the gambling habits of online poker players

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TheGamblingHabitsofOnlinePokerPlayersSeptember29,2011

IngoFiedler*

[email protected]

Abstract

Onlinepokerisadatagoldmine.Recordingactualgamblingbehaviorgivesrisetoahost

of research opportunities. Still, investigations using such data are rare with the excep

tionofninepioneeringstudiesbyHarvardMedicalSchoolwhicharereviewedhere.Thispaperfillspartofthevacuumbyanalyzingthegamblinghabitsofasampleof2,127,887

poker playing identities at Pokerstars over a period of six months. A couple of playing

variablesareoperationalizedandwereanalyzedontheirownaswellasconnectedwith

eachotherinformoftheplayingvolume($rakeaplayerhaspaidinatimeframe).

ThemainfindingsconfirmtheresultsoftheHarvardstudies:mostonlinepokerplayers

onlyplayafewtimesandforverylowstakes.Themedianplayerplayed7sessionsand

4.87 hours over 6 months. Multitabling was observed only rarely (median 1.05) andmostplayerspayverylowfeesperhour(medianUS$0.87perhourpertable).Theplay

ingvolumeisverylow,too,withmorethan50%ofallplayerspayinglessthanUS$4.86

totheoperatorsover6months.Ananalysisoftherelationshipbetweentheplayingha

bits shows that they reinforce each other with the exception of the playing frequency

which moderates gambling involvement. The average values of the playing habits are

considerably higher due to a small group of intense players: the 99% percentile player

hasaplayingvolumethatis552timeshigherthanthatofthemedianplayer(US$2,685),and1%oftheplayersaccountfor60%ofplayingvolume(10%foreven91%).Thisgroup

is analyzed more thoroughly, and a discussion shows that the first impulse to peg in

tense players as (probable) pathological gamblers is wrong. Rather, future research is

neededtodistinguishproblemgamblersfromprofessionalplayers.


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1.Introduction

Electronic gambling opens up a new era of research on gambling behavior. So far, analyses have been

limitedtotoosmallsamplesortogamblingbehaviorinlaboratorieswhereamonitoringbiascannotbe

accounted for. Another research method was to interview people about their gambling behavior a

questionable approach since selfreports of behavior are often inconsistent (Baumeister et al. 2007).

People generally tend to underreport their gambling behavior and pathological gamblers lie about

theirs.1Now,electronicgamblingandonlinegamblinginparticularautomaticallyrecordactualgambling

behavior.Thisallowsreliableandobjectiveanalysesofhugeandunbiaseddatasets.Suchresearchhow

ever,isinitsinfancy.PioneeringworkinthisfieldcomesintheformofaseriesofninepapersfromHar

vardMedicalSchool(LaBrieetal.2007,Brodaetal.2008,LaBrieetal.2008,LaPlanteetal.2008,Nelson

etal.2008,LaPlanteetal.2009,Xuan&Shaffer2009,Braverman&Shaffer2010,LaBrie&Shaffer2011).

Other research focusing on actual gambling behavior is still missing with the exception of Smith et al.

(2009)whocomparethegamblingbehaviorofpokerplayersbeforeandafterbigwinsandbiglosses.To

expandtheunderstandingofactualgamblingbehaviorthisstudyanalyzesthegamblinghabitsofasam

pleof2,127,887pokerplayingidentitiesatthelargestonlinepokeroperatorPokerstarsoveraperiodof

6months.

Thispaperisstructuredasfollows:thesecondsectionisareviewofthepaperseriesfromHarvardMedi

calSchoolthatfocusesonthepokerstudybyNelsonetal.(2009).ThethirdsectionintroducestheOn

li P k D t b f th U i it f H b (OPD UHH) d ti li th l i h bit f


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thatasmallgroupofplayersaccountforthemajorityoftheplayingvolume,byhighlightingthenecessity

todistinguishbetweenpathologicaland(semi)professionalpokerplayers.

2.Literature:ThePaperSeriesfromHarvardMedicalSchool

AllofthepapersfromHarvardMedicalSchoolontheactualbehaviorofonlinegamblersrelyonadata

set of approximately 47,000 betting accounts at the gambling operator bwin which were registered in

February2005.2Thestudiescanbedividedintotwoseparategroups.Onegroupanalyzesthegambling

behavior of sport bettors, poker players and casino gamblers solely on a descriptive basis. The other

group analyzes the gambling behavior of subsamples where problem gambling is indicated by account

closing, selflimitation or limitation by bwin. This allows the authors to investigate the differences be

tween recreational and probable pathological gamblers. The studies are unique in their approach be

causetheyanalyzeactualgamblingbehavior.Thiskindofdatasetovercomesthetypicallimitationsand

biasesofselfreporteddataandallowsanobjectivemeasurementofthegamblinghabits(seee.g.Xuan

& Shaffer 2009). The advantages of a data set of actual gambling behavior are enormous, and the au

thors even see a paradigm shift in gambling research (Shaffer et al. 2010). The authors distinguish be

tweentheheavilyinvolvedbettors(top5%ortop1%)andthemajority95%(99%)ofallparticipants.

