them together. This is shown in Figure 7. The
Logistic Regression model produces an output between 0 and 1. The selection of a
threshold will produce the detection rates as shown by this figure. The cross over point
for this model is at the detection rate of 60%, this shows that be combining the session
length variable with the total cash-in variable there is no improvement to our model
performance.
60
Detection Rate Baseline Logistic Regression Player Model
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Logistic Regression Score
true positive rate
true negative rate
Figure 7 - Analysis of using average session cash-in and average session length in
a predictive model to identify problem gamblers
Now we have developed three different player baseline models. These can be
compared by plotting the false positive rates for each model against the true positive
rate, see Figure 8. As a reminder, the objective is to get the models to be as close to the
top left hand corner of the chart as possible. Figure 8 shows that the average session
total cash-in variable performs better than the session length variable, which is only
performing marginally above the random model. By combining these two variables in
our logistic regression model we only get a marginal uplift in performance compared to
just using the average session cash-in variable alone.
It is useful in this example to point out how the ROC curve can be used to assess the
performance of the models and the trade-offs which need to be made. In the analysis
above we concluded that average session total cash-in variable was very similar to the
combined Logistic Regression model. The ROC curve corroborates this finding, but it
does show that the Logistic Regression model has a marginal increase in the true
positive rate in the lower range of false positive rates (between 20% and 40%).
1
Player Baseline Model Comparison
1
0.9
True Positive Rate
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
False Positive Rate
Average Session Cash-In
Average Session Length
Combined Logistic Regression Model (AUC=0.62)
Random
Figure 8 - Comparison of Baseline Player Models
0.8
0.9
1
Session Baseline
Now that the player baseline model has been developed, we use the same principle to
develop a session baseline model. For the session model we are now measuring our
accuracy of determining if the session is generated by a problem gambler or not. As
each player has generated many sessions we are making many more predictions. It is
expected that the performance of a session model will be less than that of the player
model as although the session may be from a problem gambler, they might not exhibit
any problematic play in each session that they play.
-in amount. Figure
9 shows the true positive and true negative rates based this variable. From the figure we
can see that at the ABB limit of £250, 4.0% of problem gambler sessions would receive
the intervention and 97.4% of non-problem gamblers would avoid the intervention. The
point of cross-over between the two rate curves is just above 50% indicating that this
variable has minimal discriminating between problem and non-problem gamblers.
Detection Rates and Session Cash-In
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
50
100
150
200
250
Value (£)
true positive rate
Figure 9
true negative rate
Session Cash-In
The next step in building our session baseline model is to look at session length. The
performance of this variable is shown in Figure 10. At the ABB limit of 30 minutes 12.9%
of problem gambler sessions receive the intervention and 87.1% of non-problem
gambler sessions avoid the intervention. Again the cross-over point between the two
rate curves is only marginally above 50%, indicating minimal discrimination between the
two categories of players.
300
Detection Rates and Session Length
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
10
20
30
40
50
Session length (minutes)
true positive rate
Figure 10
true negative rate
Session Length
investigate their combined predictive power. The performance of this model is shown in
Figure 11. Compared to the figures presented previously in this section, the true
positive and true negative rate lines show a sharp jump between 0 and 1. This indicates
that these variables provide limited discriminatory power. Using the logistic regression
model to obtain the same true positive rate as the session length model (12.9%) a
threshold of around 0.225 should be selected, this corresponds to a true negative rate
of 89.4%, and this represents a 2.3 percentage point improvement.
60
Detection Rate Baseline Logistic Regression Player Model
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Score logistic regression
true positive rate
Figure 11
true negative rate
Logistic Regression Session Model
Now that we have our three models we can compare their performance. This is shown
in Figure 12. This comparison shows that the accuracy of each metric is poor, only
Baseline Session Model Comparison
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Score RF global
Session Cash-In
Session Length
Combined Logistic Regression Model (AUC=0.52)
Random
Figure 12 - Comparison of Baseline Session Models
0.8
0.9
1
Summary
In this section we have constructed two baseline models using the two variables
session cash-in and session length to identify problem gambling. We have found that
predicting problem gambling at the player level is more effective than at the session
level. We have also found that using session cash-in is a better predictor than session
length. When these two variables are combined, the session length variable only
provides a minimal uplift to just using the cash-in variable on its own.
their detection rates. The first row of each table corresponds to a threshold on the two
models which generates a 10% true positive rate. The second row of each table
corresponds to a threshold on the two models which generates a 25% true positive rate.
From these tables we can then compare the false positive rates between the models to
see what proportion of non-problem gamblers would be wrongly identified.
Player Model
True
Positive
Rate
False
Positive Rate
True
Negative
Rate
False
Negative
Rate
Baseline Player Model (low)
10%
5%
95%
90%
Baseline Player Model (medium)
25%
15%
85%
75%
Table 7 - Summary of Baseline Player Model Performance
Session Model
True
Positive
Rate
False
Positive Rate
True
Negative
Rate
False
Negative
Rate
Baseline Session Model (low)
10%
8%
92%
90%
Baseline Session Model (medium)
25%
22%
78%
75%
Table 8 - Summary of Baseline Session Model Performance
These results highlight the challenge of distinguishing problem and non-problem
gamblers and the trade-offs that need to be made. Using the baseline player model,
when detecting 25% of the true problem gamblers 15% of the non-problem gamblers
will be incorrectly identified. In the following sections we show how the model accuracy
can be improved by using additional variables.
Player Analysis (Registered Play)
The aim of the analysis in this section is to improve upon the baseline player model to
provide a more accurate prediction of a problem gambler. When building the player
model we are able to analyse how t
different sessions that they complete. The player model can be applied to players who
use a loyalty card. To build the improved player model we first look at the variables that
can be measured across time (i.e. the between session variables defined in Appendix
B). After which we then add variables that are measured within an individual session
(i.e. the within session variables defined in Appendix B). This enables us to investigate
more subtle changes in a pla
behaviour as a whole.
Using Between Session Markers
To obtain an initial view of how well the theoretical markers of harm are able to identify
problem gamblers a predictive model was built as described in the Methodology
section. When building this model we used all of the between session variables
be impacted by the large number of variables and would automatically increase the
weight of those that are the most predictive. The performance of the predictive model is
shown in Figure 13. This figure also includes the baseline player model which only
utilises the average session cash-in and session length variables. The AUC score of the
new model is 0.69 compared to the baseline model AUC score of 0.62. This represents
a 58% improvement in overall accuracy in favour of the new model.
When comparing the two models, in particular in the range of true positive values
between 10% and 60% it can be seen that the new model generates an additional 1015% percentage points in true positive accuracy compared to the baseline. As an
example if we look at the performance of the baseline model at the point indicated by
the red circle on Figure 13 a true positive rate of 40% is achieved for a false positive
rate of 22%. The new player model is able to maintain the same level of false positives,
but instead identify 50% of the problem gamblers. Alternatively if the same true positive
rate of 40% was maintained, the false positive rate could be reduced from 23% to 17%.
Player Model using Between Session Markers
1
0.9
0.8
True Positive Rate
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
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0.4
0.5
0.6
0.7
0.8
0.9
False Positive Rate
Player Model (Between Session Marker, AUC=0.69)
BaseLine (AUC=0.62)
Random
Figure 13 - Player Predictive Model (Between Session markers)
To understand what measures of activity have the biggest influence when distinguishing
between problem and non-problem gamblers the model is rebuilt, but each time
removing one of the inputs to measuring accuracy degradation. The input variables that
cause the largest degradation in model performance are the most influential for it to
make its decision. The results of this analysis showing the top 13 most influential inputs
are shown below in Figure 14.
days did the player play on the
gaming machines. Interestingly the next seven most important features relate to the
financial characteristics of the player, including both how much the player has lost, but
also the impact of how much has been won over a weekly aggregation. Although the
are likely to be co-linear to each other, that is describing broadly the same behaviour,
but in subtly different ways. Towards the end of the top 13, features that describe the
number of losing sessions, number of different games played and the number of
sessions in a week appear. A noticeable absence from the list of important inputs are
elements that describe the session length, the number of bets placed, the game
selection across B2/B3 content or stake size.
1
Number of Playing Days
Maximum Weekly Total Winnings
Average Daily Player Loss
Average Session Total Win
Player Loss
Average Daily Player Total Stake
Average Weekly Net Position
Average Daily Player Loss
Number of Losing Sessions
Average Player Loss (Session)
Maximum Session Different Games
Maximum Daily Total Win
Number of Sessions Per Week
0
0.5
1
1.5
2
2.5
3
3.5
Mean Decrease in Model Accuracy
Figure 14 - Input variable importance for Player Model (Between Session Markers)
Incorporating Within-Session Markers
Having looked at the between session markers it was then important to see if any within
session markers could be used to enhance the prediction of problem gambling. This
was done by incorporating additional inputs that can be derived from the within-session
metrics calculated
additional work was done to examine the potential of transforming the metrics so that
they provided more discriminatory power that could be utilised by the predictive
modelling algorithms. Details of this work is provided in Appendix E.
To incorporate the within-session metrics, we have examined their characteristics over
increase the resolution we have conducted the extremes analysis by comparing
characteristics of the earliest and the latest 30 sessions. We did not find any statistical
differences in their characteristics. The examples of the trends are in Figure 15 and
Figure 16.
The incorporation of time-series data into the model is difficult, as there is a trade-off
between building a profile of regular player behaviour and then identifying potential
sessions of binge behaviours. As we are still making a prediction for the player at the
end of their entire
investigate the time series nature of the data, it would be constructive to be able to
gambling was more problematic than others.
4
Figure 15 - Time series of the trends of session-level probabilities of three nonproblem gamblers. The most recent time corresponds to the first observation, i.e.
the timeline is extending into the past.
Figure 16 - Time series of the trends of session-level probabilities of three problem
gamblers. The most recent time corresponds to the first observation, i.e. the
timeline is extending into the past.
To further improve the model accuracy we identified a subset of the most predictive
variables and experimented with different transformations. The subset of variables
included 17 between session metrics and 12 within-session metrics. A list of the 17
between session variables and their most effective transformation is provided below.
The 12 within-session metrics are provided in Table 10 in the following section and
Average loss during a session (unmodified)
Deposit after Winning vs. Loss (binned by quartiles)
Maximum monthly total pay (binned by quartiles)
Minimal value of the proportion of session cash out (binned by 3 categories:
missing, below the sample average, above the sample average)
Maximum session total (log transformed)
Maximal gap between bets in a day (log transformed and winsorized)
Maximal total session played (log transformed and winsorized)
Maximal number of stakes with high volatility (unmodified)
Maximum deposit per session (unmodified)
Number of days played (winsorized and not)
Number of sessions lost money (unmodified)
Number of sessions lost money per week (unmodified)
Number of sessions per day (unmodified)
Number of stake levels (winsorized and not)
Earliest hour played (unmodified)
Latest hour played (unmodified)
Mean hour of play (unmodified)
For the final piece of analysis on the player model we looked to see what was the
smallest subset of variables that could produce a model that could provide good
predictive power. The Occam razor principle was applied to do this. This principle
essentially provides a framework for deciding if a variable should be added to the
predictive model. It states that although a complex solution may generate optimal
performance, in the absence of evidence, the fewer assumptions that are made, the
less likely it is for these assumptions to be incorrect. The minimal set of variables that
were produced from this analysis was:
Minimal amount of cash out (unmodified)
Number of stake levels (winsorized)
Number of days played (winsorized)
Played at or later than 9pm (unmodified)
A number of alternative models were also generated in this process. These models
include the average within-session problem gambling score, frequency of session and
stakes variability variables. However their performance on the test set was slightly lower
than the subset listed above. Future work should focus on the analysis of alternative
models and identification of the most interpreta ble and a ctiona ble models with similar
predictive ability.
Finally, after incorporating the within-session markers into our player model, Figure 17
compares the performance of this model to the baseline and to the random models.
This final model is only slightly more predictive (AUC=0.70) compared to the model
which only used the between-session variables (AUC=0.69). Compared to the baseline
model, with an AUC of 0.62, overall the player model represents a 66% improvement in
overall model accuracy.
Player Model Comparison
100%
True Positive Rate
80%
60%
40%
20%
0%
0%
20%
40%
60%
80%
100%
False Positive Rate
Enhanced Player Model (AUC=0.70)
Baseline (AUC=0.62)
Random
Figure 17 - Performance of the final enhanced player model after inclusion of the
with-in session markers
To illustrate the performance improvement of the enhanced model, Table 7 from the
Baseline model section has been extended to include comparative performance
metrics at the 10% and 25% true positive rates. This comparison is shown in Table 9.
From the table we can see that at a 10% true positive rate the false positive rate has
reduced from 5% to 3%. Likewise, at the 25% true positive rate the false positive rate
drops from 15% to 9%.
