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1 Extreme Shooters in the NBA Manav Kant and Lisa R. Goldberg December 24, 2018 1. Introduction It is widely perceived that star shooters in basketball have hot and cold streaks. This perception was first challenged by an influential article, Gilovich, Vallone and Tversky (1985), in which the authors argued that hot and cold streaks in basketball are a cognitive illusion and can be explained by mere chance [1]. They further stated that the breadth and persistence of the perception that something exceptional is going on illustrates just how poorly we understand randomness. Gilovich et al. mainly identified hot handedness in two ways: as an elevated probability of scoring after a streak of a fixed length, and as the tendency of a shooter to shoot in runs, or clusters, of hits and misses. Using these identifications, a number of subsequent studies came out with disparate answers to the “hot hand question.” Koehler and Conley (2003) determined that data from the NBA Long Distance Shootout contest did not provide evidence for hotness or for sequential dependencies [2]. More recently, however, Miller and Sanjurjo (2018) found that there is “a subtle but substantial bias... in a standard measure of the conditional dependence of present outcomes on streaks of past outcomes in sequential data,” and determined that “upon correcting for the bias, the conclusions of prominent studies in the hot hand fallacy literature are reversed” [3]. However, Daks, Desai and Goldberg (2018), accounting for the bias pointed out by Miller and Sanjurjo with a (nonparametric) permutation test, concluded that there was little evidence for hot handedness among star players of the Golden State Warriors team that won the 2016-2017 NBA championship [4]. The brief summary given above is, of course, by no means a comprehensive history of the literature on hot handedness in basketball. Nevertheless, we hope it provides some insight into the continued debate on the subtleties in analyzing streak shooting. In this study, we explore a novel approach to the hot hand question by considering the possibility that the human perception of hot handedness may be more closely related to the percentage of shots made by a player in a game (relative to the expectations set by his season-long hit percentage) than to streaks of shots made throughout the game. We work with simple formulations of “hot” and “cold” that rely entirely on field goal percentages and number of shots taken, using data from the official NBA API on all field goals attempted in the 2015-2016, 2016-2017, and 2017-2018 NBA seasons by top shooters. For any player, we identify “extreme shooting” in any particular game by determining the probability that the player would have scored more or an equivalent number of shots (out of those he took) than he actually did in the game, given that the probability of the player making any given shot was the player’s season-long shooting percentage. The player is deemed an “extreme shooter” if certain high or low quantiles of the distribution of these probabilities, or “single game p-values,” throughout a season differ significantly from the corresponding quantiles of a distribution that assumes the player’s single game p-values deviate modestly from what is expected based on his

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ExtremeShootersintheNBA

ManavKantandLisaR.Goldberg

December24,20181.IntroductionItiswidelyperceivedthatstarshootersinbasketballhavehotandcoldstreaks.Thisperceptionwasfirstchallengedbyaninfluentialarticle,Gilovich,ValloneandTversky(1985),inwhichtheauthorsarguedthathotandcoldstreaksinbasketballareacognitiveillusionandcanbeexplainedbymerechance[1].Theyfurtherstatedthatthebreadthandpersistenceoftheperceptionthatsomethingexceptionalisgoingonillustratesjusthowpoorlyweunderstandrandomness.Gilovichetal.mainlyidentifiedhothandednessintwoways:asanelevatedprobabilityofscoringafterastreakofafixedlength,andasthetendencyofashootertoshootinruns,orclusters,ofhitsandmisses.

Usingtheseidentifications,anumberofsubsequentstudiescameoutwithdisparateanswerstothe“hothandquestion.”KoehlerandConley(2003)determinedthatdatafromtheNBALongDistanceShootoutcontestdidnotprovideevidenceforhotnessorforsequentialdependencies[2].Morerecently,however,MillerandSanjurjo(2018)foundthatthereis“asubtlebutsubstantialbias...inastandardmeasureoftheconditionaldependenceofpresentoutcomesonstreaksofpastoutcomesinsequentialdata,”anddeterminedthat“uponcorrectingforthebias,theconclusionsofprominentstudiesinthehothandfallacyliteraturearereversed”[3].However,Daks,DesaiandGoldberg(2018),accountingforthebiaspointedoutbyMillerandSanjurjowitha(nonparametric)permutationtest,concludedthattherewaslittleevidenceforhothandednessamongstarplayersoftheGoldenStateWarriorsteamthatwonthe2016-2017NBAchampionship[4].