The main finding is that the group of the heavily involved bettors is significantly more active than the

restofthecohort.Forexample,theinvolvementoftheintensepokerplayerswasroughlytwiceaslong

andtheyplayed7timesasmanysessions.Theywagered44timesandlost6timesmoremoneythanthe


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bwinisjustoneonlinegamblingoperatorandplayersmayhaveaccountsatmultiplesites.Multipleac

countsseemespeciallylikelyforthemostintenseplayers.Hence,theirplayingbehaviorcanonlybeob

served partially and the results underestimate their true gambling involvement. This problem is aggra

vated in the study focusingon poker players by LaPlanteet al.(2009) because bwin is mainly a sports

bettingoperatoranditcanbeassumedthatthesamplemostlyconsistsofpeoplewhoseprimarygameis

sportsbetting,meaningthatthesubsampleofpokerplayersconsistsprimarilyofplayersforwhichpoker

is their second or even third choice. Gamblers who mainly play poker games may instead sign up with

other operators specializing in these games. But as these players are, by definition, more involved, the

results of the bwin study underestimate the playing intensity of poker players.3 Although the authors

admitthisdrawbackasabias,theydonotseeitasprobablybutratherasplausible.However,thechoice

oftheoperatorisimportantfortheplayers,especiallyinpoker.Thereasonisthatthelargertheopera

tor and its network, the higher the liquidity of poker players there. This means players can choose be

tween more tables to find their preferred game structure and limit. Economists call this effect a (posi

tive)networkexternality(Katz&Shapiro,1985).ComparedtoPokerstarsorFullTiltPoker(atthattime),

bwinisaminorplayerinthepokermarket(Fiedler&Wilcke,2011a)meaningthatonlyfewprimarypok

erplayerswillhavesignedupwithbwinandifso,onlyasasecondorthirdchoicesitetoplayat.Conse

quently, the results of poker players gambling habits are not representative but underestimated. Al

though the present study cannot overcome the inherent problem of people playing at multiple opera

tors,thedatacomesfromthelargestpokeroperatorsand,hence,theanalyzedplayerpoolisrepresent


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for poker. First of all, it is unclear what is money wagered in poker: the money a player puts on the

tablewhichisthenatrisk,orthesumwhichheactuallyputsinthepotduringahand,oreachindividual

bet(meaningmultiplebetsperhand)?Thefollowingexamplesofdifferentplayerbetspointoutwhythe

variabletotalwageredshouldbeconsideredcarefully:

PlayerA sitsdownwithUS$100 ataNo LimitHoldemUS$0.50/US$1 table.Heplaysjustone

hand,foldshiscardsandleavesthetablewithUS$99.

PlayerBsitsdownwithUS$100ataNoLimitHoldemUS$2/US$4table.Heplaysjustonehand,

foldshiscardsandleavesthetablewithUS$96.

PlayerC sitsdownwithUS$100ataNo LimitHoldemUS$0.50/US$1 table.Heplaysjustone

hand,betsallUS$100duringthehandandleavesafterwards.

PlayerDsitsdownwithUS$100ataNoLimitHoldemUS$0.5/US$1table.Heplays100hands,

folds80timeswithoutabetting,andduringtheother20handshisbetsaccumulatetoatotalofUS$160.

PlayerEsitsdownwith100%ataLimitHoldemUS$0.50/US$1table.Heplays1hand,capsthe

bettingonallstreetstoatotalof$24andleavesafterwards.

InterpretingeachofthoseplayerstohavewageredUS$100omitsanalyzingthe levelofriskindifferent

gamesandthebettingstrategiesplayersadopt.Inaddition,moneywageredlosesvaluewithgrowing

difference betweenthe expected values of bets and between their riskiness (standarddeviation of the

outcomes).Forexample,totalmoneywageredisperfectforinterpretingmoneywageredonredina

roulettegame.Formoneywageredonredandalsonumbersinrouletteitlosessomeinformativevalue

as the riskiness of thebets differ. If now the expected valuediffers too, thevariable total money wa

geredlosesevenmoreofitsexplanatorypower.Inpokertheexpectedvaluesandtheriskinessofbets

differ greatly and the differences are aggravated by the path dependency of decisions during a poker


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3.DataandMethods

3.1TheOnlinePokerDatabaseoftheUniversityofHamburg(OPD-UHH)

Online poker is a data goldmine. All operators display a lot of information in their lobbies about the

people playing at their tables. It is easy to determine the origin of a player (city and/or country), the

game type, betting structure, the limit ofthetable they areplaying at, andof coursethetime and the

date. Financedby the city of Hamburg, the Institute of Law & Economics at the University of Hamburg

collected this data in the OPDUHH in collaboration with independent market spectator PokerScout.

Softwareelectronicallygatheredplayerdataforthefollowingpokernetworks:Pokerstars,FullTiltPoker,

Everest Poker, IPN (Boss Media) and Cake Poker. This software scanned each cash game table4 of the

aforementionedpokersitesandcopiedthedisplayedinformationintoaSQLdatabase.5

Datacollectionwasconductedforeachpokersiteoveraperiodofsixmonths,enablingdatafor

2,127,887 poker identities, including their country of origin and their playing habits, to be obtained. It

tookabouttenminutestoscanalltheoperatorstablesandcollectinformationaboutplayersseatedat

thetables.Thistranslatestoabout6datapointsperhouror25,920overthecourseofsixmonthsand

allowsnotonlytodeterminetheplayingtimepersessionoftheplayers,butalsotoanalyzedifferences

intime.TheperiodofthedatacollectionranfromSeptember10,2009toMarch11,2010.6

3.2OperationalizationofPlayingHabits

B f i th l i h bit f li k l it i ti l t ti li th diff t


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player is not clear. Whats more, operationalization helps to overcome the typical question concerning

totalmoneywageredwhichisakeyvariableformostgamblingopportunitiesbutnotforpoker.

