Sports Analytics and Data Science: Winning the Game with Methods and Models

352 Pages·2015·9.14 MB·English

by  Thomas W. Miller

Sports Analytics
and Data Science
Winning the Game with Methods and Models
THOMAS W. MILLER
Publisher:PaulBoger
Editor-in-Chief:AmyNeidlinger
ExecutiveEditor:JeanneGlasserLevine
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(cid:13)c2016byThomasW.Miller
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ISBN-10:0-13-388643-3
ISBN-13:978-0-13-388643-6
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Contents
Preface v
Figures ix
Tables xi
Exhibits xiii
1 UnderstandingSportsMarkets 1
2 AssessingPlayers 23
3 RankingTeams 37
4 PredictingScores 49
5 MakingGame-DayDecisions 61
6 CraftingaMessage 69
7 PromotingBrandsandProducts 101
8 GrowingRevenues 119
9 ManagingFinances 133
iii
iv SportsAnalyticsandDataScience
10 PlayingWhat-ifGames 147
11 WorkingwithSportsData 169
12 CompetingonAnalytics 193
A DataScienceMethods 197
A.1 MathematicalProgramming 200
A.2 ClassicalandBayesianStatistics 203
A.3 RegressionandClassification 206
A.4 DataMiningandMachineLearning 215
A.5 TextandSentimentAnalysis 217
A.6 TimeSeries,SalesForecasting,andMarketResponseModels 226
A.7 SocialNetworkAnalysis 230
A.8 DataVisualization 234
A.9 DataScience:TheEclecticDiscipline 240
B ProfessionalLeaguesandTeams 255
DataScienceGlossary 261
BaseballGlossary 279
Bibliography 299
Index 329
Preface
“Sometimesyouwin,sometimesyoulose,sometimesitrains.”
—TIMROBBINSASEBBYCALVINLALOOSHINBullDurham(1988)
Businesses attract customers, politicians persuade voters, websites cajole
visitors,andsportsteamsdrawfans. Whateverthegoalortarget,dataand
modelsruletheday.
Thisbookisaboutbuildingwinningteamsandsuccessfulsportsbusinesses.
Winningandsuccessaremorelikelywhendecisionsareguidedbydataand
models. Sportsanalyticsisasourceofcompetitiveadvantage.
This book provides an accessible guide to sports analytics. It is written
for anyone who needs to know about sports analytics, including players,
managers,owners,andfans. Itisalsoaresourceforanalysts,datascientists,
and programmers. The book views sports analytics in the context of data
science, a discipline that blends business savvy, information technology,
andmodelingtechniques.
To use analytics effectively in sports, we must first understand sports—
the industry, the business, and what happens on the fields and courts of
play. We need to know how to work with data—identifying data sources,
gathering data, organizing and preparing them for analysis. We also need
toknowhowtobuildmodelsfromdata. Datadonotspeakforthemselves.
Useful predictions do not arise out of thin air. It is our job to learn from
dataandbuildmodelsthatwork.
v
vi SportsAnalyticsandDataScience
The best way to learn about sports analytics and data science is through
examples. We provide a ready resource and reference guide for modeling
techniques. We show programmers how to solve real world problems by
buildingonafoundationoftrustworthymethodsandcode.
The truth about what we do is in the programs we write. The code is
there for everyone to see and for some to debug. Data sets and computer
programs are available from the website for the Modeling Techniques se-
ries at http://www.ftpress.com/miller/. There is also a GitHub site at
https://github.com/mtpa/.
When working on sports problems, some things are more easily accom-
plishedwithR,otherswithPython. Andtherearetimeswhenitisgoodto
offersolutionsinbothlanguages,checkingoneagainsttheother.
One of the things that distinguishes this book from others in the area of
sportsanalyticsistherangeofdatasourcesandtopicsdiscussed. Manyre-
searchers focus on numerical performance data for teams and players. We
takeabroaderviewofsportsanalytics—theviewofdatascience. Thereare
textdataaswellasnumericdata. AndwiththegrowthoftheWorldWide
Web, the sources of data are plentiful. Much can be learned from public
domain sources through crawling and scraping the web and utilizing ap-