Thebriefsummarygivenaboveis,ofcourse,bynomeansacomprehensivehistoryoftheliteratureonhothandednessinbasketball.Nevertheless,wehopeitprovidessomeinsightintothecontinueddebateonthesubtletiesinanalyzingstreakshooting.

Inthisstudy,weexploreanovelapproachtothehothandquestionbyconsideringthepossibilitythatthehumanperceptionofhothandednessmaybemorecloselyrelatedtothepercentageofshotsmadebyaplayerinagame(relativetotheexpectationssetbyhisseason-longhitpercentage)thantostreaksofshotsmadethroughoutthegame.Weworkwithsimpleformulationsof“hot”and“cold”thatrelyentirelyonfieldgoalpercentagesandnumberofshotstaken,usingdatafromtheofficialNBAAPIonallfieldgoalsattemptedinthe2015-2016,2016-2017,and2017-2018NBAseasonsbytopshooters.

Foranyplayer,weidentify“extremeshooting”inanyparticulargamebydeterminingtheprobabilitythattheplayerwouldhavescoredmoreoranequivalentnumberofshots(outofthosehetook)thanheactuallydidinthegame,giventhattheprobabilityoftheplayermakinganygivenshotwastheplayer’sseason-longshootingpercentage.Theplayerisdeemedan“extremeshooter”ifcertainhighorlowquantilesofthedistributionoftheseprobabilities,or“singlegamep-values,”throughoutaseasondiffersignificantlyfromthecorrespondingquantilesofadistributionthatassumestheplayer’ssinglegamep-valuesdeviatemodestlyfromwhatisexpectedbasedonhis

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season-longshotpercentage.Hotandcoldhandedness,inourstudy,aremerelytwodifferentcasesofextremeshooting.Aplayercanbehothanded,coldhanded,orboth.Ourconceptof"modestdeviation"isformulatedintermsofamixturebasedonbinomialdistributionsdeterminedbythenumberofshotstheplayertookineachgameandhisseason-longshootingpercentage.

Wedeterminethatcertainplayers—includingKlayThompson,whoisoftenconsideredtheprimeexampleofhothandedshooting—displaysignsofhotandcoldhandednessinourextremeshootingformulationofthehothandquestion.

Ourfindingscouldinfluencethewaythatcoachesselecttheirlineups(possiblyselectinghothandedplayersformatchesagainstmuchbetteropponents)andhowfansconnectwiththegameofbasketball(byconnectingpeople’sintuitionwithharddata).

2.Methodology2.1.MotivatingExampleConsiderahypotheticalplayerthattakes20shotsineachgameandmakes50%ofthoseshotsthroughoutthecourseoftheseason.Wedon’texpecthimtomakeexactly10of20shotsineachgame,andwedon’texpecthimtomakeall20shotsinhalfthegamesandnoshotsintheotherhalf.Ourexpectationsliesomewherebetweenthosetwoextremes.Wemightselectabinomialdistributionwith20trialsandmean0.5asamodelofhowwemightexpecthisgameaveragestobedistributed.Ifthegameaveragesareunusuallyconcentratedinthetailsofthedistribution,ourplayerisanextremeshooter.

