Sports Book Insights from Analytics

Submitted by:
Andrew Pearson
Managing Director
Qualex Asia Limited
Phone: (853) 6265-5885
Andrew.pearson@qlx.com
www.qlx.com
Sports book Case Study
Qualex Asia Limited
August 15, 2014
The material contained in this document and any material or information disclosed during discussions of the document represents
proprietary, confidential information pertaining to Qualex Asia Limited solutions, products and methods. By accepting this document,
QlikView agrees that the information in this document shall not be disclosed outside of QlikView and shall not be duplicated, used, or
disclosed for any purpose other than to evaluate this document. If however, a contract is awarded to Qualex Asia Limited as a result of this
document, or in conjunction with, the submission of this information, QlikView will have the right to duplicate, use or disclose the material
contained herein to the extent provided for in the resulting contract. This document is subject to a mutually approved agreement or contract
specifying full terms and conditions.
Qualex Asia Limited and all other Qualex product or service names are registered trademarks or trademarks of Qualex Asia Limited or
Qualex Consulting Services, Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are
trademarks of their respective companies. Copyright © Qualex Asia Limited, Macau and Qualex Consulting Services, Inc. All rights
reserved.

1
Table of Contents
1.1 Executive summary 1
1.1.1.1 QlikView 1
1.1.1.2 Predictive Analytics Sports Book Case Study 9
1.2 About Qualex Asia Limited 13
1.3 Index of Figure and Tables 13

1
1.1 Executive summary
This document presents a use case for the QlikView and SAP InfiniteInsights product. It was prepared by Andrew
Pearson of Qualex Asia Limited.
1.1.1.1 QlikView
To highlight the application of QlikView and SAP’s InfiniteInsights with a real source of data, Qualex has done an
analysis using transactional information from a boutique global sports book predominantly accepting wagers via
the internet. The majority of their business is on soccer and American team sports. The raw data set contains
approximately four (4) years of single wagers (i.e. no multi/accumulator bets) comprising some 2.2M unique
transactions across almost 300K unique events and 37.5K unique customers. The total turnover is $4.2B (AUD).
The initial piece of analysis done was a customer segmentation model using SAP InfiniteInsight on various
derived metrics reflective of a customer’s behavior. A ten (10) cluster solution was derived with cluster
membership defined as follows:
1. Good segment for the book: recent, bet a lot on many sports and lose.
2. Lost customers: haven’t bet for a while, and don’t bet big but bet with variety.
3. Small punters who specialize and win.
4. Hit and run customers who have spent their cash and “put the cue in the rack.”
5. Segment to encourage and cross-sell; current but small, lose often.
6. Big punters who have churned, encourage returning with incentives? Bet on many sports.
7. Mid-size punters who haven’t been seen in a while. Good for the joint.
8. Troublesome segment: bet small, specialize and significant winners.
9. Similar to cluster 8: but much bigger in volume and variety. Strong winning segment.
10. Small punters who may be shopping around; losing punters overall.
The approach of our demo is to highlight the bridge between using analytics to derive knowledge from the
information available that goes beyond merely slicing and dicing the data. The following screenshots (Figures 1 -
3) and descriptions show how QlikView can be used to present graphical representations of data that will provide
meaningful insight to a business user.
Figure 1: QlikView Dashboard Showing Customer Segmentation Clusters Graphed to Reveal Win-loss by
Segment and Type of Bet