Player Model
True
Positive
Rate
False
Positive Rate
True
Negative
Rate
False
Negative
Rate
Baseline Model (low)
10%
5%
95%
90%
Enhanced Model (low)
10%
3%
97%
90%
Baseline Model (medium)
25%
15%
85%
75%
Enhanced Model (medium)
25%
9%
91%
75%
Table 9 - Comparison of the Enhanced Player Model to the Baseline Player Model
To further understand the performance of the Enhanced model we have compared the
range of scores generated by the model for problem and non-problem gamblers. Figure
18 shows the proportion of problem and non-problem gamblers that fall into different
bands of problem gambling score. From this figure we can see that biggest proportion
(9%) of non-problem gamblers have a score around 0.19. Not surprisingly we can see
that the problem gamblers, on average have a higher score than the non-problem
gamblers. The threshold for making this
problem gambling score is also around 0.24 indicating the unbiased nature of the
model, then it would receive a prediction in favour of the bias held by the model (e.g. to
have a tendency towards either problem or non-problem gamblers).
Player's Problem Gambling Scores
0.12
Proportion of Players
0.1
0.08
0.06
0.04
0.02
0
0.00
0.09
0.19
0.29
0.39
0.48
0.58
0.68
Problem Gambling Score
Non Problem Gambler
Problem Gambler
Figure 18 - Problem Gambling scores generated for Problem and Non-Problem
Gamblers
0.78
Session Analysis (Unregistered Play)
In this section the goal was to identify if we could predict problem gambling behaviour
by only using the data available from that session. This analysis is important as it
demonstrates what is possible when a player interacts with a machine without a player
card. To perform this analysis we have selected each of the sessions generated by the
loyalty card players. The sessions generated by a problem gambler have been labelled
as a problem gambling session, and the remaining sessions labelled as a non-problem
gambling sessions.
After inspecting all of the data the 12 most influential variables were identified. These
variables are presented in Table 10. The variables are listed in order of their predictive
power and the transform which has been applied.
Variable
Transformation
Average Proportion of Cash Out
grouped
Session Start Time
grouped
Number of Stakes of High Volatile
Games
unmodified
Minimum Stake Amount
grouped
Value of Non-Debit Card Cash-
grouped
Number of Stakes of Low Volatile
Games
grouped
Number of Different Games Played
grouped
Variance in Stake
grouped
Number of Different Stake Amounts
winsorized
Amount Cashed Out
zero indicator
Number of different Games
winsorized
Minimum Stake Amount
log
Table 10 - List of most influential variables and their transformation sorted by their
predictive power.
When building the predictive models, individual variables that could be derived from the
data have been examined. To give an example of this, Figure 19 shows an analysis of
likelihood of observing a problem gambling session for different hours of the day. This
figure shows one of the dilemmas with the data we can see that there is an increase
chance of observing a problem gambling session at the beginning or the end of the
day, but the number of sessions that take place during these time periods is
significantly lower. Therefore although there is a significant association, the predictive
ability is still weak.
0.9
0.8
0.7
Probability
0.6
0.5
0.4
0.3
0.2
0.1
0
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Hour of Day
Non Problem Gambler (Probability)
Problem Gambler (Probability)
Figure 19 Figure showing that the likelihood of a problem gambler playing
increases late at night, however, the total number of sessions played decreases
substantially during this period.
The results for the session model are shown in Figure 20. The performance of the
enhanced session model (AUC = 0.63) has improved compared to the baseline session
model (AUC = 0.52) by 550%. Although we have identified a significant improvement
against the baseline model, the accuracy is less than what was achieved for the player
model.
23
Comparison of Session Models
100%
True Positive Rate
80%
60%
40%
20%
0%
0%
20%
40%
60%
80%
100%
False Positive Rate
Enhanced Session Model (AUC=0.63)
Baseline (AUC=0.52)
Random
Figure 20 - Performance of the Enhanced Session Model compared to the
Baseline.
To illustrate the performance improvement of the enhanced session model, Table 8 from
the Baseline model section has been extended to include comparative performance
metrics at the 10% and 25% true positive rates. This comparison is shown in Table 11.
From the table we can see that at a 10% true positive rate, the false positive rate has
reduced by more than half from 8% to 3%. Likewise, at the 25% true positive rate the
false positive rate drops from 22% to 12%.
Session Model
True
Positive
Rate
False
Positive Rate
True
Negative
Rate
False
Negative
Rate
Baseline Session Model (low)
10%
8%
92%
90%
Enhanced Session Model (low)
10%
3%
97%
90%
Baseline Session Model (medium)
25%
22%
78%
75%
Enhanced Session Model (medium)
25%
12%
88%
75%
Table 11 - Comparison of the Enhanced Session Model to the Baseline Session
Model
Additional Experiments
Up until now, we have focused the analytics on distinguishing between problem and
non-problem gamblers using the definition provided by the PGSI screen; that is,
problem gamblers have a score of 8 or above and non-problem gamblers have a score
below 8. In this section we have sliced the data in other ways to see what impact this
has on being able to distinguish between different groups of players.
Removing multiple loyalty ca
amblers
One of the questions in the loyalty card survey asked the participant how many loyalty
cards they owned. If a participant has more than one loyalty card, then we know that we
es.
For the first part of this experiment we only included the players that said they had one
loyalty card. The hypothesis behind his experiment was that the quality of data would
be improved as the players included would have a higher proportion of their gaming
assume that it will be a perfect
card, or not use their loyalty card all of the time.
For the second part of this experiment we also excluded the players who had a PGSI
previously labelled as non-problem gamblers. The hypothesis behind this experiment
was that we would get a clearer differentiation between problem and non-problematic
gambling behaviour.
The results of these experiments are shown in Figure 21. For the first experiment, the
performance curve for the single loyalty card model had a slightly different shape to the
player model, but overall the AUC metric demonstrated that the performance was
similar. However for the second experiment we generated an AUC metric of 0.74, a
27% improvement over the player model.
This result of this second experiment is a really interesting finding. In we focus our
attention the bottom left hand corner of this graph, from the Single Loyalty Card Model
(light blue line), if we operated at a true positive rate of 16% then a false positive rate of
8% would be achieved. If we used the model generated by the second part of this
experiment (the orange line) and operated at the same true positive rate (16%) the false
positive rate is reduced by a third to 2.6%.
included in the second model. Reminding ourselves of the definition of a false positive,
that is a non-problem gambler who is classified as a problem gambler, we can see that
who were labelled as non-problem gamblers from the
model the number of non-problem gamblers who have been classified as problem
-risk players, we can infer that a
classified as problem gamblers.
This is an important finding, as it demonstrates that if a majority of the false positives (in
this case, potentially twothese players is not as significant as triggering an intervention to a non-problem
gambler with a PGSI score of 0.
1
0.9
0.8
True Positive Rate
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
False Positive Rate
Single Loyalty Card and PGSI between 1 and 7 excluded (AUC: 0.74)
Single Loyalty Card (AUC: 0.67)
Player Model (AUC: 0.67)
Random
Figure 21 - Model Accuracy when only Players with a Single Loyalty Card are
included and Players who have an 'at risk' PGSI score excluded.
PGSI Problem Gambling Threshold
The specification for the problem gambling severity index prescribes that gamblers who
score 8 or above should be categorised as problem gamblers. On further analysis of
how this threshold was found, it was determined by a sample size of 109 students, of
which only 7 out of 9 students had a score of 8 or above. In our sample we have 951
players who score 8 or above, and this enables us to further investigate how well the
model can distinguish between different levels of PGSI score. Figure 22 shows the
number of customers that have PGSI Scores of 8 and above. This graph shows a
gradual tailing off of the number of players who achieve the greatest score within the
PGSI Scale.
Figure 24 shows the performance for different models when we modify the threshold
used to distinguish between problem gamblers and non-problem gamblers. In this
case, problem gamblers are defined as those being at or above the threshold level and
non-problem gamblers being below the threshold. From this analysis there are two
interesting results:
The highest AUC score is achieved using a threshold of 19, performing particularly
well in the true positive range between 50% and 90%. At this threshold, the
3788. Further work needs to be carried out to understand why this threshold
performs so well and to ensure this is not an anomaly with the data used.
1
At the highest threshold applied, 23, we have a comparatively high true positive
rate of 26% for a small false positive rate 1.4%. At this threshold 73 players are
labelled as problem gamblers. Although there are a reduced set of players in the
positive class, this result illustrates that extreme forms of problem gambling can
be identified relatively accurately.
140
120
Number of Players
100
80
60
40
20
0
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
PGSI Score
Figure 22 - Histogram showing the number of players who have PGSI Scores of 8
and above.
26
27
1
0.9
0.8
True Positive Rate
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
False Positive Rate
Random
PGSCORE=11 (AUC: 0.67)
PGSCORE=19 (AUC: 0.75)
PGSCORE=23 (AUC: 0.68)
PGSCORE=15 (AUC: 0.66)
Figure 23 - Model Performance using different thresholds to label Problem
Gamblers
To continue on from the previous section in which the performance of the analytical
models is compared when different thresholds are used to distinguish between problem
and nonwhen
the non-problem gamblers are always identified by a PGSI Score of 0, but the threshold
used to define a problem gambler is increased from the range of possible values of 1
through to 27.
The result of this analysis is shown in Figure 24. From this figure we can see that there is
a general pattern of the ability to predict problem gamblers improving when the of PGSI
score increases. The maximum AUC score is 0.77 and occurs at thresholds 13, 16 and
19. Interestingly these scores are all three points away from each other, which is the
maximum score given to some questions in the PGSI screen.
1
0.80
Predictive Model AUC Value
0.75
0.70
0.65
0.60
0.55
0.50
0
5
10
15
20
25
PGSI Threshold
Figure 24 - Model Performance for different thresholds for defining Problem
Gamblers
Predicting PGSI Screening Question Responses
The previous experiment then leads us on a journey to see if particular PGSI Screening
question responses were more predictive from the data than others. For this analysis we
PGSI Screening Questions is repeated below:
1.
2.
3.
4.
5.
6.
7.
8.
9.
How often have you bet more than you could really afford to lose?
How often have you needed to gamble with larger amounts of money to get
the same feeling of excitement?
How often have you gone back another day to try to win back the money you
lost?
How often have you borrowed money or sold anything to get money to
gamble?
How often have you felt that you might have a problem with gambling?
How often have people criticized your betting or told you that you had a
gambling problem, regardless of whether or not you thought it was true?
How often have you felt guilty about the way you gamble or what happens
when you gamble?
How often has your gambling caused you any health problems, including
stress or anxiety?
How often has your gambling caused any financial problems for you or your
household?
The results of our analysis are presented in Figure 25. Here we can see that questions
2, 6, 8 and 9 are the most predictable with questions 3 and 4 being the most difficult.
Questions 8 and 9 are two of the questions which are more related to gambling related
harm, so it is encouraging that these are highly predictive. Interestingly, question 3
30
which relates to chasing behaviour is the second hardest question to predict from the
available data.
0.66
Predictive Model AUC
0.64
0.62
0.6
0.58
0.56
0.54
0.52
0.5
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
PGSI Question
Figure 25 - Predictability of individual PGSI Screening Questions
Gambling Type Analysis
Within the Loyalty Card Survey Report analysis was completed to identify four different
clusters to describe different gambling types. Detailed descriptions of the classes are
obtained in Report 2. A brief description is provided below:
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Lowest engaged gamblers
Moderately engaged gamblers
Substantially engaged gamblers
Heaviest engaged gamblers.
The results of distinguishing problem gamblers and non-problem gamblers for each of
the four clusters is shown in Figure 26. From the overall AUC metrics for each of the
clusters, problem and non-problem gamblers are more easily distinguished in Cluster 1
and Cluster 2. These clusters have the lowest levels of engagement across different
forms of gambling.
gaming machines, if they have a low engagement across other forms of gambling, then
for this group we will be analysing a higher overall proportion of their entire gambling
activity. Conversely, for more engaged players, the gaming machine activity that we are
analysing will be a lower proportion of their overall gambling activity. This result
provides some evidence that to minimise problem gambling, the entire range of
gambling products needs to be considered.
For the most engaged gamblers in the study, Cluster 4, it is interesting to observe that
in the bottom left hand corner for true positive rate at approximately 15% we are able to
reduce the false positives substantially compared to the other gambling types.
1
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Cluster 1 (AUC: 0.67)
Cluster 2 (AUC: 0.67)
Cluster 4 (AUC: 0.63)
Random
Cluster 3 (AUC: 0.65)
Figure 26 - Predicting problem gambling amongst different gambling types.
Factor Group Analysis
In Report 2 exploratory factor analysis of the PGSI screen was conducted to explore the
different types of harms that people may be experiencing. This analysis resulted in two
factors being identified:
Factor Group 1 relates to harmful gambling actions and includes chasing losses,
gambling with more money to get the same excitement and betting more than one
can afford to lose
Factor Group 2 relates to harmful gambling consequences and includes items
such as people criticising behaviour, health impacts, financial difficulties or feeling
guilty about what happens when the participant gambles.