2.2.IdentifyingExtremeShootinginaSingleGameGeneralizingfromthepreviousexample,aplayer’sshootingpercentageingamen,qn,isextremeifitisunusuallyhighorlowrelativetohisseasonaverage,q.Acomplicationisthatunlikeourhypotheticalplayer,arealplayerisboundtoattemptdifferentnumbersofshotsindifferentgames.Thus,simplyanalyzingtheplayer’shitpercentageinallgameswouldbeanoversimplificationoftheproblem.Aplayerwhomakes12shotsoutof16isamoreimpressiveshooterthanonewhomakes3shotsoutof4.This,however,wouldnotbereflectedinananalysisbasedpurelyonshotpercentages.Toputourobservationsonamorecommonfooting,wecomputethep-value,pn,ofqnwithrespecttothebinomialdistributionB(sn,q),wheresnisthenumberofshotsattemptedingamen.Thebinomialdistributionservesasamodelofamodestdistributionaroundtheseasonaverage,andthep-valueistheprobabilityofequallingorexceedingtheobservedvalueinthebinomialdistributionB(sn,q).Alowp-valuecorrespondstoahighgameaverageandconverselysince,

𝑝" =$ %𝑠"𝑘 ()*

+,)*-*𝑞+(1 − 𝑞))*3+ .

Inthepreviousexample,iftheplayer’sseason-longhitfractionwas0.5,thep-valuefortheformercase(12shotsoutof16)wouldbe0.0384,whereasthatforthelattercase(3shotsoutof4)wouldbe0.3125.Thisreflectsthefactthatitbecomesmoredifficulttoattainorexceedagivenhighshootingpercentageasthenumberofattemptedshotsincreases.2.3.EvaluatingaPlayerOvertheCourseofaSeason

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WelookatwhetheraplayershootsinanextrememannerbyexaminingP,thecollectionofaplayer’ssingle-gameshootingpercentagep-valuesrealizedoverthecourseofaseason.Considerfirst,thecumulativedistributionfunction(CDF)associatedwithP,denotedF.Roughlyspeaking,anunexceptionaldistributionofgameshootingpercentageswoulddriveFtowardtheCDFoftheuniformdistributionoftheunitinterval,denotedU,whichisa45degreeline.However,thisisnotexactlyright,asabinomialdistributionisdiscreteandthesingle-gamep-valuesareconstructedfromdifferentbinomialdistributions.Consequently,amoreaccuratenullhypothesisisamixtureofdiscretedistributions.LetMbetheCDFofamixtureofthep-valuedistributionsofbinomialdistributions,wheretheweightofthep-valuedistributionofB(s,q)inthemixtureisthefractionofgamesintheseasoninwhichtheplayerattemptedsshots.Inwhatfollows,wewillusesimulationsofseasonsbasedonthenullhypothesisconstructedabovetodeterminewhich,ifany,quantilesofFareexceptionalandwhattheseexceptionalquantilesindicateaboutthedistribution.Forexample,anunusuallylow10%quantilewouldcorrespondtoapreponderanceofunusuallyhigh-percentagegames(indicatinghothandedshooting),andanunusuallyhigh90%quantilewouldcorrespondtoapreponderanceofunusuallylow-percentagegames(indicatingcoldhandedshooting).Ingeneral,unusuallylowlowerquantiles(quantilesbelow50%)indicatehothandedness,andunusuallyhighupperquantiles(quantilesabove50%)indicatecoldhandedness.Oursimulationisbasedon30,000replicationsofastarplayer’sseason.Ineachreplication,wedrawashootingpercentageatrandomfromeachbinomialdistribution,B(sn,q)andnoteitsquantileintheempiricaldistributionofsinglegamep-valuesassociatedtothedraws.Finally,forallQsuchthatQisamultiple5ontheinterval[10,90],wecomputethep-valueoftheQ%quantileoftheplayer’ssinglegamep-valuedistributionwithrespecttothedistributionofQ%quantilesofthe30,000seasonsimulations.IfQ>50andthis“Q%quantilep-value”isgreaterthan0.9,thenwedeemtheplayercoldhanded.IfQ<50andtheQ%quantilep-valueislessthan0.1,thenwedeemtheplayerhothanded.Forpracticalpurposes,wedefinetheQ%quantileofanydistributionconsistingofnvaluestobethevalueatthe⌈(Q/100)n⌉thpositionwhenthevaluesareplacedinascendingorder.2.4.CriteriaforPlayerEvaluationInordertoreducetheeffectsofthenoisethatispresentinsmalldatasets,wesetcriteriaforevaluationbasedonthenumberofgamesplayedinaseasonandtheaveragenumberoffieldgoalsattemptedpergamebyanyplayer.Welookataplayeronlyifheplayed60ormoregames(outofapossible82)andattemptedanaverageofatleast14fieldgoalspergameduringtheseasonbeingstudied.Byensuringthateachplayerplayedareasonablenumberofgames,wereducetheprobabilityofmakingaTypeIerror;itismorelikelythat,bysheerchance,thelowest3p-valuesinadistributionconsistingof30p-valuesarelowenoughtoindicatehothandednesswhenthereisnone,thanthatthelowest8p-valuesinadistributionconsistingof80p-valuesareassuch.Ourcriteriononaveragenumberoffieldgoalsattemptedaccomplishesthesamepurpose,butforsinglegamep-valuesratherthanfortheseason-longdistributionofp-values.Ouruseofaplayer’sseason-longshootingpercentagetosetexpectationsforhisshotpercentageinanygivengameintroducesanelementoflook-aheadbiastoouranalysis.Thisisbecausetheplayer’sseason-longhitpercentageisdeterminedfromanaggregateofthedatafromallofthegamesheplayed,manyofwhichmayhavebeenplayedafterthegamebeingstudied.Inessence,wemeasuretheplayer’sperformanceagainstanulldistributionthatisbasedonavaluethatwasnot