Qualex Asia Limited
Qualex Asia Limited Commercial in Confidence
2
Figure 2: QlikView Dashboard showing BetResult by Cluster and BetType HeatMap
Figure 3: QlikView Dashboard showing Conv_Stake and BetType HeatMap
Figure 4 shows a map that reveals bet results by Bettype, League Name and Cluster. The heat map clearly
shows that Cluster 8 and Cluster 9 on the Asian Handicap are big money losers (positive result indicates
customer win) and these clusters and bets should be addressed immediately by minimizing the risk associated
with these clusters via limiting the number or size of bets being taken or adjusting the betting lines (these results
could also align with the time dimension introduced above (so perhaps markets are being put up too early)).
Furthermore, the sports book could look at leveraging this insight to help shape their risk management approach.
Knowledge from these dashboards can drive actionable intelligence for the sports book; for example, there
may be a directive from senior management to have markets up early to influence customers to bet early. If a
dimension was added that represented the time before kick-off a bet was placed, it may be evident that this drive
to be up early was seeing the book suffer from inefficient and volatile markets. Armed with this kind of
information, traders could inform senior management that profitability is being severely affected by opening up
markets too early and the strategy could be changed. The location of where the bets were being placed could be

Qualex Asia Limited
3
added to the data set (and, therefore, the drilldown) so a sports book could actually see where the bets on the
Italian Serie A games are being made and, should they be in hotbeds of match-mixing countries like Russia,
Singapore or Malaysia, the sports book would have the ability to minimize exposure on games going forward.
This dimension would also allow the sports book to know whether people were likely to be more successful
wagering in their own backyard or elsewhere.
Tying this in further with the dashboards presented above, the sports books risk department could minimize
the stake of customers in Cluster 8 who are betting on UK and Italian football matches. Whilst the marketing
department could look at increasing the turnover of these customers on French football via strategic marketing
offers with regard to games in this jurisdiction.
Figure 4: QlikView Dashboard: Scatter Plot Chart Revealing Customer Stake and Bet Result
With QlikView, business users can easily build ROI charts that will reveal detailed customer data. Figure 13
reveals that one particular gambler was responsible for about $21M in turnover that resulted in a $3.4M profit
and this gambler causes the scale of the chart to be skewed by his inclusion. It is very easy in QlikView to filter
out points to facilitate more meaningful insight.
One powerful feature of QlikView is the ability to derive calculated measures within the interface.
Furthermore, data manipulation is extremely easy, with the ability to group levels of a categorical variable into
more meaningful groups. The following chart displays the derived measure of ROI (called base line result), which
is bet results divided by stake.

Qualex Asia Limited
4
Figure 5: QlikView Dashboard showing Total ROI & Customer by Cluster and BetType
It is evident from this report (Figure 5) that the troublesome cluster 8 punters who play on the Asian
Handicaps are making a healthy 11% on turnover. Decisions and changes are required to ensure the edge the
book is expecting to make is not being eroded away by poor risk management and/or strategic decisions.
According to QlikView, they are “the most flexible Business Intelligence platform for turning data into
knowledge.” More than 24,000 organisations worldwide have enabled their users to easily consolidate, search,
and visually analyse all of their data for unprecedented business insight using QlikView’s simplicity. Taking just
minutes to learn, the automatic associations of QlikView create endless possibilities for making ad hoc queries
without requiring tediously defined structures and hierarchies, as is typical in other data analysis tools. QlikView
promotes unrestricted analysis of application data, helping users make time-saving and accurate decisions
easily.
QlikView brings a whole new level of analysis, insight, and value to existing data stores with user interfaces
that are clean, simple, and straightforward. With QlikView, businesses can rapidly deploy fast, flexible Business
Discovery apps that provide business users with dynamic views of the information they need to make important
business decisions.
Unlike most business intelligence software, with QlikView business users can ask and answer the next
question, and the question after that, without going back to an expert for a new report or data visualization. The
answers are already there, available through simple clicks and taps.
At the core of QlikView is their “patented software engine that generates new views of data on the fly.
QlikView compresses data and holds it in-memory, where it is available for immediate exploration by multiple
users. For datasets too large to fit in-memory, QlikView connects directly to the data source. QlikView delivers an
associative experience across all the data used for analysis, regardless of where it is stored. Users can start
anywhere and go anywhere; they are not limited to pre-defined drill paths and preconfigured dashboards.”1
With QlikView’s patented core technology, associative experience, and collaboration and mobile capabilities,
users can ask and answer streams of questions on their own or in teams and groups, wherever they happen to
be working. The more users, the more questions, the more value. Empowering the information workforce to
derive insights from data helps organizations streamline, simplify, and optimize decision making.
1
www.qlikview.com