plicationprogramminginterfaces(APIs).
I learn from my consulting work with professional sports organizations.
Research Publishers LLC with its ToutBay division promotes what can be
called “data science as a service.” Academic research and models can take
usonlysofar. Eventually,tomakeadifference,weneedtoimplementour
ideasandmodels,sharingthemwithoneanother.
Many have influenced my intellectual development over the years. There
werethosegoodthinkersandgoodpeople,teachersandmentorsforwhom
Iwillbeforevergrateful. Sadly,nolongerwithusareGeraldHahnHinkle
in philosophy and Allan Lake Rice in languages at Ursinus College, and
HerbertFeiglinphilosophyattheUniversityofMinnesota. Iamalsomost
thankfultoDavidJ.WeissinpsychometricsattheUniversityofMinnesota
andKellyEakinineconomics,formerlyattheUniversityofOregon.
Preface vii
My academic home is the Northwestern University School of Professional
Studies. Coursesinsportsresearchmethodsandquantitativeanalysis,mar-
ketinganalytics,databasesystemsanddatapreparation,webandnetwork
data science, web information retrieval and real-time analytics, and data
visualization provide inspiration for this book. Thanks to the many stu-
dents and fellow faculty from whom I have learned. And thanks to col-
leagues and staff who administer excellent graduate programs, including
the Master of Science in Predictive Analytics, Master of Arts in Sports Ad-
ministration,MasterofScienceinInformationSystems,andtheAdvanced
CertificateinDataScience.
Lorena Martin reviewed this book and provided valuable feedback while
she authored a companion volume on sports performance measurement
andanalytics(Martin2016). AdamGrossmanandTomRobinsonprovided
valuablefeedbackaboutcoverageoftopicsinsportsbusinessmanagement.
RoySanfordprovidedadviceonstatistics. AmyHendricksonofTEXnology
Inc.applied her craft, making words, tables, and figures look beautiful in
print—anothervictoryforopensource. CandiceBradleyserveddualroles
asareviewerandcopyeditorforallbooksintheModelingTechniquesseries.
AndAndyBeasterhelpedinpreparingthisbookforfinalproduction. Iam
gratefulfortheirguidanceandencouragement.
Thanksgotomyeditor,JeanneGlasserLevine,andpublisher,Pearson/FT
Press,formakingthisbookpossible. Anywritingissues,errors,oritemsof
unfinishedbusiness,ofcourse,aremyresponsibilityalone.
MygoodfriendBrittneyandherdaughterJaniyakeepmecompanywhen
time permits. And my son Daniel is there for me in good times and bad, a
friendforlife. Mygreatestdebtistothembecausetheybelieveinme.
ThomasW.Miller
Glendale,California
October2015
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Figures
1.1 MLB,NBA,andNFLAverageAnnualSalaries 10
1.2 MLBTeamPayrollsandWin/LossPerformance(2014Season) 11
1.3 APerceptualMapofSevenSports 13
2.1 Multitrait-MultimethodMatrixforBaseballMeasures 25
3.1 AssessingTeamStrength:NBARegularSeason(2014–2015) 40
4.1 WorkofDataScience 50
4.2 DataandModelsforResearch 52
4.3 Training-and-TestRegimenforModelEvaluation 54
4.4 Training-and-TestUsingMulti-foldCross-validation 56
4.5 Training-and-TestwithBootstrapResampling 57
4.6 PredictiveModelingFrameworkforTeamSports 59
6.1 HowSportsFitintotheEntertainmentSpace(OrNot) 72
6.2 IndicesofDissimilarityBetweenPairsofBinaryVariables 73
6.3 ConsumerPreferencesforDodgerStadiumSeating 77
6.4 ChoiceItemforAssessingWillingnesstoPayforTickets 79
6.5 TheMarket:AMeetingPlaceforBuyersandSellers 80
7.1 DodgersAttendancebyDayofWeek 104
7.2 DodgersAttendancebyMonth 104
7.3 DodgersWeather,Fireworks,andAttendance 106
7.4 DodgersAttendancebyVisitingTeam 107
7.5 RegressionModelPerformance: BobbleheadsandAttendance 108
8.1 CompetitiveAnalysisforanNBATeam:GoldenStateWarriors 129
9.1 Cost-Volume-ProfitAnalysis 135
9.2 HigherProfitsThroughIncreasedSales 136
9.3 HigherProfitsThroughLowerFixedCosts 137
9.4 HigherProfitsThroughIncreasedEfficiency 137
9.5 DecisionAnalysis:InvestinginaSportsFranchise(OrNot) 143
10.1 Game-daySimulation(OffenseOnly) 152
ix