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fixedatthetimeofthegame,andthatinfact,thegamebeingstudiedplayedaroleindetermining.Inordertoreducetheeffectsoflook-aheadbiasonouranalysis,wedeterminedthat,tobedeclaredanextremeshooter,aplayermustnothaveacleartrendinhishitpercentagethroughouttheseason.Forexample,ifaplayerwhoattempted20shotsineachgame(forthesakeofsimplicity)consistentlyshotataround20%forthefirsthalfoftheseasonandataround80%forthesecond,thenaseason-longhitpercentageofabout50%maybeanincorrectexpectationforeitherhalfoftheseason,astheplayerlikelysimplygotbetterathittingshotsaroundhalfwaythroughtheseason.Then,neithera20%hitpercentageinagameatthebeginningoftheseasonnoran80%hitpercentageinagameattheendoftheseasonwouldbeconsideredextremeperformances,astheywouldbothcorrespondtotheexpectedperformancesfortheirrespectiveportionsoftheseason.3.ResultsWefirstappliedourmethodologytothoseplayersinthe2016-2017seasonthatsatisfiedourplayerevaluationcriteria(seesection2.4),andthenanalyzedthemembersofthatgroupthatalsosatisfiedthosecriteriainthe2015-2016and2017-2018seasons.3.1.2016-2017SeasonResultsForthe2016-2017season,wefound4players--KlayThompson,DeMarDeRozan,GordonHayward,andJimmyButler--tobehothanded,coldhanded,orboth(seeTableI).

TableI.Quantilep-valuesforDeMarDeRozan,KlayThompson,JimmyButler,andGordonHaywardinthe2016-2017season.SeeTableVforquantilep-valuesforallplayersinthe2016-2017season.