The information in the OPDUHH can be broken down into seven different variables to analyze

theplayinghabitsofanonlinepokerplayer(theycanalsobeconnectedwiththeoriginoftheplayerto

allowcountryorregionspecificanalyses).Thesevariablesare:1)numberofsessions,2)playingtimeper

session, 3) number of tables played simultaneously in a session (multitabling), 4) game structure (for

exampleTexasHoldemorOmaha),5)bettingstructure(forexampleNoLimitorFixedLimit),6)number

ofplayers/seatsatthetable,7)thesizeofthebigblind7.Note:asessionbeginswhenaplayerwhohas

notbeenactiveinthelast20minutessitsdownatanytable.ThisisdifferentfromthestudyofNelsonet

al.(2009)whichdefinesasession(althoughnotexplicitly)asaplayerseatedatatableandbuyingchips

regardless of whether he has played immediately beforehand at a different table. In addition, the

present studys definition of a session allows to observe if a player is seated at multiple tables at the

sametime,aspecificfeatureofonlinepoker.

While analyzingeachvariable individually is interesting,theycanalso becombinedwiththe in

formationoftheplayingduration(thetimebetweenthefirstandlastobservationofaplayer).Themost

meaningful interpretations, however, are possible when the variables are connected with each other.

Variables1),2),3),and7)arequantitativeandcanthereforeberelatedtoeachother.Forexample,mul

tiplying the number of sessions with the playing time per session yields the total playing time over six


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Holdemwith9otherplayerscannotbecomparedwithplayinganhourofPotLimitOmahawith6play

ers.SomeonesittingdownwithUS$100intheFixedLimitgameisconsiderablylessexposedtoriskthan

someoneinthePotLimitOmahagame.

Hence, the qualitative variables have to be operationalized and quantified. The one thing they

have in common is that they all relate to the rake (the fee paid to the operator). Ceteris paribus: the

moreplayersatatable,thelessrakeispaidperplayertotheoperator.InOmahamorerakeispaidthan

in Holdem, in No Limit games the rake ishigher than in FixedLimit games.However,the magnitudeof

theseeffectsisnotstaticandalsodependsonthesizeofthebigblind.Hence,itisnecessarytocombine

thesethreevariableswiththesizeofthebigblind.Thisyieldstheaveragerakepaidbyaplayerper100

handsaquantitativevariable whichcanbe relatedtotheothervariablesoftheplayinghabits.These

valuesareimportantfortheplayersastheydeterminetogetherwithbonusesandrebatestheprice

theyarechargedforplayingpoker.Thus,theyalsodifferfromoperatortooperator.

No Limit Texas Holdem is by far the most popular poker variant: 58.73% play this variant (see

AppendixA).Figure1showstheaverage absoluteandrelativerake chargedbytheoperators(industry

average)forNoLimitHoldemgameswith6and10playersinrelationtothesizeofthebigblind.While

theabsoluteamountofrakepaidper100handsonalimitincreasesinthesizeofthebigblind(themon

ey at stake) it is evident that it decreases relatively to the size of the big blind. While a player at

US$0.01/US$0.02paysUS$0.25or12.5bigblindsonaverageper100playedhandstotheoperatorata


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rakeiscappedandthehigherthelimits,thetightertheplayers(meaningtheyplayfewerstartinghands)

sotheyseefewerflops,whichmeanstheypaylessrake.

Figure1:Rakepaidtotheoperatorsper100handsinNoLimitTexasHoldem(industryaverage)

Pokerisazerosumgamebetweentheplayerssotheaveragerakepaidtotheoperatorequalstheplay

ingcostsfortheaverageplayer.Whileitiscommonforpokerplayerstouserakepaidper100handsto

comparehowmuchtheyhavetopay,itismuchmorefeasibleforresearchquestionstostandardizethe

variable in time units.8 This allows ajoint analysis with the playing time of a player and a comparison

withtheexpensesforothergames likeslotmachines.Theaveragerakepaidperhourbyaplayer isan

importantvariableandshallbedenotedwiththetermplayingintensity.Thehigherthestakes,themore

handsperhourplayed,thelessopponentsfaced,theriskierthebettingstructureandthepokervariant,

0

2

4

6

8

10

12

14

16

18

20

0

5

10

15

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25

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35

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BB/100h

US$/100h

US$/100h

10max

BB/100h

10max


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Figure2showstherelationshipbetweenthedifferentvariablesoftheplayinghabits(numberof

sessions,averageplayingtimepersession,averagenumberoftablesplayedsimultaneouslyandplaying

intensity).Theycanbeaggregatedtothetopfigureplayingvolume.Thisisdefinedastheproductofthe

playingtimeovera6monthperiodtimesthenumberofaveragetablesplayedsimultaneouslytimesthe

average$rakepaidtotheoperator.Theplayingvolumeofaplayerstateshowmuchmoneyaplayerhas

paidtotheoperatorinthe6monthsoftheobservationperiod.