Within this report we have built a predictive model for the two factors to understand if
players associated with one of the factors are more predictable than the other. The
results produced by these models are shown in Figure 27. This analysis shows that
Factor Group 1, harmful gambling actions, is more predictable (AUC=0.63) than Factor
Group 2, harmful gambling consequences (AUC=0.60) by 30%.
1
Predictive Models based on PGSI Response Factor Groups
1
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Factor Group 1: AUC: 0.63
Factor Group 2: AUC: 0.60
Random
Figure 27 - Predictive Models based on PGSI Response Factor Groups
Debit Card Usage
At the end of the time available to complete the research project we had some success
with matching over the counter debit card transactions with cash-in events on the
gaming machines. The matching rate achieved on the transactions was about 67%. A
perfect match was not expected as not all transfers from an employee will be due to a
debit card transaction. From the debit card transaction we calculated 6 new variables to
consider in our player model. The variables were:
Total amount deposited with a debit card across all sessions.
Total number of deposits with a debit card across all sessions.
Number of Sessions where a debit card was used.
Maximum amount deposited with a debit card in a session.
Maximum number of deposits with a debit card in a session.
Average value deposited with a debit card in a session.
Of the 3,988 players included in this analysis, 1,394 used a debit card in at least one
session. To measure the predictive power of these variables we added these variables
to our between session markers and built a new predictive model. The performance of
the between session marker predictive model with and without the debit card variables
is shown in Figure 28. The AUC values for both of these models was 0.69 showing that
these variables did not improve the model.
Only a limited amount of time was available to explore the use of this data. Through
future exploration it is still believed that this data will help improve the accuracy of the
model.
1
Player Model Performance with Debit Card Usage
1
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Random
Between Session Variable Player Model (AUC: 0.69)
Between Session Variable Player Model with Debit Card Variables (AUC:0.69)
Figure 28
Player model performance using debit card variables
1
Discussion
The key research objective of this study was to determine if it was possible to
distinguish between harmful and non-harmful gaming machine play. The Problem
Gambling Severity Index (PGSI) score was obtained for 3,988 loyalty card holders (who
agreed to data linkage) and was used as a proxy for identifying harmful play. Our
analysis shows that it is possible to distinguish between problem gamblers and nonproblem gamblers. The key findings are that:
For the player model, distinguishing between problem and non-problem
players was 66% more accurate than the baseline model.
For the session model, distinguishing between problem and non-problem
gambling sessions was 550% more accurate than the baseline model.
However, the baseline model only performed slightly better than random.
positive rate can be significantly improved. This indicates that many of the
Increasing the PGSI score threshold used to distinguish between problem and
non-problem gamblers improves detection accuracy. The biggest uplift was
found for a PGSI score threshold of 19.
The responses to each of the questions within the PGSI screen demonstrate
different levels of predictability. The most predictable questions are:
o Q2: How often have you needed to gamble with larger amounts of
money to get the same feeling of excitement?
o Q6: How often have people criticized your betting or told you that you
had a gambling problem, regardless of whether or not you thought it
was true?
o Q8: How often has your gambling caused you any health problems,
including stress or anxiety?
The two least predictable questions are:
o Q3: How often have you gone back another day to try to win back the
money you lost?
o Q4: How often have you borrowed money or sold anything to get
money to gamble?
Problem Gamblers who had the lowest engagement across multiple gambling
products, but did gamble on gaming machines, were more distinguishable
than players who engaged in many gambling products.
Players who matched the factor for harmful gambling actions were more
distinguishable than players who matched the factor for harmful gambling
consequences.
Note, the baseline model used to calculate the uplift in model accuracy have been
-in
and session length. These variables were selected as they are used in the current code
of conduct by the ABB to generate popresearch it is not possible to determine the impact of gaming machine play to problem
gambling status as the participants in the study participated in many different forms of
gambling.
The players studied in this research represent the more engaged subsection of gaming
machine players. Therefore the results presented in this document are likely to
represent conservative estimates in regards to the accuracy of distinguishing problem
and non-problem players. If these results are operationalised we would expect a
reduced false positive rate; that is, less non-problem gamblers would be incorrectly
identified as problem gamblers, as in general their activity would be less involved.
Can we identify harm?
In this research programme we demonstrated that is possible to identify harm via the
proxy of the Problem Gambling Severity Index. Prior to this research taking place, the
Association of British Bookmakers had implemented a code of conduct which triggered
pop-ups on the gaming machine when either a cash-in limit or time limit was reached. A
baseline model was constructed using these two variables, which delivered an AUC
performance of 0.62. After incorporating additional data elements, a predictive model
was developed which delivered an AUC performance of 0.70. This represented a
performance increase of 66%. By applying a stricter definition of problem and nondefine a problem gambler) the highest model performance of 0.77 was achieved. This
represents a 125% improvement against our original baseline model.
If we look at other applications of predictive analytics in the gaming sector, we generally
see higher detection accuracies. For example, remote operators can identify fraud with
an AUC performance of 0.90 or more, and customer churn7 within an AUC accuracy of
0.85 or more. But to provide context we need to consider what these models are trying
to achieve.
To begin with, in the remote environment we have the ability to measure a player
activity in much more precise detail and cover many more interactions. In comparison,
the gaming machine environment allows only a small proportion of customers to be
monitored. Not all of the interactions are captured, and uncertainties exist about
defining players
When identifying Fraud, we have a precise definition of what we are identifying, for
example, will this player generate a deposit transaction which will then result in a
chargeback from their credit card issuer? Similarly, in dealing with customer churn we
have a precise definition. We may ask, will the particular customer use our product or
service in the foreseeable future (such as 2 weeks, 3 months, etc.)? These predictions
are heavily biased to the actions which are taking place within that environment, e.g., Is
the player defrauding me? or Is the player not likely to return to my product or service?
If we compare this to the task of predicting harmful play, we have a broad definition of
someone who is spending more time or money than they can afford. To provide a more
precise definition we are using the Problem Gambling Severity Index, and in particular,
players who score 8 and above to be problem gamblers. The screen has been
to d ete ct individuals in the g eneral population who have a g ambling
problem, or who are at risk of d eveloping a problem 8
threshold of 8 is generally well accepted, but is only drawn from a sample of 148
7
Predicting customer churn is an application of predictive analytics where customers
who are at risk of no longer gambling with the particular operator are identified.
Customers identified by this model are generally targeted with retention offers to keep
them as customers.
8Page 7, http://classes.uleth.ca/201201/hlsc3700a/The%20Canadian%20Problem%20Gambling%20Index.pdf
interviews of clinical respondents to a sub-sample drawn from an initial survey. The
defined as in our other examples.
This imperfection has two consequences in the model building process. Firstly, in the
model building stage, decisions are made both in the design of input variables and the
training process which is guided by these labels. Secondly, when evaluating the impact
we are again using imprecise labels, so there could be circumstances were the model
The assessment of accuracy in this project is measured against the player, not the
point: a problem gambler may only exhibit harmful play in some forms of gambling. At
the same time, the PGSI screen is a broad assessment of their overall activity across
multiple products. Therefore if their gambling machine behaviour is not harmful, this
increases the error of the prediction. There is a counter-argument that if a player is a
problem gambler, this behaviour will be exhibited in all forms of their play.
However, given all of these challenges, we have shown that is possible to identify
problem gamblers. Whilst mode
improvement to current
methods of identification. In
from predictive models deployed for fraud and customer churn in a remote environment
where richer data and a more precise target variable are available.
As a research team, we therefore believe that there is a bright outlook for the
application of behavioural analytics to enhance the social responsibility strategy of
operators to protect against harmful play on gaming machines. However, operators will
need to make trade-offs when identifying problem gamblers, as some non-problem
gamblers will also be identified and therefore receive miss-targeted interventions.
Research Implications
From the research that has been completed there are number of important implications
that should be considered.
Multiple Variables
It was demonstrated in this research that to be able to adequately distinguish between
problem and non-problem gamblers that a combination of variables needs to be
considered. It was not possible to accurately identify problem gamblers through one
variable alone. This demonstrates that to help mitigate the impact of problem gambling
the focus should shift away from regulating particular parameters, such as stake size,
but take a balanced, rounded approach which considers the player, the product and
the environment.
Registered vs Non-Registered Play
Being able to identify problem gamblers from registered play was more successful than
identifying problematic sessions in non-registered play. The research team had been in
doubt that non-registered play would yield actionable analysis. However, as universal
understand what operators can do in this situation to help fortify their responsible
gambling efforts. It was demonstrated that compared to the baseline measurement, it
was possible to more accurately identify problematic sessions. Although the accuracy
was less than for registered play, we need to acknowledge that not all gaming sessions
are likely to exhibit problematic play. The method for measuring accuracy of this model
assumed that all sessions generated by problem gamblers exhibited problematic play,
leading to a conservative accuracy estimate. Therefore it would be premature to
dismiss the potential value of within-session markers to minimise harm on nonregistered play.
Mandatory ABB Limits
Through the development of the baseline models some preliminary analysis on the ABB
mandatory limits was possible. Through this analysis it was demonstrated that the
mandatory limits set within this code are too high. For example, by looking at the
average session cash-in value, at a threshold of £250 only 1.3% of the problem
gamblers would have been identified.
Research Limitations
There are a number of limitations to the conclusions that can be drawn from this
research program. Most importantly from this research it is not possible to determine
the impact of gaming machine play on overall problem gambling status, as the
participants in the study engaged in many different forms of gambling.
Limitations have had an impact on our understanding of how the predictive models
would perform when operationalised, or decreased our ability to provide better
discrimination between the problem and non-problem gamblers. Three of the key
limitations are highlighted below.
Heavily engaged participants The sample of loyalty card surveys included
in this study represented a heavily skewed subset of gaming machine players
and their associated sessions. The impact of this is that the performance of the
models developed in this report is likely to be a conservative estimate of how
they would perform in practice (in particular for the false-positive rates).
From the loyalty card survey we know
that only 49% of the participants either always or almost always used their
loyalty card. We also know that on average the participants engaged in 4.8
activity that is studied in this research is limited. With a more complete view of
a pla
developed.
Incomplete view of a player s gambling decisions The data that was
available for this research project only provided transactional details of the
interactions that took place on the machine. This excluded information relating
to the selection of bets within the game. Being able to incorporate the risks
being taken by a player on each bet is likely to generate a more predictive
model.
Future Research
This initial research has just scratched the surface of what is possible. With further
research, accuracy can be improved to reduce the impact of necessary trade-offs when
operationalising the predictive models described in this report. There are numerous
areas of investigation that are likely to improve accuracy. Four of key areas are
discussed below:
Better target variable The PGSI screen has been used as a proxy in this
analysis for identifying harmful gaming machine play. In this report we have
highlighted several deficiencies with this screen which are likely to contribute
to inaccuracies of the developed models. These deficiencies include the
choice of threshold for determining a problem gambler and questions that refer
to harmful gambling consequences rather than harmful gambling actions. It is
unrealistic to expect that a model built on analysing gaming machine data will
accurately predict harmful gambling consequences. By developing a target
variable which is more closely aligned to harmful gaming machine behaviour,
we are likely to generate a significantly more accurate model and be able to
more accurately target interventions.
More variables Through the process of delivering this research programme
we have learnt significantly more about how players interact with gaming
machines. For example, in the patterns of play report we see that higher stakes
and spend typically occur on mixed B2 and B3 content sessions. Therefore
there is significant scope to develop additional variables based on our
enhanced knowledge of gaming machine behaviour.
Improved operation definitions of existing variables When reviewing the
variables that were most informative to the predictive models we were
surprised that variables relating to some markers where absent, for example:
game volatility and chasing. It would be premature to rule these markers out as
being important for identifying harmful play. Through further analysis of the
data, the way these variables are defined could be improved, providing further
insight into how the machines are used and facilitate improvements to the
predictive models.
Improved measurement of existing variables A larger range of variables
were considered in this research project. However, there was not sufficient
time to be able to further investigate how the measurement of these variables
could be improved. For example with the debit-card data, by working more
closely with the operators it may be possible to provider higher matching rates
and also incorporate declined debit card transactions. Improvements could
also be made by grouping together gaming machine sessions into a higher
level
proxy. This would enable more behaviours around movement
between machines in a venue to be further understood.
Whilst delivering this research program a number of question came out of discussions
with the research team when considering how the predictive models could be
operationalised. The main questions are summarised below:
What is the impact of delivering a harm minimisation intervention to a non problem gambler? In this research project, we have identified that trade-offs
need to made when using a predictive model to identify problem gamblers, in
that some non-problem gamblers will be incorrectly identified as problem
gamblers. The impact, both commercially from an
perspective, and
from an enjoyment perspective of a player, is not yet fully understood.
Understanding the nature of any potential impact would enable operators to
make a more informed decision when deciding how to operationalise the
predictive models.
Developed targeted interventions based on problem gambling behavioural
subtypes. From the complexities of the developed models, we know that
problematic gambling takes multiple forms. Further exploring the behaviours
of the different types of problem gamblers would enable targeted interventions
to be developed, which through testing and evaluation would enhance our
ability to minimise gambling related harm.