Inouranalysis,wefoundthatthequantilep-valuesforthe30%,35%,and40%quantilesofKlayThompson’ssinglegamep-valuedistributionwere0.052,0.065,and0.023respectively(seeTableI).ThesevaluesgiveusreasontobelievethatThompsonisahothandedshooter,astheyindicatethathisshootingfollowsadistributionthathasagreaterconcentrationofsinglegamep-valuesatitslowerendthandoesthenull.Aslowerp-valuesresultfromhighershotpercentages(seesection2.2),thisimpliesthatThompsonhasaproclivitytoget“hot”(withrespecttotheexpectationssetbyhisseason-longhitfraction)inadisproportionatelylargenumberofgames.AvisualizationofthisempiricalresultispresentedinFigure1.1(A).Atthe30%,35%,and40%quantiles(demarcatedbythedashedyellowlines),theCDFofKlayThompson’ssinglegamep-valuedistributionisclearlytotheleftofthenulldistribution.However,atthe45%quantileandabove,Thompson’sdistributionsticksfairlyclosetothenull.Finally,lookingatFigure1.1(B),wedeterminedthatKlayThompson’sshootingpercentagesthroughoutthe2016-2017seasondidnotseemtofollowanycleartrend.Thus,look-aheadbiaslikelydidnotplayalargeroleintheoutcomesfromouranalysis.

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(A) (B)

Figure1.1.ThegraphontheleftdisplaystheCDFofKlayThompson’ssinglegamep-valuedistributioninthe2016-2017season(inred)ascomparedtothatofthenulldistributionforKlayThompsonthatseason(ingreen).ThegraphontherightisatimeseriesgraphofThompson’shitfractionineachgamethroughoutthe2016-2017season.

WefurtherfoundDeMarDeRozanandGordonHaywardtobecoldhanded(incontrastwithKlayThompson)asillustratedinFigure1.2andFigure1.3.

Figure1.2.DeMarDeRozan’ssinglegamep-valuedistributionforthe2016-2017seasonascomparedtothenulldistribution,andthetimeseriesgraphofDeRozan’shitfractionsthroughoutthatseason.

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Figure1.3.GordonHayward’ssinglegamep-valuedistributionforthe2016-2017seasonascomparedtothenulldistribution,andthetimeseriesgraphofHayward’shitfractionsthroughoutthatseason.

Finally,wefoundJimmyButlertobeageneralextremeshooter(showingsignsofhotandcoldhandedness)inthe2016-2017season,with25%,85%,and90%quantilep-valuesof0.054,0.917,and0.933respectively(seeTableI).Thesevaluesindicatethathisshootinginthe2016-2017seasonfollowedadistributioninwhichsinglegamep-valuesareconcentratedatbothtails.ThiscanbeseeninthegraphoftheCDFassociatedwithButler’ssinglegamep-valuedistributionandthetimeseriesgraphofhiscrudeshootingpercentages,thelatterofwhichmakesapparentthevariabilityassociatedwithButler’sshooting(seeFigure1.4).

Figure1.4.JimmyButler’ssinglegamep-valuedistributionforthe2016-2017seasonascomparedtothenulldistribution,andatimeseriesgraphofButler’shitfractionsthroughoutthatseason.

3.2.2015-2016SeasonResultsForthe2015-2016NBAseason,wefoundevidencethatoneplayer--DeMarcusCousins--wasacoldhandedshooter,asillustratedbyTableIIandFigure2.

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TableII.Quantilep-valuesforDeMarcusCousins’singlegamep-valuedistributioninthe2015-2016season.SeeTableIVforquantilep-valuesforallplayersinthe2015-2016season.

Figure2.DeMarcusCousins’singlegamep-valuedistributionforthe2015-2016seasonascomparedtothenulldistribution,andthetimeseriesgraphofCousins’hitfractionsthroughoutthatseason.

3.3.2017-2018SeasonResultsWefound4players--RussellWestbrook,AndrewWiggins,KembaWalker,andKlayThompson--tobehothanded,coldhanded,orbothduringthe2017-2018NBAseason(seeTableIII).Westbrookinparticularshowedclearsignsofhothandedness,withquantilep-valuesof0.026,0.087,and0.058athis10%,15%,and20%quantilesrespectively(seeTableIIIandFigure3.1).

TableIII.Quantilep-valuesforRussellWestbrook,AndrewWiggins,KembaWalker,andKlayThompsoninthe2017-2018season.SeeTableVIforquantilep-valuesforallplayersinthe2017-2018season.

