The Market for Online Poker Ingo Fiedler Ann-Christin Wilcke

Abstract The recent events of the “Black Friday” – the biggest online poker networks in the USA were shut down – demonstrated the necessity to make decisions about the regulation of online poker. But although online poker is a gold mine of data, until now nobody knows where the players and their money come from. It seems that the knowledge about the online poker market has not been able to keep up with the speed of its evolution in the past years. This paper is the first to shed light on this matter. We use data of 4,591,298 poker identities from the Online Poker Database of the University of Hamburg (OPD-UHH) collected over a six months period from 09/2009 to 03/2010. We find that the worldwide 6 million players paid 3.61 billion US$ rake to the operators in 2010. USA is still by far the biggest market with 1,429,943 active players and 973.3 million US$ net revenues in 2010. With regard to the number of internet users in a country, Hungary is the biggest relative market: One out of 50 Hungarians with internet access plays online poker for real money. The two main drivers of the relative market size in a country are GDP per capita and culture. Using the data from the OPD-UHH, future research will be able to break down the market also on a regional level within countries and to examine inter and intra country differences in the playing habits of online poker players. Keywords: online, poker, market, gambling, data, habits.

Introduction Online gambling and online poker in particular is a relatively new phenomenon nearly nonexistent in 2003. During the past years it has grown extraordinarily so that it is now an important factor in the whole gambling market. However, even today it is often neglected by the old industry, the legislators and the researchers as an unwelcome black market just as if denying would cease its existence. But the economic reality is different. Online gambling has tremendous cost advantages due to electronic instead of brick and mortar operation and, even more important, its operators do not have to pay (high) taxes and license fees. In the case of poker, large player pools and the corresponding network effects have also helped the game to grow to a size not to be matched in the offline world. From an academic perspective, most research in the field of poker has focused on playing strategy (e.g. Chen & Ankenman, 2006) and especially the question whether poker is a game of skill or a game of chance (see e.g. Dreef et al. 2003, Cabot & Hannum 2005, Dedonno & Detterman 2008, or Turner 2008 and for an empirical point of view see Fiedler & Rock 2009). The answer to this question is especially important from a legal perspective as most jurisdictions legalize games of skill while they regulate games of chance. However, the skill/chance debate has not yet come to an end, and also skill does not seem to be the best criterion for the distinction between harmful and non-harmful Ingo Fiedler games (Rock & Fiedler 2008). Research Associate The recent events of the “Black Friday” – the biggest online poker networks in Institute of Law & Economics the USA were shut down – demonstrated the necessity to make decisions about the University of Hamburg regulation of online poker. Should it be prohibited? If so, what are the best instruments to enforce a prohibition? Or should online poker be legalized like Italy and France Ann-Christin Wilcke Research Associate have chosen lately? If so, what is the optimal tax and how could player protection be Institute of Law & implemented? Before answering these questions it may be worthwhile to gather more Economics information about the national markets as information about them is practically non- University of Hamb UNLV Gaming Research & Review Journal ♦ Volume 16 Issue 1 7 existent. We use data from the Online Poker Database of the University of Hamburg (OPD- UHH), a database including information on the origin and playing habits of 4,591,298 poker identities over a six months period. Before describing the data in the OPD-UHH we analyze the worldwide online poker market and the market shares of the various poker operators. We The recent events of the “Black then present the results about the size of the whole market and for Friday” – the biggest online the ten biggest markets in dollar terms. Afterwards we analyze poker networks in the USA were the prevalence of online poker in absolute terms in relation to the internet users. We also identify the main drivers of the relative shut down – demonstrated the market size of a country. Both sections provide new and important necessity to make decisions about insights into the online gambling market especially for legislators. the regulation of online poker. We continue by highlighting the limitations of this study before we summarize the results and give an outlook on further research in the last section.