How could behaviour across multiple gambling products be utilised? The
research presented in this report only considers gambling activity on gambling
machines. We know that the survey participants engage in multiple forms of
gambling. By understanding how
transition between different forms of
gambling would provide useful insight into how harmful gambling play could
be reduced for a player across multiple gambling products.
Recommendations
When considered against previous gambling research that has been conducted in last
40 years, this research program (executed over six months) represents one of the
largest step changes in knowledge about problem gambling behaviour. One of the key
ways in which this been achieved is through the collaboration of a number of
organisations with a variety of backgrounds. Based on what has been learnt through the
analysis of the data, and to maintain the momentum gained from this research we have
identified 5 key recommendations:
Live Trials The results presented in this research show that is possible to identify
problem gamblers using behavioural modelling. To further validate this result, it is
important to operationalise the results of this research. It would be a missed
opportunity if all of the learning remained locked in the pages of reports
gene
enables problem gamblers to be identified, but rather a variety of factors combine
to enable problem gambling behaviours to be identified. It follows that
interventions will therefore be ineffective if they focus on addressing one particular
variable, such as stake size.
The identification of harmful play on a gaming machine is only one step to a final
objective of being able to generate interventions on the machine which would not
only be effective in reducing problem gambling, but also not detract from the
experience of non-problematic gamblers.
Being able to focus interventions on players who are most likely to be at risk
means that different types of interventions can be targeted to players and their
performance evaluated. It is important that this is done in a test-and-learn cycle so
that our understanding of the efficacy of various types of interventions continues to
evolve.
Continued Industry Involvement One of the key successes of this research
project has been in the involvement of the industry. Our analysis of gaming
machine play has only scratched the surface of the full range of gambling
products available to consumers. By enriching our understanding beyond gaming
machines, further insight into how to ensure a safer gambling environment for
gamblers will be gained. For example, when a player self-excludes, or complains,
it would be useful to understand why the player decided to exclude and to get
permission to use their data for research.
Treatment Provider Involvement
One of the fundamental aspects of this
loyalty card data. Such a time-consuming data collection process would be further
enriched with data available from a variety of treatment providers. To enable the
continued understanding of problem gambling behaviour, building a usable
knowledge base from treatment providers and gaining consent to obtain and link
industry data would be an invaluable asset.
Continued Data Exploration Analysis of industry data has only taken place at
the final stages of the research program. Further exploration into the potential of
what this data provides will help improve the models and uncover additional
insights into gambling behaviour.
Review Screening Tools We now have a significant amount of information on
which to further analyse the PGSI screening tools, in particular the weighting and
scoring of the individual questions. More development of these screening tools will
enable further understanding of some of the deeper relationships between the
individual questions and the behaviour observed from the players.
Conclusion
The objective of this research program was to answer the following two challenges from
the Responsible Gambling Strategy Board:
Is it possible to distinguish b etwe en harmful and non-harmful g aming ma chine
play?
If so, what me asures might limit harmful play without imp a cting those who do not
exhibit harmful b ehaviours?
The focus of this research has been to answer the first question. To meet this goal, we
have worked with the industry obtained data relating to the activity of players on their
machines, as well as loyalty card holders. This enables players to be screened for
problem gambling using the PGSI screen. Report 2 in this series of documents
describes the methodology and results of that exercise. This report describes the
methodology and results of combing the PGSI screening data with the industry data to
identify problem gamblers.
The results of the analysis show that using the PGSI Screen as a proxy for measuring
harmful play, it is possible to distinguish between harmful and non-harmful gaming
machine play. This is the first time that this type of analysis has been performed on a
large sample across multiple operators and therefore this result marks a significant step
forward in the progress towards understanding problem gambling and more general
gambling behaviours.
To measure the accuracy of the analytical models, a baseline metric was produced
based on the current measures used by the Association of British Bookmakers to
generate pop-up interventions on their gaming machines. The AUC value for measuring
accuracy of this baseline model was 0.62. The best model that used the PGSI definition
of problem gambling generated an AUC score of 0.70. By using this model, an
additional 10-15% more problem gamblers would be identified with the same false
positive rate that is, there would be no increase in non-problem gamblers being
flagged as problem gamblers. When looking at individual sessions, the baseline model
generated an AUC value of 0.52. The model built during this research generated an
AUC value of 0.63. This effectively enables the proportion of detected problem
gamblers to be improved by 15%, or, alternatively, the proportion of non-problem
gamblers identified as problem gamblers is reduced by 15% a significant
improvement when compared to previous methods of measurement.
Additional experiments were run on the data which produced some interesting findings.
The first was that an increase from the PGSI threshold from 8 to higher values produced
an early uplift in being able to identify a subset of problem gamblers very accurately.
For example, at a PGSI threshold of 23, 26% of the problem gamblers could be
identified with a false positive rate of 1.4%. The objective of the model was also
modified to distinguish between players who had a problem gambling score of 0 (i.e. a
non-problem gambler), and those who had a problem gambling score above a
particular threshold. The accuracy of the predictive models was measured for the range
of possible PGSI scores. At thresholds of 13, 16 and 19, an AUC score of 0.77 was
achieved. This is a significant uplift against earlier models and provides an interesting
insight for further analysis of the PGSI screen.
When investigating the predictability of individual PGSI screening questions, the
following three were the most predictive:
Q2: How often have you needed to gamble with larger amounts of money to get
the same feeling of excitement?
Q6: How often have people criticized your betting or told you that you had a
gambling problem, regardless of whether or not you thought it was true?
Q8: How often has your gambling caused you any health problems, including
stress or anxiety?
The two worst performing questions are:
Q3: How often have you gone back another day to try to win back the money you
lost?
Q4: How often have you borrowed money or sold anything to get money to
gamble?
The result of Q3 being the second least predictive has a potential ramification for the
ability to be able to identify chasing behaviour from this gambling product.
Finally, after reflecting on the results of this research, we have made the following 5
recommendations to take these positive results forward, both within the industry and for
further research:
Live Trials The results of this research should be evaluated in a live environment
so the effectiveness can be more accurately measured;
Continued Industry Involvement The collaboration with the industry in the
execution of this project has been one the keys to its success. This relationship
between industry and researchers should be further developed to other operators
within the industry;
Treatment Provider Involvement The ability to accurately identify problem
gambling behaviour within the data has been a fundamental component of this
research. To further enrich this data set would enable research to continue to
evolve as gambling behaviours and products evolve;
Continued Data Exploration This research project was limited by time
constraints, rather than exhausting the range of ideas that the research team has
for investigating the data. Through further investigation, additional insights will
appear to enable enriched understanding gambling behaviour, and in particular
harmful gambling behaviour;
Review Screening Tools This research has highlighted some of the limitations of
the existing screening tools. Using this data set, it would be possible to further
analyse the PGSI screening tool and potentially identify new weights for defining
gambling categories or alternatively different weights for each of the questions.
About Featurespace
Featurespace is a UK technology company at the vanguard of predictive analytics,
pioneering the next level of data analysis: Adaptive Behavioural Analytics. We combine
the very latest research in statistics and data analysis with a unique method of
modelling human behaviour.
core ARICTM technology is a revolutionary
approach to accurately predicting what individuals and dynamic groups of people will
do, in real time. Featurespace has deployed a series of award-winning products for
fraud and risk management, as well as customer insight and retention, and is
recognised as an industry authority on responsible gambling and player protection.
To find out more, visit http://www.featurespace.co.uk/
About RTI
RTI International is one of the world's leading research institutes, dedicated to
improving the human condition by turning knowledge into practice
more than 3,700 provides research and technical expertise to governments and
businesses in more than 75 countries in the areas of health and pharmaceuticals,
education and training, surveys and statistics, advanced technology, international
development, economic and social policy, energy and the environment, and laboratory
and chemistry services. RTI has established itself as a central player in expanding
knowledge about the consequences of substance abuse and the efficacy of programs
that combat it. Substance use and mental health research program emphasizes the
development of improved methods of measuring substance abuse and its
consequences in high-risk populations. RTI uses innovative predictive analytics
methods to evaluate the impact of policies and interventions.
RTI gambling analytics team: Georgiy Bobashev, Robert J. Morris, Paul Ruddle
References
Hoffer L., Bobashev G.V., Morris R.J., (2011) Simulating patterns of heroin addiction
within the social context of a local heroin market. In Gutkin B. & Ahmed S. (Ed.), The
computational neuroscienc e of drug a d diction . Springer Verlag. pp. 313-331
Bobashev, G., Liao, D., Hampton J., and Helzer, J., Individual patterns of alcohol use.
(2014) Ad dictive B ehaviors 39(5),934 940
Fagerström K. Time to first cigarette; the best single indicator of tobacco dependence?
Monaldi Arch Chest Dis. 2003;59:91 4. Transdisciplinary Tobacco Use Research
Center (TTURC) Tobacco Dependence. Baker TB, Piper ME, McCarthy DE, et al. Time
to first cigarette in the morning as an index of ability to quit smoking: implications for
nicotine dependence. Nicotine Tob. Res. 2007; 9:S555 70.
Document Information
Document History
Version
Date
Modified By
Comments
0.1
31-October-2014
David Excell
First draft report sent to RGT for peer review
by MROP2.
0.2
28-November-2014
David Excell
Final version for publication on the RGT
website.
Appendix A
Calculating Proxy Sessions
To calculate the proxy sessions, Featurespace developed an algorithm to score each
To identify the optimal threshold for determining the start of a new session, the accuracy
of the score was measured against sessions defined by the use of a loyalty card. The
result of this process is shown below in Figure 29
Indicative Proxy Session Detection Accuracy
100%
True Positive Rate
80%
Operating point generating
a true positive rate of 87.3%
and a false positive rate of
11.8%
60%
40%
20%
0%
0%
20%
40%
60%
80%
False Positive Rate
Figure 29 - ROC Curve for the Proxy Session detection Algorithm
For the results presented in this report, the threshold of 0.35 was selected. This
threshold delivers a true positive rate of 87.3% with a corresponding false positive rate
of 11.8%. If a lower threshold was chosen (moving further right along the ROC curve),
the false positive rate would increase, resulting in an overall reduction in the reported
session lengths. We are confident that the setting selected provides an optimal
equilibrium between short and long sessions.
To provide an illustration of how the Proxy Session process works, a sample of activity
from one gaming machine is provided in Table 12. In this table, the first 6 columns
represent data that has been provided by the industry. Of particular interest is the first
column, which indicates the events when the player has their card inserted into a
machine. The last three columns illustrate the data which is added when calculating the
proxy sessions. Each of these columns is defined as:
Proxy Session Score provides a threshold which can be applied to determine if
a new session has started.
Session ID The unique identifier assigned to the session.
Proxy Session PlayerID The player ID that is now associated with each of the
events based on the extent of the newly defined session.
100%
The table has been shaded so that the alternative sessions are highlighted in a different
colour. The derived columns have been shaded in a darker colour. A Proxy Session
Score threshold of above 0.35 has been used to define a new session. In this particular
example we can see that in the second session (with ID 987655), we have extended the
player ID to include the Cash Out transaction. It is interesting to note that in this
example there was a 9 minute gap of inactivity between the player putting the money
into the machine and then deciding to take it all out.
It is also interesting in this example to observe how choosing a higher threshold could
have impacted the analysis. For example If a threshold of 0.4 had been selected, that is
proxy session scores above 0.4 are only used to indicate new sessions, then the first
resulted in sessions 987655 and 987656 being merged and player 123456 being
mapped to the following 4 stakes (where Action = Play).
PlayerID
Timestamp
Value
Balance
Action
Game
Proxy
Session
Score
Session
ID
09:18
-1160
1180
Play
Roulette
0.00
987654
09:18
720
1900
Win
Roulette
0.00
987654
09:18
-1160
740
Play
Roulette
0.00
987654
09:18
1260
2000
Win
Roulette
0.00
987654
09:19
-1160
840
Play
Roulette
0.00
987654
09:19
1440
2280
Win
Roulette
0.00
987654
09:19
-1160
1120
Play
Roulette
0.00
987654
09:20
-1120
0
Play
Roulette
0.00
987654
123456
12:53
1000
1000
CashIn
0.58
987655
123456
123456
12:53
1000
2000
CashIn
0.08
987655
123456
123456
12:54
1000
3000
CashIn
0.04
987655
123456
13:01
-3000
0
CashOut
0.00
987655
123456
13:05
200
200
CashIn
Roulette
0.38
987656
13:05
10
210
CashIn
Roulette
0.04
987656
13:05
-210
0
Play
Roulette
0.00
987656
13:05
360
360
Win
Roulette
0.00
987656
13:06
20
380
CashIn
Roulette
0.02
987656
13:06
-380
0
Play
Roulette
0.00
987656
13:06
500
500
CashIn
Roulette
0.10
987656
13:07
-480
20
Play
Roulette
0.00
987656
13:07
200
220
CashIn
Roulette
0.16
987656
13:07
-220
0
Play
Roulette
0.00
987656
13:29
500
500
CashIn
Slots
0.58
987657
13:29
-20
480
Play
Slots
0.00
987657
Table 12 - Example application of the Proxy Session Algorithm. The unit of the
value and balance fields is pence.