Qualex Asia Limited
5
Business users primarily interact with the QlikView browser and mobile clients—though they can also use
QlikView Desktop. QlikView users access apps running on QlikView Server with a browser independent,
download-free Ajax client or a Microsoft Internet Explorer plugin. Users can make selections in list boxes and
charts and can change charts or create new ones for new views of data. They conduct direct and indirect
searches—globally or within a particular field. They can always see in an instant what data is related to their
selections and what data is not.
Right in the QlikView browser, users can collaborate in real time with colleagues, partners, or customers—
even those who don’t have QlikView licenses. QlikView also provides in-app threaded discussions to preserve a
record of how decisions were made. With a server-based QlikView app, all users are working with the same
data. Any modifications a user makes to an app (e.g., modify or add a graph or chart) can be kept private or
shared with others.
With mobile Business Discovery, information workers can take advantage of being in a particular place at a
particular time and can generate insights “on location.” QlikView on mobile delivers full Business Discovery and
the power of QlikView to mobile devices connected to a server—including the associative experience, interactive
analysis, access to live data, and search. For Apple iPads, QlikView also provides users with offline views of
data.
At no additional license cost, QlikView delivers full mobile functionality for Apple iPad and Android tablets as
well as small-screen devices such as Apple iPhone and Android phones. Using a browser-based, build once /
deploy anywhere approach, QlikView on mobile takes full advantage of native mobile gestures and features,
while providing centralized security, scalability, and manageability.
As one of Qualex’s consulants put it, “QlikView is about the destination of data exploration and discovery
and taking a developer-like approach. If you do things in Excel forms or have done any simple VB programming,
you will feel right at home with
QlikView. WYSWIG dashboards are
much easier in QlikView. Each object
in QlikView has a multitude of options
and properties, you can manipulate
nearly everything about the object
from a look and feel standpoint, to the
data being processed.
In addition, QlikView comes with
its own ETL engine built-in so if you
have issues with data, you have the
option to use this powerful tool. There
is an amazing amount of flexibility in
how you can set the properties as
well as create the look and feel of the
dashboard.“
QlikView is a developer mindset
UI, study the problem, program the
dashboard, print. QlikView allows you
to do wonderful things of looking at
the data as long as you don't change the question on it. If the data is in different forms, needs to be pinpointed in
terms of its printing capability, you know what questions to ask, and are OK with more of a programmer-like UI
Figure 6: QlikView Business Discovery Platform

Qualex Asia Limited
6
and mind set, or is more reporting-only-focused, QlikView may be right for you,” our consultant added.
Business Discovery (see Figure 6 for a layout of the Qlikvew Discovery Platform) is user-driven business
intelligence that helps people make decisions based on multiple sources of insight: data, people, and the
environment. Users can create and share knowledge and analysis in groups and across organizations. Business
Discovery platforms help people ask and answer their own questions and follow their own path to insight.
Business Discovery platforms deliver insight everywhere, an app model, mobility, remixability and
reassembly, and a social and collaborative experience. Other QlikView features include:
 An app model–business discovery applications empower users to create their own intuitive apps that
can be easily reused and discarded when no longer needed. QlikView apps are easy to use, modify and
share.
 Remixability and reassembly—nobody can predict what questions business users will have when they
start exploring data—not even the users themselves. Traditional BI solutions require IT pros to get
involved and stay involved, creating new queries and reports whenever users come up with new
questions. In contrast, QlikView makes it easy for business users themselves to remix and reassemble
the data. With QlikView, business users can utilize visualization to facilitate the easy understanding of
data.
 A social and collaborative experience—Business Discovery is about nurturing communities of people
who engage in active decision-making to drive knowledge that can cascade across an organization.
Business Discovery enables users to collaborate on insights and move toward decisions either directly
within their Business Discovery applications or through integration with enterprise collaboration tools
like Microsoft SharePoint or Salesforce Chatter.
 Mobility—Business decision makers at all levels in an organization need data at their fingertips
wherever they are. Tablets and other mobile devices have made business data mobile. Unlike
traditional BI solutions, QlikView mobile Business Discovery platforms provide intuitive interfaces and
an application infrastructure tailor-made for business users. Business users can explore data and draw
associations and insights wherever they happen to be working.
With QlikView Business Discover, business users pursue their own path to insight, make business decisions
collaboratively, and arrive at a whole new level of decision making. Business users are not given predetermined
paths or questions formulated in advance. They ask what they need to ask and explore independently rather
than follow a predetermined route. Business Discovery is the approach that ultimately fulfills the promise of BI
(see Figures 6 – 11 for additional dashboards).