Market Size and Market Shares by Operators With the exception of an analysis of poker marke ts in the European Union (Fiedler & Wilcke 2012) academic research on the prevalence and size of online poker markets isthe still observation missing. of theHowever, industry bydue PokerScout to the observation which tacks the of activethe industry players at by the PokerScoutdifferent operators which at tacksany given the moment active theplayers market at shares the differentof the operators operators are known. at any Table given 1 shows moment the market the marketshares for shares the ofnine the biggest operators poker sites are inknown. 2008, 2009 Table and 1 2010 shows (until the August). market The shares market for shares the are nine computed biggest in poker sites in 2008, 2009 and 2010 (until August). The market shares are computed in relation relation to the monthly average players of the total market: 53,121 in 2008, 68,483 in 2009 and 71,441 in to the monthly average players of the total market: 53,121 in 2008, 68,483 in 2009 and 71,4412010. Note in that2010. a player Note is thatnot counted a player in this is notstatistic counted unless inhe thisis playing statistic at the unless moment he of isthe playing scan by at thePokerScout. moment The of average the scan number by PokerScout.accounts for all The differences average in actinumberve players accounts per daytime, for all week differences days inand active months. players per daytime, week days and months.

Table 1

Market shares in the online poker industry in 2008, 2009, and 2010

a 2008 2009 2010 Data recorded for Site/Network Market share Market share Market share OPD-UHH Pokerstars 30.53% 36.05% 40.96% Yes Full Tilt Poker 14.65% 19.45% 21.73% Yes iPoker Network 9.82% 8.40% 5.77% No Party Poker 8.72% 7.22% 6.12% No Cereus Networkb 2.35% 3.40% 3.03% No Everest Poker 4.85% 3.36% 1.80% Yes Microgamingc 1.84% 2.71% 2.66% No IPN (Boss Media)d 2.81% 2.80% 2.88% Yes Cake Poker Network 2.18% 2.45% 1.97% Yes Ongame (bwin)e 6.86% 3.96% 3.55% No Other 15.39% 10.20% 9.53% No a: 2010 includes only data from January to August. b: Cereus merged with Ultimate Bet in 12/2008. c: Microgaming merged with Ladbrokes in 03/2009. d: IPN (Boss Media) merged with Cryptologic 04/2009. e: Ongame (bwin) merged with Betfair in 08/2010.

TheThe online online poker poker industry industry is highly is dominatedhighly dominated by the two biggestby the providers: two biggest Pokerstars providers: and Full Tilt Pokerstars and Full Tilt Poker, and their importance has increased over the past years. Poker, and their importance has increased over the past years. In 2008 they had a combined market share In 2008 they had a combined market share of 45.18% while in 2010 they already accountedof 45.18% while for innearly 2010 theytwo already thirds accounted (62.69%) for of nearly the twowhole thirds playing (62.69%) volume of the whole in the playing market. Correspondingly,volume in the market. theCorrespondingly, smaller operators the smaller lost operators significance lost significance and dwindled and dwindled not onlynot only in relativein relative size but also in absolute size. While in 2008 on the average 29,121 players were actively playing 8on smaller UNLVnetworks Gaming at any given Research moment, & Review the number Journal decreased ♦ Volume to 26,655 16 Issue in 2010. 1

