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TheGamblingHabitsofOnlinePokerPlayersSeptember29,2011
IngoFiedler*
ingo.fiedler@public.unihamburg.de
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$0.25or12.5bigblindsonaverageper100playedhandstotheoperatorata
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rakeiscappedandthehigherthelimits,thetightertheplayers(meaningtheyplayfewerstartinghands)
sotheyseefewerflops,whichmeanstheypaylessrake.
Figure1:Rakepaidtotheoperatorsper100handsinNoLimitTexasHoldem(industryaverage)
Pokerisazerosumgamebetweentheplayerssotheaveragerakepaidtotheoperatorequalstheplay
ingcostsfortheaverageplayer.Whileitiscommonforpokerplayerstouserakepaidper100handsto
comparehowmuchtheyhavetopay,itismuchmorefeasibleforresearchquestionstostandardizethe
variable in time units.8 This allows ajoint analysis with the playing time of a player and a comparison
withtheexpensesforothergames likeslotmachines.Theaveragerakepaidperhourbyaplayer isan
importantvariableandshallbedenotedwiththetermplayingintensity.Thehigherthestakes,themore
handsperhourplayed,thelessopponentsfaced,theriskierthebettingstructureandthepokervariant,
0
2
4
6
8
10
12
14
16
18
20
0
5
10
15
20
25
30
35
40
BB/100h
US$/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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