Figure2:Thedifferentvariablesoftheplayinghabitsandtheirrelationship

4.EmpiricalResults

4.1NumberofSessions

The total number of sessions observed over the 6 months period is 51,141,167. At 2,127,887 playing

identities9 the average number of sessions played is 24 03 As the nicknames of the player identities

PlayingvolumeNumberoftables

playedsimultaneously

Playingintensity=

$rakeperhour

Playingtime

over6months

GamestructurePlayingtime

persession

Numberofsessions

Tablesize(seats)

Bettingstructure

Bigblind


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whileasmallnumberofintenseplayersplayedfrequentlyandcreatedthelargegapbetweenthemean

and median number of sessions played. This hypothesis is strengthened by the standard deviation of

49.3sessions7timesashighasthemedian.Thegapbetweenthemeanandthemedianvaluescanbe

foundineveryvariableoftheplayinghabitsandisinvestigatedmoredeeplyineachcase.Itleadstotwo

conclusions: (1) a small group of heavily involved poker players is responsible for the majority of the

playing volume, and (2) the median values describe the gambling behavior of the typical online poker

playermoreaccuratelythanthemeanvalues.

The number of sessions played shows that a relatively large proportion of the players did not

playveryoftenoverthecourseof6months:403,592,equivalenttomorethan18%ofallplayeridenti

ties, only played once. Nearly half a million identities were observed between two and four times and

18.2%betweenfiveandtentimes.Another17.2%playedbetween11and25timeswhile10.3%ofthe

sample was observed between 2650 times. 7.1% played between 50 and 100 sessions and 3.5% be

tween100and180sessions.Agroupof2.1%ofthesamplewasseenmoreoftenthan180timesatthe

tablestheyplayedmorethanonesessionperday.

4.2Playingtimepersession

Theaveragepokerplayerstayedatthetablefor50.27minutespersession.At42minutes,themedian

playerhad anaverage session lengthof only slightly less. In comparison tothenumberof sessions the

gap is relatively small and the average is not affected by a few extremely long sessions. The standard


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another3.7%ofthesessionsalengthoftwotothreehourswasrecordedand1.1%ofthesessionslasted

morethan3hourswithoutabreak.

4.3Totalplayingtimeover6months

Asdiscussedbefore,thecombinationofthenumberofsessionsandtheirlengthsyieldsthetotal

playingtimeofaplayerovertheobservationperiodof6months.Thisispossiblebecauseeachnickname

isuniqueoneachpokerplatformandtheplayerscanberecognizedandtracked.Itisnoticeablethatthe

averageplayingtimeover6monthswas25.28hoursfortheaverageplayerwhilethemedianplayeronly

played 4.88 hours over the courseof 6 months. Hence, the average value is again impacted by a small

group of intenseplayers, ahypothesissupportedbythehugestandarddeviationof65.21hours(13.36

timesthemedianvalue).Itmeansthatthegapbetweentheaverageandthemedianvaluesofthenum

berofsessionsandtheplayingtimepersessionisamplifiedbycombiningthemtothetotalplayingtime.

Analyzingtherelativefrequencyoftheclassifiedtotalplayingtimeshowsthatalargeproportion

of the players play poker rarely: 22.9% of all players did not play for more than an hour, 27.6% of the

observed player identities played between 1 and 5 hours poker for real money over the course of 6

months,and20%haveatotalplayingtimebetween5and15hours.12.8%oftheplayerswereobserved

for 15to 35 hours and10.6%of thesample for 35 to 100 hours(which still is not tobe categorized as

excessiveifpokerisahobbyforthem).Theproportionofplayerswhospentmorethan100hoursatthe

virtualpokertableshowever,isnottobedisregarded.6.1%ofallplayershaveplayedmorethan33mi


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dotal evidence, online poker players do not tend to multitable frequently. In the average session the

playerplayedat1.31tablessimultaneouslyandinthemediansessionat1.05tables.Thegapisnotvery

largeandthestandarddeviationof1.04tablesalsosuggeststhatthe mean value isonlymarginally af

fectedbyasmallgroupplayingmanytables.Still,thereisagapbetweenamajorityplayingjustonone

orpartiallyatasecondtableandsomepeopleplayingonmoretablesregularly:10%oftheplayersplay

at1.65,5%on2.36,and1%at6.03tablesonaverage.

Analyzing multitabling not by player but by session shows that multitabling is most often not

practicedonaregularbasis(whichyieldsahighaverageoverallsessionsofaplayer)butinsteadsome

timestriedoutbya lotofplayers(yieldingonlyslightly increased averagesper sessions for many play

ers). Still, in 60.3%of allsessions the player was singletabling. In 15.8%of all sessions twotables were

playedsimultaneously. Inanother 5.8% three tableswereplayedat thesame time and 5.1%of allses

sionswereplayedatfourtablessimultaneously.Fiveorsixtableswereobservedin4.4%,sevenoreight

tablesin2.2%,andnineto12tablesin3.1%ofallsessions.In3.2%ofthesessionstheplayerplayedat

12ormoretablesatthesametime.Hence,massivemultitablingisnotexercisedregularlybymanyplay

ersbutonlysometimesbyseveralplayers.