3 size but also in absolute size. While in 2008 on the average 29,121 players were actively playing on smaller networks at any given moment, the number decreased to 26,655 in 2010. This development shows the power of positive network effects in the online poker market. The bigger the player pool, the more players are attracted to it just by its mere size. As a player, you will more likely find (weak) opponents on a large network for your favorite game type, betting structure and limit. This is even more true for players who play multiple tables at the same time (“multitabling”) and therefore generate significantly more traffic than players who only play at one table. Another important factor reason for the large and increasing market share of PokerStars is the price of the product in form of the rake structure, which is cheapest for the players at Pokerstars. But this only applies to the gross costs without considering rakeback or promotions which are used to set incentives for players to play more often and stick to one site. Rakeback and bonuses effectively reduce the costs for the players and are, hence, also an important This development shows the power of positive network effects in the online poker market. The bigger factor which drives market shares. Other factors include the quality of the software, security,the player pool,reputation, the more ease players of depositsare attracted and to itwithdrawals, just by its mere and size. also As ainteractions player, you will between more likely thefind online(weak) opponentssite and offline on a large casinos network (e.g. for yo satellitesur favorite into game live type, events betting like structure the andWord limit. Series This ofis Poker).even more true for players who play multiple tables at the same time (“multitabling”) and therefore generateKnowing significantly the market more traffic shares than of players the different who only operators, play at one thetable. next Another step importantis to look factor at the revenue which is generated in the market. The gambling consultant company H2GC reason for the large and increasing market share of PokerStars is the price of the product in form of the estimated the size of the online poker market in 2009 to be 2.4 billion US$ (H2GC 2009).rake structure, Another which way is cheapestto determine for the theplayers revenues at Pokerstars. is to lookBut this at theonly revenues applies to thestated gross in costs financialwithout considering reports rakebackof stock or listed promot companiesions which areand used extrapolate to set incentives it in relationfor players to to its play market more share.often and For stick example, to one site. PartyGaming Rakeback and reported bonuses effectively to have earnedreduce the together costs for with the players its subsidiary and are, PartyPoker,hence, also an 196.7important million factor US$which net drives revenues market shares.in 2009 Other (Partygaming factors include 2010). the qua lityExtrapolated of the with the 7.22% average market share of PartyPoker in 2009, the total market for online software, security, reputation, ease of deposits and withdrawals, and also interactions between the online poker was 2.7 billion US$, close to the estimate of H2GC. site Butand offlinethese casinosfigures (e.g. are satellites only estimates. into live events They like also the doWord not Series answer of Poker). where the money comes from and which the biggest national markets with the most players are. What Knowing the market shares of the different operators, the next step is to look at the revenue which is was the size of the US-market before the Black Friday? Was it still the largest market althoughgenerated inthe the legislator market. The tried gambling to enforce consultant a prohibition company H2GC of online estimated gambling the size of with the online the poker Unlawfulmarket in 2009 Internet to be 2.4Gambling billion US$ Enforcement (H2GC 2009). Act Another (UIGEA) way to since determine 2006? the revenuesThese are is toquestions look at wethe revenuesaddress statedwith inthe financial data from reports the of OPD-UHH stock listed companies which we and describe extrapolate next. it in relation to its market share. For example, PartyGaming reported to have earned together with its subsidiary PartyPoker, The Online Poker Database of the University of Hamburg (OPD-UHH) 196.7 million US$ net revenues in 2009 (Partygaming 2010).1 Extrapolated with the 7.22% average Online poker is a goldmine of data as all operators show in their lobby a lot of informationmarket share of about PartyPoker the people in 2009, playing the total atmarket their for tables. online It poker is easy was to2.7 observe billion US$, the close origin to the ofestimate a player of H2GC. (city2 or country ), the game type, betting structure and limit of the table they play at, and, of course, the date and time. Financed by the city of Hamburg, the InstituteBut these of figuresLaw & are Economics only estimates. at the They University also do not ofanswer Hamburg where thecollected money comes these fromdata and in which collaborationthe biggest national with markets the independent with the most market players are.spectator What was PokerScout the size of the in USthe- marketOPD-UHH. before the A softwareBlack Friday? electronically Was it still the gathered largest market the data although of the the players legislator for tried the to followingenforce a prohibition poker networks: of online Pokerstars,gambling3 with Full the TiltUnlawful Poker, Internet Everest Gambling Poker, Enforcement IPN (Boss Act Media) (UIGEA) and since Cake 2006? Poker. These This are software scanned each cash game table of the mentioned poker sites and wrote the questions we address with the data from the OPD-UHH which we describe next. displayed information into a SQL database. The data collection was conducted for each poker site during a period of six months. It took about ten minutes to scan all tables of an operator and collect the information about the players sitting at the tables. This implies about 6 data points per hour or 25,920 in the course of six months and allows not only to determine the session lengths of the players but also to analyze differences in time. The main period of the data collection

1 The gross revenue was 250.9 million US$. 54.2 million US$ were paid in bonuses to the players (so called “rakeback”), meaning that the players got an average effective discount on the price of 21.6%. 2 Note that the extrapolation assumes that the revenues per player of PartyPoker are representative for the industry. 3 As the UIGEA applies to the definition of remote gambling in the Wire Act it does not necessarily include poker. UNLV Gaming Research & Review Journal ♦ Volume 16 Issue 1 94

The Online Poker Database of the University of Hamburg (OPD-UHH) Online poker is a goldmine of data as all operators show in their lobby a lot of information about the people playing at their tables. It is easy to observe the origin of a player (city or country4), the game type, betting structure and limit of the table they play at, and, of course, the date and time. Financed by the city of Hamburg, the Institute of Law & Economics at the University of Hamburg collected these data in collaboration with the independent market spectator PokerScout in the OPD-UHH. A software electronically gathered the data of the players for the following poker networks: Pokerstars, Full Tilt Poker, Everest Poker, IPN (Boss Media) and Cake Poker. This software scanned each cash game table5 of the mentioned poker sites and wrote the displayed information into a SQL database.6