Proxy
Session
PlayerID
It is important to note the impact that the definition of the proxy session has in the
indeed multiple visits on a given day. For example, if the proxy sessions are too short
we will see overall reductions in total staking levels, reloading, and changes in games.
Conversely, if the proxy sessions are too long, then we will be asserting that players are
spending more money and time on the machines than what is actually occurring.
Appendix B - Measurement of Harm
Markers
This appendix outlines how Featurespace has calculated each of the markers of
plausible harm from the preliminary dataset (1-September-2013 to 30-November-2013).
We have included histograms to show the distributions of each of the variables used to
describe individual markers. Each of the markers has been converted into a number of
variables for exploration. Combinations of variables form a harm marker, or metric, for
the purpose of analysis.
These histograms have been scaled to show 95% of the complete range of values for
-tailed data
contains extreme values which occur more frequently than are expected from a more
For heavymedian value rather, than the mean value, for more accurate interpretation. The median
value is calculated by sorting the data and selecting the value in the middle. The mean
value is calculated by summing all of the values and dividing by the number of values
present, and is therefore subject to distortion by a few values which are significantly
different to the majority.
If the reader is understanding general behaviours on machine players they should refer
to the Patterns of Play report included in this research programme.
Between Session Metrics
sessions associated with a registered player. These values, extracted from 3 months of
data ranging from 1-Sep-2013 to 30-Nov-2013, have then been aggregated so that a
value for each of the defined outputs is generated for each registered player.
Some of the common errors within this data set concern:
A registered player may not always be using his/her loyalty card, or may share
a loyalty card with an unregistered player.
Players may visit different operators.
Registered players may be duplicated if they are registered in more than one
LBO.
Two of the operators introduced their player cards a few months prior to the
time period represented by this data, so it may not represent stable behaviour.
Customers who have only used their card once during a session may skew the
data to an unknown extent.
1) Frequency of Play
Aim
The aim of this metric is to understand the frequency that a player uses a Gaming
Machine.
Measurement
discussed in previous sections. A player may have multiple sessions on any given day.
Outputs
The following variables are calculated for this marker of harm:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
Total number of sessions between 1-Sept and 30-Nov 2013
Maximum number of sessions on any one day
Average daily sessions
Maximum number of sessions in any one week
Average weekly sessions
Maximum number of sessions during a month
Average monthly sessions
Average number of days between sessions
Maximum length of successive playing days
Shortest gap between playing sessions
Longest gap between playing sessions
Average gap between playing sessions
Errors
The challenge with this measure is that the frequency may not be constant, and
therefore aggregation transformations may hide specific increases in the rate of activity.
As an example, when calculating the number of sessions over a four week period, the
two players below would look the same:
Player 1
Player 2
We ek 1
1
1
We ek 2
1
3
We ek 3
1
0
We ek 4
1
0
Total
4
4
The accuracy of the proxy sessions will also have an impact on some of the variables, in
particular the maximum number of daily sessions and the shortest gap between
sessions. In these cases, a session may be mistakenly separated into two different
sessions, resulting in the incorrect observation of more activities that are closer
together.
Results
From the detailed results below, during the 3 month period a typical player will have 5
sessions and at the most 3 in a week, or 4 in a month. However at the 90 th percentile a
player will have 40 sessions with up to 8 sessions in a day, 14 in a week and 25 in a
month.
The histogram above shows the total number
of sessions between 1-Sept and 30-Nov 2013.
The median value is 5 sessions. The number
of sessions at the 10th and 90th percentile is 1
and 40 sessions retrospectively.
The histogram above shows the maximum
number of sessions from a player in any one
day. The median value is 4 sessions. The
number of sessions at the 10th and 90th
percentile is 1 and 8 retrospectively.
The histogram above shows the average
number of sessions per player per day. The
median value is 1 session. The number of
sessions at the 10th and 90th percentile is 1
and 3 respectively.
The histogram above shows the maximum
number of sessions in a week for each player.
The median value is 3. The number of weekly
sessions at the 10th and 90th percentile is 1
and 14 respectively.
The histogram above shows the average
number of sessions in a week for a player. The
median weekly sessions is 2. The number of
sessions at the 10th and 90th percentile is 1
and 7 respectively.
The histogram above shows the maximum
number of monthly sessions for a player. The
median value is 4. The number of sessions at
the 10th and 90th percentile is 1 and 25
respectively.
The histogram above shows the average
number of monthly sessions for a player. The
median value is 3. The number of sessions in
the 10th and 90th percentile is 1 and 18
respectively.
The histogram above shows the average
number of days between sessions for each
player. The median value is 5. The number of
days at the 10th and 90th percentile is 2 and 19
respectively.
The histogram above shows the maximum
number of consecutive days that a player has
played the machines. The median value is 1.
The values at the 10th and 90th percentile are 1
and 4 respectively.
The histogram above shows the shortest time
gap between player sessions. The median
value is 4:22:57 seconds. The values at the
10th and 90th percentile are 00:33:19 seconds
and 9.00:42:20.
The histogram above shows the longest gap
between player sessions. The median value is
11 days. The values at the 10th and 90th
percentile are 0:42:22 hours and 32 days
respectively.
The histogram shows the average gap
between player sessions. The median value is
4 days. The values at the 10th and 90th
percentile are 19:33:43 and 16.15:14:19
respectively.
1. Number of
Sessions
2. Maximum
Daily
Sessions
3. Average
Daily
Sessions
4. Maximum
Weekly
Sessions
5. Average
Weekly
Sessions
16
4
2
6
3
5
2
1
3
2
5
1
1
1
1
1
10
1
1
1
1
1
25
2
1
1
1
1
50
5
2
1
3
2
75
15
4
2
6
4
90
40
8
3
14
7
95
69
11
4
21
10
6. Maximum
Monthly
Sessions
7. Average
Monthly
Sessions
8. Average
Dates Between
Sessions
9. Maximum
Successive
Playing Days
10
8
8
2
4
3
5
1
5
1
1
1
1
10
1
1
2
1
25
1
1
3
1
50
4
3
5
1
75
11
8
10
2
90
25
18
19
4
95
40
29
27
6
Mean
Median
Percentile
Mean
Median
Percentile
10. Shortest Gap
Between Sessions
11. Longest Gap
Between Sessions
12. Average Gap
Between Sessions
Mean
3.10:11:21
14.03:28:17
7.05:38:52
Median
0.04:22:57
10.23:51:19
4.06:28:47
5
0.00:31:20
0.00:02:50
0.07:27:43
10
0.00:33:19
0.00:59:04
0.19:33:43
25
0.00:47:06
3.20:12:04
1.20:50:56
50
0.04:22:57
10.23:51:19
4.06:28:47
75
2.00:42:51
20.14:39:52
8.16:36:28
90
9.00:42:20
32.02:48:56
16.15:14:19
95
18.21:04:09
41.21:43:28
24.12:22:56
Percentile
2) Duration of Play
Aim
To understand how long a player is at a machine for any particular session.
Measurement
The length of a session as defined by the time difference between the timestamp at the
measurement will be seconds, and the start and end of the sessions will be determined
by the proxy sessions.
Outputs
The following variables are calculated for this marker of harm:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
The longest session of play
The mean duration of play
Total amount of play over the period 1-Sept to 30-Nov
Average daily duration of play
Maximum duration of play on a day
Average weekly duration of play
Maximum duration of play in a week
Average monthly duration of play
Maximum duration of play in a given month
Errors
The errors will be similar to proxy sessions: a player may have used all available funds,
then leave the machine to re-load. In this circumstance, a new session would be
identified for the same player.
The length of session is likely to be determined by the amount of money a customer
deposits and the size of any wins experience by the customer.
Results
From the detailed results below for a typical player, we can see that the average
session length is 0:12:53 and the mean of the players longest session is 0:32:30. The
typical playing times over a day, week and month are 0:23:20, 0:32:39 and 0:49:53
respectively. In each of the histograms below the unit of measurement of the horizontal
axis is hours.
The histogram above shows the longest
playing session for each player. The median
The histogram shows the mean session
duration for each player. The median value is
value is 0:32:30. The values at the 10th and
90th percentile are 0:02:15 and 11:01:19
respectively.
0:12:53. The values for the 10th and 90th
percentile are 0:01:30 and 1:08:06
respectively.
This histogram shows the variance of a
ngth. The median value is
0:07:04. The values for the 10th and 90th
percentiles are 0:00:00 and 1:32:04.
This histogram shows the total amount of time
a player has played. The median value is
1:10:26.The values at the 10th and 90th
percentiles are 0:02:48 and 19:14:25.
This histogram shows the average amount of
daily play for each player. The median amount
of time is 0:23:20. The values at the 10th and
90th percentiles are 0:01:40 and 2:11:34.
This histogram shows the maximum amount of
time a player has played on any giving day.
The mean amount of time is 0:42:38. The
values at the 10th and 90th percentiles are
0:02:20 and 11:17:44.
This histogram shows the average amount of
playing time in a weekly period. The median
amount of time is 0:32:39. The values at the
10th and 90th percentiles are 0:01:49 and
4:05:21.
This histogram shows the maximum amount of
time a player has played in any given week.
The median amount of time is 0:50:15. The
values at the 10th and 90th percentiles are
0:02:25 and 12:42:06.
This histogram shows the average amount of
time a player has played over a month. The
median amount of time is 0:49:53. The values
at the 10th and 90th percentiles are 0:02:20 and
9:59:17.
The histogram shows the maximum amount of
time a player has played over a month. The
median amount of time is 1:00:39. The values
at the 10th and 90th percentiles are 0:02:37 and
15:19:44.
1. Longest
Sessions
2. Average
Session
Length
3. Session
Length
Variance
4. Total
Playing Time
5. Average
Daily Playing
Time
Mean
0.02:16:59
0.00:33:07
0.00:32:51
0.07:10:44
0.00:58:02
Median
0.00:32:30
0.00:12:53
0.00:07:04
0.01:10:26
0.00:23:20
5
0.00:00:55
0.00:00:37
0.00:00:00
0.00:01:02
0.00:00:39
10
0.00:02:15
0.00:01:30
0.00:00:00
0.00:02:48
0.00:01:40
25
0.00:09:30
0.00:05:04
0.00:00:11
0.00:14:27
0.00:07:10
50
0.00:32:30
0.00:12:53
0.00:07:04
0.01:10:26
0.00:23:20
75
0.01:31:31
0.00:30:53
0.00:22:35
0.05:27:29
0.00:58:19
90
0.11:01:19
0.01:08:06
0.01:32:04
0.19:14:45
0.02:11:34
95
0.14:23:20
0.01:54:18
0.03:20:35
1.09:29:50
0.03:40:49
Percentile
6. Maximum
Daily Playing
Time
7. Average
Weekly
Playing Time
8. Maximum
Weekly
Playing Time
9. Average
Monthly
Playing Time
10.
Maximum
Monthly
Playing Time
Mean
0.02:42:21
0.01:37:44
0.03:29:43
0.03:31:40
0.05:02:37
Median
0.00:42:38
0.00:32:39
0.00:50:15
0.00:49:53
0.01:00:39
5
0.00:00:56
0.00:00:42
0.00:00:57
0.00:00:53
0.00:01:00
10
0.00:02:20
0.00:01:49
0.00:02:25
0.00:02:20
0.00:02:37
25
0.00:10:55
0.00:08:29
0.00:11:45
0.00:11:12
0.00:13:04
50
0.00:42:38
0.00:32:39
0.00:50:15
0.00:49:53
0.01:00:39
75
0.02:08:44
0.01:35:01
0.02:53:07
0.03:03:05
0.04:07:03
90
0.11:17:44
0.04:05:21
0.12:42:06
0.09:59:17
0.15:19:44
95
0.15:05:15
0.07:04:48
0.17:02:21
0.16:19:40
0.23:14:18
Percentile
3) Net Expenditure
Aim
To understand how much a player is winning or losing on the machine.
Measure
Net expenditure is calculated as the total stake amount minus the total win amount. This
figure will exclude any bonuses credited to the player where such bonuses can be
sufficiently identified.
Output
The following variables are calculated for this marker of harm:
1.
2.
3.
4.
5.
6.
7.
8.
9.
Total net expenditure over the three month time period
Maximum session net expenditure
Average session net expenditure
Daily average net expenditure
Daily maximum net expenditure
Weekly average net expenditure
Weekly maximum net expenditure.
Average monthly net expenditure
Maximum monthly net expenditure
Note daily and weekly figures are only calculated on days and weeks when the player
actually played.
Errors
If a player has had a significant win then this could skew the results, masking potential
big losses before or after the win.
Results
From the detailed results below it can be seen that a typical player has lost £24.33 over
the 3 months. The typical maximum loss for a player in any session is £50.10. Over a
day, week and month, a typical player would lose £8.22, £11.00 and £17.50
respectively.