Giventhatonaverageabout70handsareplayedpertableperhour,aplayerplayingat12tables

simultaneouslycompletes840handsperhouror14perminute.Whilemostcombinationsofcardsare

foldeddirectlyandonautopilotbythepracticedplayer,ittakesalotofefforttoanalyzetheinforma


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tothinkaboutasinglepokerhandforacoupleofhours.Hence,playing14handsperminuterequiresa

veryhighamountofconcentrationandfocusorontheotherhandsuggestsrecklessness.

4.5Playingintensity

Theplayingintensityisdefinedastheaveragerakepaidbyaplayerperhourtotheoperator.Itdepends

on thegame structure(forexampleHoldemor Omaha),thebettingstructure(forexampleNoLimit or

FixedLimit),thenumberofplayersseatedatthetable,andthesizeofthebigblindcorrespondingtothe

money at stake. The playing intensity is the cash flow from the players tothe operator and equals the

average lossperhour of an average skilled player. The average playing intensity was US$2.40perhour

per table. The median player paid considerably less rake: US$0.87 per hour per table. Paired with the

relativelylargestandarddeviationofUS$4.46(5.06timesthemedianamount)thisleadstotheconclu

sionthatthereisasmallgroupofplayerswithahighplayingintensitywhodrivethemeanvalue.Com

paredwiththecostofothergamblingopportunitieslikeslotmachines,onlinepokerisquiteinexpensive

(for most players). Key reasons for cheap offers are operators situated in small countries (tax oases)

whopaylowtaxesandaverysmallfeeornothingatallfortheirlicense.Butthemainreasonisprobably

thatthemarginalcostsoftheoperatorare(nearly)zerobecausetheydonothavetopaydealersorcov

er rent costs. Instead they use scalable software which costs the same, regardless of how many tables

areoffered.

Nearlyeveryfifthonlinepokerplayer(19.9%)paysUS$0.20orlessperhourpertabletotheop


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sample.Another11.3%paybetweenUS$515andonly2.0%ofallplayeridentitieswereobservedtopay

morethanUS$15perhourpertable.

4.6Topfigure:Playingvolumeover6months

Themultiplicationofthetotalplayingtime,multitablingandplayingintensityyieldstheplayingvolume.

Itisthetopfigureregardingplayinghabitsandstateshowmuchmoneyaplayerhaspaidtotheoperator

overtheobservationperiodof6months.Theaggregatedplayingvolumeofallplayersequalstheopera

torsrevenuesandtheplayerslosses.

Whiletheanalysisoftheindividualvariablesoftheplayinghabitswasalreadygreatlyinfluenced

byasmallgroupofheavily involvedpokerplayers,thisfindingbecomesevenmoreevidentthroughan

analysisofplayingvolume.Thetotalobservedplayingvolumeover6monthsforallplayerswasUS$378

million.10ThisleadstoanaverageplayerlossofUS$177.51.Theemphasis,however,isthehugegapbe

tweenthemeanandthemedianplayingvolume:50%ofthesamplepaidonlyUS$4.86over6monthsto

the operators. The standard deviation of US$1,935 is 398 times the median amount and amplifies this

difference. It can only be explained by a small group of players who have a huge playing volume and

strongly impact theaverage value. These figures suggest that there is asmallgroup ofexcessive poker

players. Thishypothesis issupportedbyfurther evidence inthissubsectionbeforethegroupofheavily

involvedplayerswillbeanalyzedseparatelyinthefollowingsection.

29.8% of all player identities paid less than US$1 rake over 6 months. Their playing volume is


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paid between US$5 and US$15 is very small. Relative to the observation period of 6 months even ex

penses of US$1550 by approximately 200,000 players (14.2%) is not much. Nearly every tenth person

(9.4%)hasaplayingvolumeofUS$50150over6monthswhichcannotbedisregardedbutisnotexces

sive either and is still below the average value. 6.3% of the players paid between US$150 and US$500

raketotheoperatorsandtheyarepotentiallyatrisk.4.7%ofthesamplepaidmorethan$500.Giventhe

smallfeesinonlinepoker,theirplayingvolumecanbecalledexcessive.

Before analyzing the group of the intense players in more detail in the next section, it is to be

highlightedthattheplayingvolumeofaplayerequalsthepaymenttotheoperatorbutdoesnotequal

theplayerslosses.Playerscanalsolosemoneytotheiropponents(orwinfromthem).Presumably,un

trainedplayerswhoplaypokerinfrequentlylosemoneyonaveragetotheiropponentswhilethetrained

playersusuallywin(forempiricalevidence,seeFiedler&Rock2009).Hence,playerswithalowplaying

volumetendtohavehigherlossesthantherakepaidtotheoperators,whileplayerswithahighplaying

volumehavelessexpensesorevenwinnings. Forthisreason,aninterpretationofanindividualsplaying

volume as his total losses is not meaningful. The playing volume can only be interpreted as players

losses when aggregated. Still, the use of playing volume to determine the involvement of an individual

playerisreasonable.Itallowstheconclusionthatmostplayershaveasmallplayingvolumeandarenot

atrisktodevelopanaddiction,whileasmallgrouphasanexcessiveplayingvolumeandmaybepatho

logicaland/orprofessionalgamblers.