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Figure3.1.RussellWestbrook’ssinglegamep-valuedistributionforthe2017-2018seasonascomparedtothenulldistribution,andthetimeseriesgraphofWestbrook’shitfractionsthroughoutthatseason.

WealsofoundevidencethatbothAndrewWigginsandKembaWalkerwerehothandedinthe2017-2018season(seeFigure3.2andFigure3.3).

Figure3.2.AndrewWiggins’singlegamep-valuedistributionforthe2017-2018seasonascomparedtothenulldistribution,andthetimeseriesgraphofWiggins’hitfractionsthroughoutthatseason.

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Figure3.3.KembaWalker’ssinglegamep-valuedistributionforthe2017-2018seasonascomparedtothenulldistribution,andthetimeseriesgraphofWalker’shitfractionsthroughoutthatseason.

Finally,wefoundevidencethatKlayThompsonshowedsignsofbothhothandednessandcoldhandednessinthe2017-2018season.ThisisevidentinthegraphoftheCDFofhissinglegamep-valuedistributionforthatseason--thegraphseemstobetotheleftofthenulldistributionatthelowerquantiles,andtotherightofthenullattheupperquantiles(seeFigure3.4).

Figure3.4.KlayThompson’ssinglegamep-valuedistributionforthe2017-2018seasonascomparedtothenulldistribution,andthetimeseriesgraphofThompson’shitfractionsthroughoutthatseason.

4.ConclusionTheperceptionofthe“hothand”inbasketballiscommonamongfans.However,manypreviousstudiesthatfocusedonstreakshootingwithingameslargelydidnotfindthestatisticalevidenceforthisphenomenon.

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Bylookingatshotpercentagesthroughoutaseasonratherthanshotstreakswithinagame,wedeterminedthatthereisreasontobelievethatanumberofNBAplayersareextremeshooters--playersthatshootaccordingtosinglegamep-valuedistributionsthatdifferatthetailsfromwhatcanbeexpectedbasedontheirseason-longhitfractionsandthenumberoffieldgoalstheyattemptedineachgame.Someoftheseplayers,mostvisiblyKlayThompson,arewidelyperceivedtobeplayersthat“gethot”incertaingames,achievingresultsthatnormallyseemfaroutoftherangeofpossibilities.WedidinfactfindThompsontobeahot-handedshooterinoneseasonoutofthethreewestudiedandanextremeshooterinanother.Thisindicatesthatourmethodologymaybebetteralignedwithfanpsychologythantraditionalstreakshooting-basedmethods.5.AcknowledgementsWethankAlexPapanicolaouforprovidinguswithshotdatafromtheNBAAPIPHPLibrary.WearegratefultoNishantDesaiandJoshMillerfortheirhelpfulcommentsintheinitialstagesofourstudy.6.ResourcesCodehostedat:https://github.com/kantmanav/Reproducibility-for-Extreme-Shooters-in-the-NBA

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References[1] Gilovich,ThomasRobertVallone&AmosTversky(1985).Thehothandhandin

basketball:Onthemisperceptionofrandomsequences.CognitivePsychology17,295-314.

[2] Koehler,J.J.&Conley,C.A.(2003)The“hothand”mythinprofessionalbasketball.JournalofSports&ExercisePsychology,25(2),253-259.

[3] Miller,J.&Sanjurjo,A.(2018).Surprisedbythegambler’sandhothandfallacies?atruthinthelawofsmallnumbers.Econometrica86(6),2019-2047.

[4] Daks,A.,Desai,N.andGoldberg,L.R.(2018).DotheGoldenStateWarriorshavehothands?TheMathematicalIntelligencer40(4),1-10.

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AppendixBelowarecompletetablesofquantilep-valuesforallplayersanalyzedinthe2015-2016,2016-2017,and2017-2018NBAseasons.

TableIV.2015-2016playerquantilep-values.

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TableV.2016-2017playerquantilep-values.

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TableVI.2017-2018playerquantilep-values.