Note that if the net expenditure amount is negative, this indicates the player has won
money.
This histogram above shows the distribution of
This histogram shows the distribution of the
month period. The median value is £24.33.
The values at the 10th and 90th percentiles are
-£180.00 and £776.09 respectively.
given session. The median value is £50.10.
The values at the 10th and 90th percentiles are
£0.00 and £500.00 respectively.
The histogram above shows the distribution of
xpenditure. The
median value is £5.07. The values at the 10th
and 90th percentiles are -£30.99 and £86.67
respectively.
The histogram above shows the distribution of
The histogram above shows the distribution of
The histogram above shows the distribution of
median value is £50.00. The values at the 10th
and 90th percentile are -£3.75 and £558.50
respectively.
median value is £11.00. The values at the 10th
and 90th percentile are -£77.18 and £236.40
respectively.
The histogram above shows the distribution of
each pl
The median value is £47.60. The values at the
10th and 90th percentiles are £13.40 and £607
respectively.
The histogram above shows the distribution of
median value is £8.22. The values at the 10th
and 90th percentile are -£54.75 and £157.00
respectively.
The median value is £17.50. The values at the
10th and 90th percentiles are -£122.40 and
£441.81 respectively.
The histogram above shows the distribution of
The median value is £39.90. The values at the
10th and 90th percentile are £53.30 and
£695.50 respectively.
1. Total
Expenditure
Mean
3. Average
Session
Expenditure
4. Average
Daily
Expenditure
5. Maximum
Daily
Expenditure
200.91
168.60
16.61
31.31
184.64
24.33
50.10
5.07
8.22
50.00
5
-451.44
-16.60
-95.36
-160.00
-60.00
10
-180.00
0.00
-30.99
-54.75
-3.75
25
-6.50
7.60
-1.67
-2.31
5.00
50
24.33
50.10
5.07
8.22
50.00
75
227.60
200.00
25.00
46.78
217.90
90
776.09
500.00
86.67
157.00
558.50
95
1380.75
792.90
175.00
294.90
900.00
Median
Percentile
2. Maximum
Session
Expenditure
6. Average
Weekly
Expenditure
7. Maximum
Weekly
Expenditure
8. Average
Monthly
Expenditure
9. Maximum
Monthly
Expenditure
Mean
48.73
196.04
101.27
209.62
Median
11.00
47.60
17.50
39.90
5
-214.98
-109.00
-319.80
-222.00
10
-77.18
-13.40
-122.40
-53.30
25
-3.00
4.00
-4.80
1.20
50
11.00
47.60
17.50
39.90
75
74.75
230.00
139.20
237.80
90
236.40
607.00
441.81
695.50
95
417.30
994.20
758.00
1176.78
Percentile
4) Levels of Play Engagement
Aim
The aim is of this metric is to determine how involved a player is with their playing
environment.
Measurement
been combined to measure how engaged each player is when their attributes are
compared to the rest of the player base.
Output
The output from this analysis is a ranking of all the players from the most engaged to the
least engaged.
Errors
Errors in this metric are due to the selection and reliability of the input variables used to
Results
The tables below show snap-shots of the most engaged players ranked from 10,000 to
10,019 and 100,000 and 100,019 respectively. The total number of players used in this
analysis was 244,450. Here we can see that the 10,000th most engaged player had 14
sessions, played 9 difference games, had an average session length of 3.5 hours and
lost in total £137.80. The 100,000th most engaged player had 7 sessions, played only 1
game, had an average session length of 30 minutes and lost just under £400.
Sessions
Max
Monthly
Time
(days)
Average
Session
Length
Average
Session
Stakes
Number of
Games
Played
Player
Loss
10000
14
2.1
3:34:28
206.86
9
137.8
10001
33
1.3
1:00:50
1624.80
15
1075
10002
137
0.9
0:12:49
98.76
31
-1010.15
10003
5
0.7
3:21:13
5697.27
4
180
10004
146
0.6
0:16:14
816.16
23
1223.55
10005
43
0.9
0:42:32
412.53
32
238.18
10006
110
1.1
0:16:53
99.98
31
59.7
10007
227
1.0
0:13:51
443.43
9
5092.7
10008
141
0.8
0:11:35
219.50
29
2203.09
10009
14
0.6
1:00:51
2959.51
28
-916.1
10010
60
0.6
0:38:15
1344.15
29
1215.55
10011
10
1.6
3:46:19
2991.12
1
810
10012
28
0.9
1:23:30
2972.85
11
2439.8
10013
155
0.7
0:15:05
132.60
31
1925.3
10014
97
0.6
0:20:55
242.85
36
3679.85
10015
36
0.9
1:06:30
538.77
28
-173.71
10016
23
0.7
1:09:17
3151.50
17
-2234
10017
92
1.7
0:47:24
196.21
12
663.99
10018
87
0.7
0:12:43
332.67
37
1114.25
10019
81
1.0
0:31:55
219.61
29
1952.4
Sessions
Max
Monthly
Time
(days)
Average
Session
Length
Average
Session
Stakes
Number of
Games
Played
Player
Loss
100000
7
0.1
0:30:03
726.29
1
399.6
100001
12
0.1
0:10:05
118.31
12
-615.65
100002
2
0.0
0:17:11
3710.20
1
-100
100003
4
0.0
0:14:01
1658.45
6
2018.9
100004
6
0.0
0:19:36
1842.97
2
513.7
100005
18
0.1
0:09:21
177.73
9
367.6
100006
16
0.1
0:11:02
161.30
7
232.45
100007
39
0.2
0:07:37
22.60
4
161.55
100008
1
0.0
0:21:24
3556.00
2
-124
100009
4
0.0
0:22:16
307.53
12
149
100010
1
-261
1
0.0
0:20:58
4167.00
100011
8
0.1
0:23:08
375.53
5
-14.6
100012
11
0.1
0:17:38
74.08
10
42.45
100013
1
0.0
0:47:32
2342.60
1
22.4
100014
22
0.2
0:14:12
96.35
4
206.85
100015
1
0.0
0:46:48
763.00
6
-109.5
100016
11
0.1
0:11:16
159.08
12
166.25
100017
10
0.1
0:19:14
27.44
12
105.25
100018
6
0.1
0:35:09
108.80
8
31.5
100019
12
0.2
0:24:16
89.69
4
119.5
5) Number of Activities/Games Types Undertaken
Aim
The aim of this marker is to understand the different range of activities undertaken by
the player. Here, we want to understand if this customer explores different styles of
We also want to understand if there is any increase in the number of activities as a
player becomes more familiar with the machine.
Measurement
The measurement will only be related to the different types of activities taking place
within the context of the Gaming Machine.
Output
The following variables will be calculated for this marker of harm:
1.
2.
3.
4.
5.
6.
7.
8.
9.
The total number of different games played by this customer
The average number of games played by this customer per session
The percentage of bets placed by this player on the most popular game
The maximum number of games played in any one session
The percentage of bets on B2 games
The percentage of bets on B3 games
The number of different stake levels made by the player
The increase in the number of B2 games played. This has been calculated as
the average of the number of games played in the current week, divided by the
average number of games played in the preceding 7 weeks.
10. The increase in the number of B2 bets played, calculated on a weekly basis.
This has been calculated as the proportion of the number of bets placed in the
last 7 weeks of the data set compared to the total number of bets in the entire
14 weeks.
Errors
The errors associated with this marker are due to staking levels, which may change
within a session due to player wins or losses.
Results
From the detailed results below, a typical player engages with 3 different games, but
usually only one game in a session. Over 70% of the bets placed will be on the player
favourite game. A majority of the bets (87%) are placed on B2 games.
This histogram shows the distribution of the
number of different games played by each
player. The median value is 3. The values at
the 10th and 90th percentiles are 1 and 17
respectively.
The histogram shows the average number of
games played in a session by each player.
The median value is 1. The values at the 10th
and 90th percentiles are 1 and 3 respectively.
This histogram shows the proportion of bets
onal favourite game.
The median value is 70%. The values at the
10th and 90th percentiles are 30% and 100%
respectively.
This histogram shows maximum number of
different games played by a player in any one
session. The median value is 2 games. The
values at the 10th and 90th percentiles are 1
and 7 respectively.
The histogram above shows the distribution of
the proportion of bets that the player makes on
B2 games. The median value is 87%. The
values at the 10th and 90th percentiles are
0.4% and 100% respectively.
The histogram above shows the distribution of
the proportion of best that a player makes on
B3 games. The median value is 0%. The
values at the 10th and 90th percentiles are 0%
and 96% respectively.
This histogram shows the distribution of the
number of different stake values bet by each
player. The median value is 15. The values at
the 10th and 90th percentiles are 1 and 118
respectively. This value at the 90th percentile
was higher than expected, but this is due to
players being able to make multiple bets
This histogram shows the distribution of the
number of different stake values the player has
chosen for their first bet. The median value is
3. The values at the 10th and 90th percentile
are 1 and 14 respectively.
This histogram shows the distribution over the
rate at which players are increasing the
amount of B2 games they play. The median
rate is 0.13. The values at the 10th and 90th
percentile are 0 and 2 respectively.
This histogram shows the distribution over the
proportion of B2 bets placed in the 2nd half of
the data provided. The median value is 37.2%.
The values at the 10th and 90th percentiles are
0% and 100% respectively.
1. Number
of Unique
Games
2. Average
Unique
Session
Games
3.
Percentage
of Favourite
Game
4. Maximum
Unique
Session
Games
5.
Percentage
of B2 Game
Bets
Mean
7
2
69%
3
63.8%
Median
3
1
70%
2
86.9%
5
1
1
23%
1
0.0%
10
1
1
30%
1
0.4%
25
1
1
45%
1
19.7%
50
3
1
70%
2
86.9%
75
8
2
100%
4
100.0%
90
17
3
100%
7
100.0%
95
25
4
100%
10
100.0%
6.
Percentage
of B3 Game
Bets
7. Unique
Stake
Values per
Session
8. Unique
First Stake
Values per
Session
9. Increase
in B2
Games
10. Increase
in B2 Bets
23.2%
42
6
0.73
45.0%
0.0%
15
3
0.13
37.2%
5
0.0%
1
1
0.00
0.0%
10
0.0%
2
1
0.00
0.0%
25
0.0%
4
1
0.00
0.0%
50
0.0%
15
3
0.13
37.2%
75
42.6%
50
7
1.00
100.0%
90
96.0%
118
14
2.00
100.0%
95
100.0%
178
20
3.00
100.0%
Percentile
Mean
Median
Percentile
6) Chasing
Aim
The aim of this marker is to identify sessions where the intent of the player is to win back
the money that was lost in the previous session. Within this marker, player reloading of
the Gaming Machine within an ongoing session is not analysed.
Measurement
As the emotional state of the player cannot be inferred from the data, this marker is
more challenging to measure. From the data, we can only measure correlations
between the outcome of one session, to the time until the subsequent session and any
actions measured in that session.
Output
The following variables will be calculated for this marker of harm:
1.
2.
3.
4.
The number of sessions where the player lost money
The percentage of sessions where the player lost money
Impact of the initial deposit value on winning and losing sessions. This output
has been calculated as the median initial deposit in the sessions following a
session where the player made a profit, minus the median initial deposit in the
sessions following a session where the player made a loss. A value greater
than 0 indicates that the player typically deposits more after a winning session.
Impact of the time between sessions after winning and losing sessions. This is
the median time between a winning session and beginning the next session,
minus the median time between a losing session and beginning the next
session. A value greater than 0 indicates the player typically returns for a new
session sooner if they lost money in their last session.
Errors
There are no specific errors associated with the variables that have been calculated for
this marker. However, we believe further analysis may provide more insight into this
particular marker.
Results
From the detailed results below, a typical player has had 3 losing sessions during the 3
month period which corresponds to 67% of their sessions. Using the data to uncover
signs of chasing between sessions has proven difficult. Metric 3 indicates that the
amount won or lost
-in. Metric 4 shows
that after a losing session, a player is more likely to start the next session 2 minutes
later. Neither of these results is strong enough to show a particular correlation between
winning and losing sessions and the behaviours observed in the next sessions.
The figure shown at the end of this section shows the relationship between the amount
lost by a player in one session (on the horizontal axis), and the amount deposited by the
player on the next session (on the vertical axis). Interestingly, here we can see that a
This histogram shows the distribution of the
number of losing sessions experienced by a
player. The median value is 3. The values at
the 10th and 90th percentiles are 0 and 26
respectively.
This histogram shows the distribution of the
proportion of losing sessions that a player
experiences. The median value is 67%. The
values at the 25th and 75th percentiles are 50%
and 100% respectively.
This histogram shows the distribution of the
This histogram shows the distribution in the
change in time between sessions after a
winning or losing session. The median value is
0:01:53. The values at the 10th and 90th
percentile are -3.19:01:20 and 4:04:24:31
respectively.
winning or losing session. The median value is
£0.00. The values at the 10th and 90th
percentile are -£10.00 and £10.00
respectively.