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number of sessions yieldsan average of .74and a medianof .60 sessions per day. This suggests sup

portedbytherelativelylowstandarddeviationof.66sessions/daythateventhemostintenseplayers

donotplaymuchmoreoftenperdaythanrecreationalplayers.On theotherhandthisvaluemightbe

biasedbythegroupofplayerswhowereonlyobservedononedayandstoppedplayingthereafter.They

haveasessions/dayratioof at leastoneandaccountformorethan20%ofthesample.This isa draw

back not inherent to the ratio playing time/playing duration. On average the sample played 38.70 mi

nutesperday. The median value is 20 minutes perday and the standarddeviation53.62 minutes/day.

Herewefindagainthattheaverageisstronglyaffectedbyasmallgroupofplayerswithahighexposure.

The most interesting combination is playing volume per playing duration. The average rake/day is

US$2.48andmorethan9timeslargerthanthemedianvalueofUS$.27/day.Thissuggests,again,thata

small group of players account for mostof theplaying volume. However, although the standard devia

tionof14.44US$/dayisrelativelyhuge,itisnotaslargecomparedtothemedianvalueasintheanalysis

ofplayingvolumewithoutconsiderationoftheplayingduration(53.5xto398x).Thisleadstotheconclu

sion that the small group of the most involved poker players dominate in every variable of the playing

habits.

4.8Relationsbetweenthedifferentvariablesofthegamblinghabits

Theabove resultssuggestthatthe variables ofthe playing habits reinforceeach other. This hypothesis

canbetestedbyanalyzingtherelationshipbetweenthemwhichallowsconclusionsaboutwhetherthey

reinforce each other or there is a moderating variable to be drawn In fact it is obvious that total playing


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variablesarenotnormallydistributed(allsignificantatp


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Analyzing the relationship between the combination of playing habits with playing duration

yieldsimportantresults.Thecorrelationbetweensessions/dayandplayingdurationisstronglynegative.

Thismeansthatthehigherplayingfrequencyofaplayerthemorelikelyheistostopgambling.Withthe

exception of the correlation to session lengths, sessions/day shows a weak negative correlation to all

other playing habits. This means that playing very often in a short periodof time reduces overall gam

blinginvolvement.Thisfindingmightbecounterintuitivewhenitcomestopathologicalgambling.Butit

is reasonable for recreational players who have a given limit for their expenses and stop when it is

reached (they reach it faster when they play more frequently). However, playing frequently does not

mean playing long sessions. And the correlations of the time spent playing poker per day are different

fromthoseofsessions/day.Whiletime/dayisnegativelycorrelatedtoplayingintensityandplayingdura

tion it is positively related to the other playing variables. Rake/day is also positively related to all va

riablesoftheplayinghabitswiththeexceptionofplayingduration.Overall,itcanbeconcludedthatthe

only moderator for gambling involvement is playing frequency while all other playing habits reinforce

eachother.

4.8Thegroupofintenseplayers

Theplayinghabitsofintenseplayersdifferfromthoseofcasualplayers.Table2presentsasummaryof

theresultsforthedifferentvariablesofplayinghabitsandcomparesthemeanandmedianwiththosein

thetop10%,top5%andtop1%players.


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Theincreaseinthesessionlengthfromthemedianplayer(42minutes)totheintenseplayersis

moderate. The top 10% player played 94.8 minutes on average per session, the top 5% player 119 mi

nutes and the top 1% player 182 minutes. The increase is considerably higher with the number of ses

sions.Whilethemedianplayeronlyplayed7sessionsoverthecourseof6months,thetop10%player

played63,thetop5%player108,andthetop1%player247sessions.Hence,itcanbededucedthatthe

huge difference between the total playing time of the median player (4.88 hours) and intense players

(63,118and318hours)isduetothenumberofsessionsandonlyslightlyaffectedbytheplayinglength

per session. Multitabling is uncommon among recreational players and median players only play 1.05

tables at the same time, but it is common among intense players: the top 10% player played 1.65, the

top 5% player 2.36 and the top 10% player 6.03 tablessimultaneously. The ratio intenseplayer to me

dian player is also notable when it comes to playing intensity. While the median player pays US$0.87

rakeperhourtotheoperator,thetop10%playerpaysUS$6.12,thetop5%playerUS$9.90,andthetop

1%playerevenUS$19.75ornearly21timesthemedianamount.Combiningplayinghabitswithplaying

volume widens the gap between median and intense players greatly. The median player paid US$4.86

raketotheoperatorsover6monthsandthetop10%playeralready36timesasmuch(US$174)which

equals the average of US$178. The average is mainly driven by the most intense players. The top 5%

playerwasobservedtohavepaidUS$460andthetop1%playerevenUS$2,685552timesthemedian

amount. The analysis by percentiles supports the evidence that most online poker players only have a


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Hence,theoperatorneedsmorethan500recreationalpokerplayerstogetasmuchrevenueas

hegetsfromoneveryintenseplayeranditcanbeconcludedthattheoperatorsgeneratemostoftheir

revenuefromtheintenseplayers.Thisfindingisvalidatedbythecomparisonoftheaggregatedplaying

volumeoftheintenseplayerstothewholesample(seetable3).10%oftheplayersaccountfor91.06%

ofallrakepaid,5%for83.1%andstillmorethanhalfofeachdollar(59.59%)isgeneratedbyjust1%of

theplayers.Theirshareofthetotalexpensesismorethanthe80/20Paretoprinciple,whichstatesthat

for most consumer goods about 80% of the revenues come from 20% of the customers. Viewing such

numbersinthecontextofgambling,thefirstideathatcomestomindisthattheintenseplayersareei

therpathologicalgamblersoratriskofbecomingpathological.Butthisconclusionmaybeprematurein

thelightoftheskillelementinpokerandtheprofessionalplayers.