Qualex Asia Limited
7
Figure 6: QlikView Dashboard Revealing Scatter Plot Chart
Figure 7: QlikView Dashboard - Total ROI by Cluster and Bet Type

Qualex Asia Limited
8
Figure 8: QlikView Dashboard - Average ROI and Wages by Cluster
Figure 9: QlikView Dashboard: Average Wagers by Cluster

Qualex Asia Limited
9
Figure 10: QlikView Dashboard - BetResult by Bettype, League Name and Cluster
Figure 11: QlikView Dashboard showing Conv_Stake by Client ID and BetType HeatMap
1.1.1.2 Predictive Analytics Sports Book Case Study
To continue with the story that began with the QlikView dashboards, we have constructed a data set that
contains major soccer bet types with the aim of discovering the following: Can a segmentation of customers help
the sports book identify distinct types of customers? Moreover, we thought it would be worthwhile to see whether
winning customers could be separated from losing customers. Armed with this knowledge, strategic decisions
could then be made with respect to risk profiling and management. Marketing campaigns could also be aimed at
the losing customers to stimulate more turnover and try and balance the ledger a little in the house’s favour. The
following bet types were used:
 'Asian Handicap','Asian Handicap First Half','Correct Score', 'Coupon','Half-time Coupon','Half/Full Time
Double','Under Over', 'Under Over First Half'
Raw Data Set metrics:

Qualex Asia Limited
10
 Turnover = $175,421,910
 Result = $5,347,080 (house lost)
 Wagers = 613,768
 Customers = 22,087
 Time Span = 3 years, 11 months
Some records are omitted due to not being able to attach a region to them, modelling data set reduces to:
 Turnover = $168,970,928
 Result = $3,747,225 (house lost)
 Wagers = 581,012
 Customers = 20,635
 Time Span = 3 years, 11 months
Input Metrics:
 Wagers: number of unique wagers placed by customer
 BetResult: total profit/loss of customer on bets placed (positive indicates winning, negative losing)
 Conv_stake: total turnover of customer converted to standardised currency (AUD)
 ROI: profitability of customer = Conv_stake/BetResult
 Unique_region = number of regions customer has wagered on
 Unique_bettype = number of bet types customer has placed
 Days_since = days since customer has placed last wager.
 Modelling: II used to create a cluster model on the input metrics described above
o Cutting strategy = random without test (due to smallish number of customers in data set)
o Solution requested that is between 8 and 16 clusters
o Distance metric = system determined
o Encoding strategy = Standard Deviation Normalization (due to different scales and magnitudes of
input variables)
Cluster profile window used to name clusters, and profile shown below:

Qualex Asia Limited
11
Figure 12: Pie Chart Showing Clusters Frequency
The cluster metrics are:
 Cluster Frequency Wagers
Conv
Stake
Unique
Region
Unique
Bettype
Days
Since
Bet
Result
ROI
Negative ROI, Extremely Light
Players, Action within past 12
months, Losing, Small Stakes,
Few Regions played, Limited
bet type
25.84% 3 $567 2 1 280 -$446 -89%
Very High ROI, Extremely
Light Players, Spread of
Recency, Moderate Winners,
Small Stakes, Specialised
Region, Specialised bet type
6.95% 2 $319 1 1 438 $420 180%
Negative ROI, Very Light
Players, Not seen for 3 years
(Lapsed), Losing, Small
Stakes, Limited Regions,
Limited bet types
11.58% 5 $505 2 1 1043 -$306 -82%
Negative ROI, Light Players,
Wagering Recently (New
Customers), Losing, Average
Stakes, Average Regions,
Limited bet types
6.39% 20 $23,224 5 2 283 -$3,568 -15%
Positive ROI, Light Players,
Semi-Established, Heavy
Winners, Small Stakes,
Specialised Regions,
Specoalised bet type
14.58% 7 $1,784 3 1 299 $531 42%

Qualex Asia Limited
12
Positive ROI, Average Players,
Not seen for three years,
Average returns, Small
Stakes, Average Regions,
Average bet types
12.67% 19 $2,301 4 2 1016 $333 29%
Positive/Negative ROI,
Average Players, Very Recent
customers, Spread of
winners/losers, Average
Stakes, Play many Regions,
Play many bet types
12.10% 28 $5,792 7 3 227 -$44 -5%
Positive/Negative ROI, Heavy
Players, Very Recent
Customers, Spread of
winners/losers, High Stakes,
Play Anywhere, Play Anything
9.43% 218 $47,725 21 4 192 $1,078 0%
Unassigned 0.46% 49 $3,269 9 6 311 $11 -2%
In looking at the overall profile of metrics across segments, a further summary of clusters could result as follows:
 Cluster 1: New rats and mice customers still maturing.
 Cluster 2: Definite watch for the book. May be customers picking off prices, i.e. taking value.
 Cluster 3: Dormant customers who joined, lost and left.
 Cluster 4: New customers still maturing, but worth encouraging.
 Cluster 5: Danger segment. Knowledgeable Players.
 Cluster 6: Winning customers that have lapsed. Don’t encourage back.
 Cluster 7: Bread and butter customers who have joined recently.
 Cluster 8: Big players whose given profile should be able to be beaten. Definitely need to encourage and
propagate.
It is clearly evident that a significant reduction in data has taken place, allowing the sports book to utilise the
much more succinct representation of their clientele to make strategic decisions across different areas of the
business. The results of this analysis would be beneficial to the odds makers, risk managers, marketing
department and also senior management. It would be expected that various dashboards (similar to those
presented earlier) would be surfaced across the relevant areas of the business to help shape decision making.
Some examples of potential insight that could be gained from different users:
 Odds Makers—The appearance of a customer from Cluster 2 on an event market could be used as an
early warning system for winding the price in, or at the very least validating the market against
competitors or the exchange.
 Risk Managers—Wagers placed by customers residing in Cluster 5 could be handled differently, be it
bet back at a better price or throttled back by only partially accepting the requested stake.
 Marketing—Many and varied applications of the results of this model, including the targeting of Cluster
8 customers with a “Welcome Offer” that will ensure entrenchment with the organisation and generate
turnover; win back campaign to Cluster 3 customers to see whether more business can be extracted
from them. A different application of the results could be to build an acquisition model which identifies
the publicly available metrics/characteristics associated with the desirable population of cluster 7 and 8.
 Senior Management—Used, as outlined above, where policy and process decisions can be verified as
appropriate, using the results of the segmentation model. This may include an analysis of lead time, i.e.