1. Number of
Losing
Sessions
2. Percentage
of Losing
Sessions
3. Initial Deposit
Difference
Between
Winning and
Losing
10.4
64%
1.5
0.03:56:42
3.0
67%
0.0
-0.00:01:53
5
0.0
0%
-16.0
-7.10:12:24
10
0.0
0%
-10.0
-3.19:01:20
25
1.0
50%
-2.8
-0.18:56:30
50
3.0
67%
0.0
-0.00:01:53
75
10.0
100%
3.9
0.18:12:39
90
26.0
100%
10.0
4.04:24:31
95
45.0
100%
20.0
8.22:21:48
Mean
Median
Percentile
4. Session Gap
Difference
Between
Winning and
Losing
Within Session Metrics
1) Debit Card Payment Reloading and Switching
Aim
The aim of this marker is to understand if a player uses a debit card as a secondary
payment method within their gaming session.
Measurement
The measurement is only applied to sessions where the first cash-in is a non-debit card,
and at least one subsequent sequence of cash-ins is associated with a debit card.
Output
The following variables are calculated for this marker of harm:
1.
2.
3.
4.
If the session initiated by a debit card transaction
The number of debit-card transactions in the session where at least one debit
card transaction took place
Total value of the debit card cash-ins in the session
Total value of other cash-ins in the session
The following metrics are only applied to sessions where the first cash-in sequence
does not include a debit card and at least one later sequence of cash-ins uses a debit
card. A sequence is defined by one or more cashany bet events.
5.
6.
7.
If the session initiated by a non-debit card transaction but a subsequent debit
card transaction occurred
If the player started the session with cash (or voucher), the number of
subsequent cash-in events which are associated by a debit card
The value of the initial cash-in sequence
Errors
It is difficult to map all debit card payments to amounts transferred to Gaming
Machines. For example, a player may withdraw £100 from their debit card, place a
sports bet for £20 and transfer £80 to a Gaming Machine. Alternatively, the player may
have £50 cash and transfer this with the £100 withdrawn from the debit card on to the
Gaming Machine. In this instance, the data only indicates that there is a £150 transfer.
Results
Due to the time constraints required to generate this report,
has been used to generate the first 6 variables. Due to the confidential nature of this
non-aggregated data, these results have been removed from the report. The 7 th variable
has been calculated across all of the operators. From the results, we could identify that
2% of sessions involved the use of a debit card.
This histogram shows the distribution of the
initial amount cashed into a session. The
median value is £8.00. The values at the 10th
and 90th percentiles are £1.00 and £20.00
respectively.
7. Initial Cash In Amount
Mean
13.58
Median
Percentile
8
5
1
10
1
25
2.4
50
8
75
20
90
20
95
40
2) Debit Card Payment Decline
Aim
The aim of this marker is to identify if a player has exhausted his/her bank balance.
Measurement
This maker could be calculated from the debit card transaction result returned from the
payment terminals.
Output
Unfortunately, due to the time restrictions of this project and data availabi
possible to explore this marker of harm.
3) Variability In Staking Behaviour
Aim
The aim of this marker is to understand how a player s staking changes within a
session; in particular, are changes in the stake value occurring due to a win (because
more money is available to play with), or do changes begin to increase after a period of
no wins?
Measurement
this changes under different scenarios which take place during the session.
Output
The following variables will be calculated for this marker of harm:
1.
2.
3.
4.
5.
6.
7.
level before increasing?)
The number of wins in the session
The total amount won in the session
The number of bets in the session
The total amount bet in the session
The average ratio of the stake size after and before a win (e.g. for each win,
calculate the post-stake value divided by the pre-stake value and average the
amounts over the session)
Errors
The impact of a player winning may influence the level at which they are comfortable
staking.
Results
The results below show that, for a typical player, the median total value from the 8 bets
was £29. Typically 3 of the bets will result in a win, but there has been little evidence to
suggest that a win results in an increase of stake value. The typical standard deviation
is £0.81, which is a low amount of variation.
This histogram shows the distribution in the
variance of stakes over all of the sessions. The
median value is 0.67 pounds squared. The
values at the 10th and the 90th percentiles are
0.00 and 8.28 pounds squared respectively.
This histogram shows the distribution over how
a playe
sessions. Values above 1 indicate that the
staking level increased. The median value is 1.
The value at the 10th and 90th percentiles are
0.611 and 2 retrospectively.
This histogram shows the distribution of the
number of winning bets that a player has over
a session. The median value is 3. The values
at the 10th and 90th percentiles are 0 and 26
respectively.
This histogram shows the distribution of total
winnings across the sessions. The median
value is £20. The values at the 10th and 90th
percentiles are £0.00 and £391.50
respectively.
The histogram above shows the distribution of
the number of bets played across the
sessions. The median value is 8. The values at
the 10th and 90th percentiles are 1 and 86
respectively.
The histogram above shows the distribution of
the total amount bet in the sessions. Note that
this variable will be different to the total
amount cashed in by the player, as it will
include amounts re-staked from previous wins
in the session. The median value is £29.00.
The values at the 10th and 90th percentile are
£1.55 and £400.00 respectively.
The histogram above shows the distribution of
the ratio in stake before and after a win. If the
ratio is greater than 1 then the player has
increased their stake size. The median value
for this variable is 1. The values at the 10th and
90th percentile are 0 and 1.35 respectively.
1. Player's
Stake
Variance
2. Stake
Value
Gradient
3. Number
of Winning
Sessions
4. Amount
won in
Winning
Sessions
5. Number
of Bets
Placed in
Sessions
Mean
2.89
2.05
10.53
186.81
37.26
Median
0.67
1
3
20
8
5
0.00
0
0
0
1
10
0.00
0
0
0
1
25
0.00
0
0
0
2
50
0.67
1
3
20
8
75
3.25
2
10
111.6
28
90
8.28
5
26
391.5
86
95
13.56
9
44
783.7265
164
Percentile
6. Amount Bet in
Sessions
Mean
193.74
1.86
29
1
5
1
0.11
10
1.55
0.611
25
6.2
1
Median
Percentile
7. Stake Value Ratio
Before/After Win
50
29
1
75
120.4
1.11
90
400
2
95
792.8
3.43
4) Use of Autoplay
Aim
Gaming Machine.
Measurement
As there is no autoplay feature on the Gaming Machines, we instead examined the gap
between bets to identify if the customer is playing as fast as the machine will allow.
any choices about the bet about to be made. Data
was not available in the supplied data set.
Output
The following variables will be calculated for this marker of harm:
1.
2.
3.
The number of gaps between B2 bets which are at the 20 second legal limit
The percentage of gaps between B2 bets which are at the 20 second legal
limit
The average gap between bets on B2 games
Errors
Note that the values calculated below only include games from machines supplied by
Scientific Games. Inspired Gaming machines data was not used, due to the way in
which it had been transformed for the preliminary analysis.
Results
The results below show that a majority of sessions on B2 games are longer than the
legal limit of 20 seconds. The typical gap between bets is 31.7 seconds. Approximately
10% of all sessions contain bets which are only staked at the minimum legal gap,
however we observe from the first variable that a significant majority of these sessions
have a small number of bets.
This histogram shows the number of bets at
the legal 20 second B2 limit. The median value
is 0. The values at the 10th and 90th percentiles
are 0 and 15 bets.
This histogram shows the percentage of bets
within a session which are at the legal limit.
The median value is 40%. The values at the
10th and 90th percentiles are 10% and 100%
respectively.
This histogram shows the distribution of the
average gap between B2 games within a
session. The median value is 31.7 seconds.
The values at the 10th and 90th percentiles are
22.7 seconds and 53.8 seconds.
1. Number of B2 Bets
at 20 Second Limit
2. Percentage of B2
Bets at 20 Second
Limit
3. Average Gap
Between B2 Bets
(Seconds)
8.64
0.48
35.4
0
0.40
31.7
5
0
0.07
21.5
10
0
0.10
22.7
25
0
0.20
25.8
50
0
0.40
31.7
75
2
0.78
41.1
90
15
1.00
53.8
95
39
1.00
63.2
Mean
Median
Percentile
5) Play of Multiple Machines Simultaneously
Aim
The aim of this marker is to understand if a player is using multiple machines at the
same time.
Measurement
It is very difficult to measure this marker from the data, as it is challenging to accurately
identify a player from their data. It may be possible to examine machines which are
located in proximity to each other that have sessions that start at roughly the same time,
where the same game is played with the same staking levels and there is a correlation
in the play between the two machines (e.g., wherein the play on one machine is slightly
delayed when compared to another in its proximity). It may also be possible to look at
debit card transactions which have been split and then transferred to two machines.
Output
No outputs are calculated for this metric due to the difficulties associated with
measurement.
6) Stake Size
Aim
The aim is to identify the value of the stakes in a session.
Measurement
The measurement is based on the stake value.
Output
The following variables will be calculated for this marker of harm:
1.
2.
3.
4.
5.
Minimum stake size of that session
Number of bets at the minimum stake size
Maximum stake size of that session
Number of bets at the maximum stake size
Proportion of stakes at each betting level
Errors
There are no errors associated with the measurement of this metric.
Results
The results below show that a typical player will bet at two different stake values in a
session. The average amount is £3.53. The median minimum amount is £1.80 and the
median maximum amount is £5.40. A player will typically place 16 bets at the lowest
value in that session.
The histogram above shows the distribution of
the minimum stake value in each session. The
median value is £1.80. The values at the 10th
and 90th percentile are 20p and £10.00
respectively.
The histogram above shows the distribution of
the number of bets at the minimum stake
amount for that session. The median value is
16. The values at the 10th and 90th percentile
are 3 and 120 respectively.
The histogram above shows the distribution of
the maximum stake amount across the
sessions. The median value is £5.40. The
values at the 10th and 90th percentile are 50p
and £37.60 respectively.
The histogram above shows the distribution of
the number of best at the maximum B2 stake
level (£100). The median value is 0.
The histogram above shows the distribution of
the average stake value across the sessions.
The median value is £3.53. The values at the
10th and 90th percentiles are £0.50 and £21.18
respectively.
1. Minimum
Bet Amount
in Session
2. Number
of Bets at
Minimum
Session
Amount
3. Maximum
Bet Amount
In Session
4. Number
of Bets at
Maximum
Session
Amount
5. Average
Bet Value in
Session
4.45
52.83
13.89
3.11
8.57
1.8
16
5.4
0
3.53
5
0.2
2
0.25
0
0.24
10
0.2
3
0.5
0
0.50
Mean
Median
Percentile
25
1
6
1.8
0
1.05
50
1.8
16
5.4
0
3.53
75
5
46
18
0
10.00
90
10
120
37.6
0
21.18
95
20
215
60
1
34.00
7) Game Volatility
Aim
characteristics. A low volatile game is defined as one with frequent small winnings
whilst a high volatile game has infrequent large winnings.
Measurement
The measurement examines the proportion of switching between games with different
levels of volatility.
Output
The following variables will be calculated for this marker of harm:
1.
2.
3.
4.
5.
6.
7.
8.
Number of bets on low volatile games
Proportion of bets on low volatile games
Number of bets on high volatile games
Proportion of bets on high volatile games
Number of changes from a low volatile game to a high volatile game
Number of changes from a low volatile game to another low volatile game
Number of changes from a high volatile game to a low volatile game
Number of changes from a high volatile game to another high volatile game
Errors
The behaviours associated with variables calculated for this metric may not be due to a
conscious decision of the player. For example, changes observed may be due to
game has either a high or low volatility.
Results
From the results below, we can see that a majority of players place most of their bets on
low volatility games (frequent small wins). Approximately 67% of sessions contain bets
on only low volatility games. The table below shows the average number of times a
player changes games to the same or different volatilities. From this table, we can see
that players are more likely to keep playing games with the same level of volatility.
To Low Volatile
Game
From Low Volatile Game
From High Volatile Game
To High Volatile Game
0.11
0.08
0.07
0.22
This histogram shows the distribution
across sessions of the number of stakes
on low volatile games. The median value
is 3. The values at the 10th and 90th
percentile are 0 and 28 respectively.
This histogram shows the distribution
across the sessions of the proportion of
bets on low volatile games. Approximately
67% of sessions are played on only low
volatile games.
This histogram shows the distribution
across sessions of the number of bets on
high volatile games. The median value is
0. The values at the 10th and 90th
percentile are 0 and 41 respectively.
This histogram shows the distribution
across the sessions of the proportion of
bets on high volatile games.
Approximately 7% of sessions are played
on only high volatile games.