Table3:Aggregatedplayingvolumeoftheintenseplayersandtheirshareofthetotalplayingvolume

Playergroup Playingvolumein$rakepaid Shareoftotalplayingvolume

Top1% 225,086,489 59.59%

Top 5% 313 888 432 83 10%

0.1 0.2 0.4 0.7 1.1 1.6 2.4 3.4 4.8 6.7 9.4 13 19 27 41 65 89174 204 243

294 362460

608852

1334

2685

0

500

1000

1500

2000

2500

3000

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 91 92 93 94 95 96 97 98 99

$rake

Percentile


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5.Discussion

and

Perspectives

5.1Intensepokerplayersaretheypathologicalgamblers?

One major challenge arises when analyzing games with skill elements. In poker and to a somewhat

lesserextent insportsbettingthe influenceofskill is largeenoughthatprofessionalscanplaywitha

positiveexpectedvalueandwininthelongrun(seepokertableratings.comandsharkscope.comforthe

resultsofprofessionalpokerplayers).Skillmattersagreatdealinthegameofpoker(Cabot&Hannum,

2005).Playershaveseveralpossibilitiestoinfluencetheoutcomeofthegame.Theseare:folding,calling,

betting,raising,andreraisingbefore theflop,ontheflop,ontheturn,andontheriver.Ifthegame is

played as No Limit, the player can also decide how much to bet, raise, or reraise. These decisions de

pend on many influential factors, such as the position at the table, the size of the pot (pot odds), the

rangeofthepossiblehandsoftheopponent(s)and,ofcourse,onthecardsoftheplayerandthecom

munitycards.Theskillinpokeristointerpretandweighupthesefactorsaccordinglyandthenmakethe

bestdecisions(Fiedler&Rock2009).

Inpoker,relativeskillmatters(Dreefetal.2003).Therearerelativelyskilledplayerswhoconsis

tently win money from their opponents and relatively unskilledplayers who lose this money (although

thisgroupmaybeskilledinrelationtootherplayers).Duetothefeesinformofraketheplayershaveto

paytotheoperator,mostplayersloseoverall,includingthosewhoarebetterthantheiropponents.Still,

thereareplayerswhoaresoskilledthattheyovercompensatethisdisadvantageandwinmoneyinthe


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not attractive for purely financial reasons or they fulfill the peterprinciple and are water boys who

climb to limits where more money is at stake but where they are not good enough any longer to win.

Still, theyprobably play morethanthe averagerecreational player. Thegroup of the semiprofessional

playersconsistsofindividualswhoseskillissufficienttohavesuccessinafinanciallymeaningfulcontext.

However,the people in this grouphaveafulltimeoccupation.Hence, theyonlyplay intheirfreetime

butonhigherlimitsthanthesuccessfulrecreationalplayersandtheyseepokerasalucrativepossibility

foranadditionalsourceofincome.Thegroupoftheprofessionalplayersisverysmall.Itconsistsofplay

erswhoaresufficientlyskilledtoconsistentlywinmoneybyplayingpokertoanextentthattheydonot

needanotherjob.Theyarenotnecessarilymoreskillfulthantheplayersinthesemiprofessionalgroup

buttheyspendconsiderablymoretimeplayingpokerandregarditastheirjob.Alloftheseplayershave

an incentivetoplayoften and(andfor largeramounts) andahigherthanaverageplayingvolume.This

maybe reachedby playing high limits, playing manytables,manyor long sessions or a combination of

these. Allsemiprofessionalsandprofessionalsandalargenumberofthesuccessfulrecreationalplayers

haveahighinvolvementandcanbefoundinthegroupofintenseplayers.Hence,theyaffectthedis

tributionsoftheplayingvariables.This isa hugeproblemwhentryingto identifyexcessiveorevenpa

thologicalpokerplayers(andsportsbettors)bytheirplayingvolume.

Ontheotherhand,notallintenseplayersarewinningplayerswhichindicatesthatalsopatholog

ical players are in the group of intense players. Thus, the question is how many players of the intense

playersarepathologicalandhowmanyareprofessionals,andalsowhethertheseplayersareonlygood


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lyzesactualplayingbehaviorinmoredetail.Untilsuchresearchisavailable,itcanonlybesuspectedthat

thegroupoftheintenseplayersmostlyconsistsof(semi)professionalplayers,pathologicalplayersand

(semi)professionalplayerswhoareaddictedtopokerbuthavenotsufferedanynegativefinancialcon

sequences(yet).