Qualex Asia Limited
13
time before event start that bets are placed to ascertain whether ensuring markets are visible early
doors is beneficial.
1.2 About Qualex Asia Limited
Qualex Asia is a subsidiary of Qualex Consulting Services, Inc., a Virginia corporation, which maintains its
corporate headquarters in Miami, Florida and currently has a worldwide employee base of 85+ consultants.
Founded over two decades ago, Qualex is a leading software solutions services company that provides software
and consulting services to multiple industries, including, gaming and hospitality, professional sports, government,
financial services, insurance, telecommunications, energy, pharmaceutical, manufacturing, retail and healthcare.
In 2011, Qualex opened Qualex Asia and set up an office in Macau, (SAR) China, to tap into the burgeoning
casino and hospitality business in Macau. Since then, Qualex Asia has worked on projects in the Philippines,
Singapore, Malaysia and Australia.
Since 2004, Qualex has implemented several business intelligence, customer intelligence, data integration,
ERP, enterprise data warehouse, campaign management, data visualization and predictive analytics solutions for
the casino and hospitality industry. Qualex was involved in the initial development of the SAS Patron Value
Optimization (PVO) solution and has experience with both small and large-scale casino implementations. Qualex
also offers mobile and social media solutions.
Qualex’s casino gaming projects include large integrated casino resorts in the United States and Asia,
several smaller Native American gaming properties, lottery and sports betting companies, and hybrid
racetrack/casino complexes (Racinos). Implementations have been conducted at South Point Casino (Las
Vegas), Foxwoods (Connecticut), Delaware Park (Delaware), Pinnacle Corporation (Las Vegas), The Venetian
Macao (Macau), Sands Bethlehem (Pennsylvania), The Cosmopolitan (Las Vegas), Pearl River Resort
(Mississippi) and Osage Casino (Oklahoma).
Qualex brings to the table a team of senior level consultants who offer detailed experience with most of the
operational source systems generally found in the casino gaming industry, such as:
 Casino Management – IGT Advantage; Bally ACSC SMS; Bally ACSC; Bally SDS; Bally Casino
Marketplace; Aristocrat Oasis; Konami KCMS.
 Hotel Management – LMS; LGS; Opera; Megasys.
 Point of Sale – InfoGenesis; Micros.
 Database & Warehouse – Teradata; Oracle; Microsoft/SQL.
 Time & Attendance – Kronos
 Financial Management – Lawson; Microsoft Dynamics.
 Spa Management – SpaSoft.
 Data Visualization - CDI
 Reservation Management – GuestBridge.
1.3 Index of Figure and Tables
Figure 1: QlikView Dashboard Showing Customer Segmentation Clusters Graphed to Reveal Win-loss by Segment
and Type of Bet...................................................................................................................................................... 1
Figure 2: QlikView Dashboard showing BetResult by Cluster and BetType HeatMap ................................................... 2

Qualex Asia Limited
14
Figure 4: QlikView Dashboard showing Conv_Stake and BetType HeatMap ................................................................ 2
Figure 5: QlikView Dashboard: Scatter Plot Chart Revealing Customer Stake and Bet Result ..................................... 3
Figure 5: QlikView Dashboard showing Total ROI & Customer by Cluster and BetType ............................................... 4
Figure 6: QlikView Dashboard Revealing Scatter Plot Chart ......................................................................................... 7
Figure 7: QlikView Dashboard - Total ROI by Cluster and Bet Type.............................................................................. 7
Figure 8: QlikView Dashboard - Average ROI and Wages by Cluster............................................................................ 8
Figure 9: QlikView Dashboard: Average Wagers by Cluster.......................................................................................... 8
Figure 10: QlikView Dashboard - BetResult by Bettype, League Name and Cluster ..................................................... 9
Figure 11: Conv_Stake by Client ID and BetType HeatMap .......................................................................................... 9
Figure 12: Pie Chart Showing Clusters Frequency ...................................................................................................... 11

Sports Book Insights from Analytics

Recommended

Recommended

More Related Content

Similar to Sports Book Insights from Analytics

Similar to Sports Book Insights from Analytics (20)

More from Qualex Asia

More from Qualex Asia (10)

Recently uploaded

Recently uploaded (20)

Sports Book Insights from Analytics