Mean
Median
Percentile
1. Number of
Bets on Low
Volatile Games
2. Proportion of
Bets on Low
Volatile Games
3. Number of
Bets on High
Volatile Games
4. Proportion of
Bets on High
Volatile Games
11.58
0.69
19.15
0.20
3
1
0
0
5
0
0
0
0
10
0
0
0
0
25
1
0.01
0
0
50
3
1
0
0
75
11
1
0
0
90
28
1
41
1
95
47
1
104
1
6. Switch Between
Low Volatile Games
7. Switch from a
High to a Low
Volatile Game
8. Switch Between
High Volatile Games
0.11
0.08
0.22
0
0
0
5
0
0
0
10
0
0
0
25
0
0
0
50
0
0
0
75
0
0
0
90
0
0
0
95
1
1
1
Mean
Median
Percentile
8) Way Game Played (e.g. number of bets per stake)
Aim
The aim is to examine the different ways a session can be characterised.
Measurement
For this marker, we have examined some of the key components of how a session can
be described, such as its length, number of bets and games played, money spent and
net position of the player.
Output
The following variables will be calculated for this marker of harm:
1.
2.
3.
4.
5.
6.
7.
The session length
Number of times stake value increases
Number of times stake value decreases
Number of different games played
Number of different game types played (e.g., collating all of the roulette game
types together)
Total amount cashed in during the session
Net position of the session (e.g., total stake minus total win)
Errors
Any errors associated with the variables calculated for this marker will be associated
with the accuracy of the proxy session calculation.
Results
From the detailed results below, the typical session is 0:03:52 long with the player
cashing in £12.30 and losing £3.50. A player is more likely to decrease than increase
their stake. The player is most likely to play on a single game.
This histogram shows the distribution across
the session lengths. The median session is
0:03:58 long. The values at the 10th and 90th
percentile are 0:00:26 and 0:25:54.
This histogram shows the distribution of the
number of times a player increases the
amount staked across the session. The
median value is 0. The values at the 10th and
90th percentile are 0 and 66 respectively.
This histogram shows the distribution of the
number of times a player decreases the
amount staked across the session. The
median value is 1. The values at the 10th and
90th percentile are 0 and 10 respectively.
The histogram shows the distribution of the
number of different games played across the
sessions. The median value is 1. The values at
the 10th and 90th percentiles are 1 and 2
respectively.
The histogram shows the distribution of the
number of types of games played in a session.
The median value is 1. Only in the 95th
percentile does the value increase to 2.
This histogram shows the distribution of the
amount cashed into each session. The median
value is £12.30. The values at the 10th and 90th
percentile are £1.50 and £100.00 respectively.
This histogram shows the distribution of the
net position across the sessions. A negative
value indicates a win for the player. The
median value is £3.50. The values at the 10th
and 90th percentiles are -£60.00 and £51.50
respectively.
1. Session
Length
(Minutes)
2. Number
of Stake
Value
Increases
3. Number
of Stake
Value
Decreases
4.
Unique
Games
Played in
Session
5.
Unique
Game
Types
Played in
Session
Mean
0:17:50
27.08
3.63
1.34
1.07
Median
0:03:58
0
1
1
1
5
0:00:12
0
0
1
1
10
0:00:26
0
0
1
1
25
0:01:16
0
0
1
1
50
0:03:58
0
1
1
1
75
0:10:52
8
4
1
1
90
0:25:54
66
10
2
1
95
0:45:53
145
16
3
2
Percentile
6. Total Session Cash
In Amount
7. Net Position of
Session
Mean
43.45
-9.75
Median
12.30
3.50
5
1.00
-161.40
10
1.50
-60.00
25
5.00
-5.25
50
12.30
3.50
75
40.00
20.00
90
100.00
51.50
95
173.60
100.00
Percentile
9) Cash-Out
Aim
The aim is to understand whether players will stop playing when they win, or play to a
zero balance.
Measurement
To calculate this ma
to cash out and then identified some of the key characteristics of this decision.
Output
The following variables will be calculated for this marker of harm:
1.
2.
3.
4.
If the player cashed out, the return generated on their initial winnings
If the player cashed out, the total amount cashed out
The number of cash-out sequences
When a player cashed out, the proportion of the balance that was cashed out
(e.g., this will be less than 100% when players continue to play after winning)
Errors
The player may cash out on one particular machine, and then cash the same money
back into another machine.
Results
From the detailed results below, we can see that approximately 25% of sessions result
in a player cashing out. When a player cashes out, they are typically taking out £40.00,
or a return of 178% of their original cash-in. Also, in over 90% of cash-out sessions the
customer is withdrawing their entire balance.
This histogram shows the distribution of the
return that players have achieved based on
the percentage of the total amount cashed in.
This metric is only calculated for sessions
where the player cashed out. The median
value is 178%. The values at the 10th and 90th
percentile are 37% and 875% respectively.
This histogram shows the distribution of the
total amount cashed out by players in sessions
were at least one cash out was recorded. The
median value is £40.00. The values at the 10th
and 90th percentile are £4.80 and £316.80
This histogram shows the distribution over the
number of cash-out sequences made by the
player. If the value is greater than 1, it
indicates the player withdrew some winnings,
played for a bit longer, and then withdrew
again. The median value for is 0. At the 75th
and 95th percentile the values are 1 and 2
respectively.
This histogram examines the proportion of the
available balance that a player decides to
cash-out. The median value is 100%. At the 5th
and 10th percentile the values are 93% and
100% respectively.
1. Player
Percentage
Return
2. Value
Cashed Out
3. Number of
Cash Out
Sequences
4. Proportion of
Available
Balance
Cashed Out
Mean
9.87
125.65
0.53
0.99
Median
1.78
40.00
0
1.00
5
0.09
0.80
0
0.93
10
0.37
4.80
0
1.00
25
1.00
12.00
0
1.00
50
1.78
40.00
0
1.00
75
3.72
108.00
1
1.00
90
8.75
316.80
1
1.00
95
17.63
550.00
2
1.00
Percentile
Appendix C Representativeness of
Loyalty Card Data
As this research programme has focused on the analysis of loyalty card players, it is
useful to understand the representativeness of this dataset. To examine the
representativeness we calculated 8 within session metrics for the registered players
and for the entire data set. The histograms for each of these calculations are shown in
the Figures below. The red columns represent metrics from registered players and the
blue columns represent metrics from the entire data set.
Visually, in these figures it can be observed that the registered players are overa metric based on the Kolmogorov-Smirnov test. This test essentially provides a metric
calculating the degree of difference between two statistical distributions. The results of
applying this test are shown in Table 13. A higher value in this table indicates a poor
correspondence between the two distributions.
In general, there is a reasonable fit between the registered sessions and the entire
population, the main exception being the length of session and amount cashed in. The
registered sessions are biased to longer sessions with higher amounts of money
cashed in. This is likely due to registered players being more engaged players, and
also to the fact that players may only insert their card when they plan to have a longer
session on the Gaming Machine.
Variable
Modified KolmogorovSmirnov Value
Number of winning sessions
0.113
Amount won
0.118
Number of pets placed
0.118
Total amount bet in session
0.071
Average bet value in session
0.102
Session length
0.281
Total session cash in
0.231
Net position of session
0.102
Table 13 - Measurement of the representativeness of registered sessions
The histogram above shows the distribution of the number of winning bets in a session.
All sessions are represented by blue and registered sessions by red. The modified
Kolmogorov-Smirnov result is 0.113.
The histogram above shows the distribution of the total winnings across the sessions.
All sessions are represented by blue and registered sessions by red. The modified
Kolmogorov-Smirnov result is 0.072.
The histogram above shows the distribution of the number of bets placed across the
sessions. All sessions are represented by blue and registered sessions by red. The
modified Kolmogorov-Smirnov result is 0.118.
The histogram above shows the distribution of the total amount bet across the sessions.
All sessions are represented by blue and registered sessions by red. The modified
Kolmogorov-Smirnov result is 0.071.
The histogram above shows the distribution of the average stake across the sessions.
All sessions are represented by blue and registered sessions by red. The modified
Kolmogorov-Smirnov result is 0.102.
The histogram above shows the distribution of session lengths. All sessions are
represented by blue and registered sessions by red. The modified Kolmogorov-Smirnov
result is 0.281.
The histogram above shows the distribution of amounts cashed into each session. All
sessions are represented by blue and registered sessions by red. The modified
Kolmogorov-Smirnov result is 0.213.
The histogram above shows the distribution of the
net position across each
session. All sessions are represented by blue and registered sessions by red. The
modified Kolmogorov-Smirnov result is 0.102.
Appendix D Candidate Predictive
Modelling Approaches Explored by RTI
algorithms utilised by RTI when exploring the ability to distinguish between problem and
non-problem gamblers.
M ain c a n did a te pre dic tiv e mo d el . Our baseline model is a logistic step-wise
regression model which can capture both: main effects and heterogeneity based on the
interactions. Higher order interactions were identified by using classification trees
[Hastie and Tibshirani, 2009]. These interactions were added to the regression model to
provide possible improvement. Thus, our basic model has a form:
𝑌~𝛽0 + ∑ 𝛽1𝑖 𝑋𝑖 + ∑ 𝛾𝑖𝑗 𝑋𝑗 𝑋𝑖 ,
𝑖
𝑖𝑗
where Y indicates the outcome, X i corresponds to a set of influential predictor variable,
coefficients beta and gamma correspond to the main effects and the interactions. In
addition to this simpler model we used advanced methods that include classification
tree ensembles, random forests, artificial neural networks (ANN), and support vector
machines (SVMs). Below is a brief description of methodology that we have used.
C la ssific a tion Tre e E ns e mble s a nd R a n dom F orests: Classification trees recursively
partition subjects into groups in such a manner that groups are as internally
homogeneous as possible, while cross-group heterogeneity is maximized. Splits are
made in order of decreasing statistical significance, i.e., beginning with the most
significant split. The predicted value for an individual is calculated as the proportion
with the outcome for the subjects within its terminal node. In order to assess the
stability, the procedure is repeated many times on bootstrapped samples, thus forming
outcomes involves the aggregation (e.g., using means) of predicted values over the
ensemble. Random forests extend tree ensembles to incorporate randomization in the
node splitting process, when only a random subset of weak predictors is allowed to
enter the model. This additional use of randomization allows the model to incorporate
useful, but weaker, predictors that otherwise would be masked by stronger predictors.
The size of the ensembles and forests will be in the range of 400-1,000 trees.
Random forests also allow one to rank the variables according to their impact on
prediction. Each variable was randomly reshuffled one at a time and the variables were
ranked according to the loss of prediction power. The more loss in prediction a
scrambled variable incurred the more influential it was.
Artificial N eural N e tworks (A N N s): We will consider an application of non-parametric
methods such as artificial neural networks. ANN algorithms resemble a network of
interconnecting functions (hidden components) where an output of one or several
components becomes an input into another component. Although the structure of each
component is clearly defined with functional form usually resembling a sigmoid, the
entire neural network can become very complex and thus ANNs are often referred to as
other methods, interpretability is rarely possible.
Su p p ort Ve c tor M a chine s: Support vector machine analysis for a dichotomous
outcome, which endeavors to correctly separate subject data into proper groups based
on covariate information, is similar in concept to linear discrimination. Use of support
vector machine methodology to build predictive models is described in Hastie et al.
Similar to classification tree ensembles and random forests, all potential predictors are
considered simultaneously in the model building process.
Appendix E Example of transformed
between session variables.
variables and the range of values that are exhibited by both problem and non-problem
gamblers after transformation. Each of these figures show that there is a high-degree of
overlap between the problem and non-problem gamblers, illustrating the challenge of
distinguish these players.
To be able to distinguish these players we are relying the predictive algorithms to
identify combinations of these variables that when considered together, provide a high
degree of predictive power.
Log of the maximum amount of total session time
on a given day
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
-0.19
0.19
0.58
0.97
1.35
1.74
2.12
2.51
2.90
3.28
3.67
4.06
4.44
4.83
5.21
5.60
5.99
6.37
6.76
7.15
7.53
7.92
8.31
8.69
9.08
9.46
9.85
10.24
10.62
11.01
11.40
11.78
0
Non-Problem Gambler
Problem Gambler
Figure 30 - The range of values taken of for the log transformed maximum amount
of total session time on a given day. Shown for problem and non-problem
gamblers.
Log of the maximum amount lost on a given day
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
-0.20
0.20
0.61
1.02
1.43
1.84
2.25
2.66
3.07
3.48
3.89
4.30
4.71
5.12
5.53
5.94
6.35
6.76
7.17
7.58
7.99
8.40
8.81
9.22
9.63
10.04
10.45
10.86
11.27
11.68
12.09
12.50
0
Non-Problem Gambler
Problem Gambler
Figure 31 - The range of values taken of for the log transformed maximum amount
of lost on a given day. Shown for problem and non-problem gamblers.
Log of the maximum amount cashed in on a given
day
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
-0.22
0.22
0.65
1.08
1.51
1.94
2.37
2.81
3.24
3.67
4.10
4.53
4.96
5.39
5.83
6.26
6.69
7.12
7.55
7.98
8.42
8.85
9.28
9.71
10.14
10.57
11.00
11.44
11.87
12.30
12.73
13.16
0
Non-Problem Gambler
Problem Gambler
Figure 32 The range of values taken of for the log transformed maximum amount
cashed in on given day. Shown for problem and non-problem gamblers.