5.2Limitations

Althoughthestudyyieldsmanyfindingstherearesomelimitations.Pokerplayerscaneasilyplayonmul

tiple sites and, somewhat less likely, on the same site with multiple user names. This data set cannot

take this fact into consideration and as a consequence every observed nickname at each site is inter

preted separately. Thus, players with multiple accounts are interpreted as multiple players. This is a

probleminherenttoallanalysesofactualplayingbehavior:theyarealwayspartialanalysesasgambling

behavioratdifferentlocationsorgamesisnotrecorded.Underestimationistheresult.Forthisstudy,it

mainlyaffectstheplayingbehaviorofintenseplayersastheyaremostlikelytoplayatmultiplesites.On

the other hand, it may also be possible that more than one person uses the same player identity (ac

countsharing),forexamplefriendsorfamilymembers.

A more importantlimitation isthat cash flowsbetweentheplayerswerenotobserved.Thus, it

cannot be determined whether a player is winning or losing. However, this is important information

whichwouldhelptogiveaclearerinsightintohighvolumeplay.Itwasshownforexample,thatplayers

whoplaymoreoftenloseless(Nelsonetal.,2009)andevenwin(Fiedler&Rock,2009).Thus,itcanbe


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firsttodothisinaseriesofninepapers.Onegroupofpapersdescribedgamblingbehaviorandthemain

conclusionwasthatmostplayersdonotplayveryoften,whileasmallgroupplays intensely.However,

the conclusions for the poker players by Nelson et al. (2009) have to be considered carefully because

thesedatasetsarenotrepresentativeasbwinismainlyasportsbettingoperatorandonlyofferspoker

ontheside.Furthermore,theauthorsdidnotaddresstheroleofskillinpokerwhichcanleadtoprofes

sionalgamblersinfluencingthevariablesofgamblingbehavior.

This paperadvancesresearch inthisfieldforwardbyanalyzingactualgamblinghabitsofonline

poker players by means of a large and unbiased sample of 2,127,887 player identities from the Online

PokerDatabase of the University of Hamburg(OPDUHH) whowere trackedover 6months at fivedif

ferentpokeroperators.Inadditiontoaplayerscityorcountryofresidence,softwarerecordedwhosits

athowmanyandwhatkindoftableseverytenminutes.Thisdatawasoperationalizedintothefollowing

variables:numberofsessions,timespentpersession,totalplayingtimeandtheplayingintensityinform

of $ rake paid perhourand tableto theoperator. This way ofoperationalizing thevariables of playing

habits makes sense, not only against the background that poker is a game between players and not

against the house, but also because the variables of the playing habits can be analyzed in isolation as

wellasincombinationwitheachother.Thisallowsthekeyfiguretotalplayingvolumetobedefined,

indicatinghowmuchrakeaplayerhaspaidtotheoperatoroveragiventimeframe(here6months).

ThemainfindingconfirmstheresultsoftheHarvardstudies:mostonlinepokerplayersonlyplay


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ly.Hence, the totalplaying volume ofthe median player is also very low: more than 50% of all players

paidlessthanUS$4.86inraketotheoperatorsover6months.However,theaveragevaluesoftheplay

inghabitsareconsiderablyhigherthanthemedianvaluesandtheyarehighlyaffectedbyasmallgroup

of intense players. For example, the 99% percentile player hasa552timeshigher playingvolume than

themedianplayer(US$2,685).ThisisavaluemuchhigherthanthatfoundbyNelsonetal.(2009).This

smallgroupofplayersaccountsformostoftheplayingvolume:operatorsearn59.6%oftheirrevenues

fromonly1%ofthesample.5%oftheplayersaccountfor83.6%and10%for91.1%ofplayingvolume.

Thegroupofhighvolumeplayersisnotonlyinterestingfortheindustrybecauseoftherevenue

they generate but also for research on gambling addiction. However, it is wrong to label every one of

themasa(probable)pathologicalgambler,becauseinthelongrunskillplaysakeyrolefortheoutcome

inpoker.Sophisticatedplayersareabletoplaywithapositiveexpectedvalue.Thus,incontrasttotypical

gambling where no skill is involved, the group of intense players in pokerconsists of pathologicalgam

blers aswellas(semi)professionalplayersearninga livingbyplayingpoker.Whenanalyzingpokerit is

importanttokeepthisinmind.Consequently,itisimportantthatfutureresearchaddressestheissueof

areliabledistinctionbetweenprofessionalandpathologicalpokerplayers.Therearetwodifferentalter

nativestoaccomplishthisgoal.Oneapproachistodigdeeperintotheactualbettingdecisionsofpoker

players (or other gamblers) to find tendencies of chasing, reinforcement or irrationality. The other ap

proachistocombinedataonplayinghabitswithinterviewdata.Bothideasseempromisingandcapable

ofpushingtheboundariesofcurrentresearchforward.


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Blaszczynski,A.(1999).Pathologicalgamblingandobsessivecompulsivespectrumdisorders.Psychologi

calReports,84,107113.

Braverman, J., & Shaffer, H. J. (2010). How do gamblers start gambling: Identifying behavioral markers

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Broda,A.,LaPlante,D.A.,Nelson,S.E.,LaBrie,R.A.,Bosworth,L.B.,&Shaffer,H.J.(2008).Virtualharm

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Cabot, A. & Hannum, R. (2008). Poker: Public Policy, Law, Mathematics and the Future ofan American

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EUCommission(2011).OnonlinegamblingintheInternalMarket,GreenPaper.

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the gambling habits of online poker players

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