The data collection was conducted for each poker site during a period of six months. It took about ten minutes to scan all tables of an operator and collect the information about the players sitting at the tables. This implies about 6 data points per hour or 25,920 in the course of six months and allows not only to determine the session lengths of the players but also to analyze differences in time. The main period of tookthe data place collection during took September place during 2009 September and March 2009 and2010. March Table 2010. 2 depictsTable 2 depictsthe exact the exactstarting starting and endingand ending points points for for each each site.site. NoteNote that that the the period period of data of datacollection collection had to be had extended to be extendeddue to technical due toproblems technical such problems as downs ofsuch the server,as downs software of the updates server, and software disconnections. updates and disconnections.

Table 2

Starting and ending points of the data collection for each site

Poker site Start End PokerStars 09/10/2009 03/11/2010 Full Tilt Poker 09/06/2009 03/11/2010 Everest Poker 08/13/2009 03/11/2010 IPN (Boss Media) 07/27/2009 02/02/2010 Cake Poker 11/01/2009 07/02/2010 The Online Poker Database of the University of Hamburg (OPD-UHH) OnlineIn the poker6 months is a goldmine of the dataof data collection as all operators we obtained show in their data lobby for a4,591,298 lot of information poker about identities the includingIn the 6 theirmonths country of the data of origincollection and we their obtained playing data forhabits. 4,591,298 Regarding poker identities the market4 including shares their of people playing at their tables. It is easy to observe the origin of a player (city or country ), the game type, thecountry observed of origin poker and their sites playing these habits.are 64.72% Regarding of allthe pokermarket playersshares of worldwide the observed (88.27% poker sites of these the US-playersbetting structure and and 57.4% limit of of the the table players they play from at, and, all ofother course, countries) the date and who time. were Financed playing by theduring city theof Hamburg, period of the the Institute data ofcollection. Law & Economics If this numberat the University is extrapolated of Hamburg to collected the total these market, data in we getcollaboration a number with of the7,094,095 independent different market spectatorplayer identities. PokerScout Notein the OPDthat -oneUHH. real A software person can have multipleelectronically4 The poker player sites gathered Full identities Tilt the Poker, data if Everestof he the opens pl Pokerayers accountsand for IPNthe following(Boss with Media) more poker quote than networks: the one country operatorPokerstars, of origin and Fullwhile manyTilt Poker Stars playersand Cake havePoker triedspecify out the citymore of originthan ofone the pokerplayer. site and therefore have multiple accounts.5 Poker,5 Everest Poker, IPN (Boss Media) and Cake Poker. This software scanned each cash game table of However, Play money to tabl bees countedhave not been multiple taken into times consideration. in the OPD-UHH he has to have played with these 6 For a more detailed description regarding the technical approach of the data collection see6 Sakai/Haruyoshi 2005. accountsthe mentioned actively poker sitesduring and thewrote data the collectiondisplayed information period. This into amay SQL bedatabase. true for three groups of players: 1) High volume players (professional and addicted gamblers), 2) Players on the 5 The data collection was conducted for each poker site during a period of six months. It took about ten high limits as they may need more than one site to find opponents and 3) People trying outminutes different to scan sites all tables to see of an which operator one and suits collect them the informationbest. The highabout volumethe players players sitting athave the tables.a huge incentiveThis implies to about play 6 atdata just points one per network hour or 25,9as the20 inbonuses/rebates the course of six monthsthey can and get allows increase not only with to theirdetermine playing the session volume. lengths Players of the onplayers high but limits also to are analyze quite differences rare (which in time. is the The reason main period why of they maythe data have collection to switch took sitesplace duringto find September enough 2009competition). and March 2010.The Tabledata reveals2 depicts that the exactfor examplestarting onlyand ending 0.36% points of the for eachNo Limitsite. Note Holdem that the players period of are data on collection high stakes had to (seebe extended table 11 due in to the technical appendix). We therefore reason that most people with multiple player identities in our problems such as downs of the server, software updates and disconnections. sample come from the third group. We estimate that for 100 player identities about 85 real players exist. This results in 6,029,980 people with a 6-months prevalence rate of Table 2 participating in online poker cash games for real money.

Results:Starting and Market ending pointsSizes, of Prevalence, the data collection and for Drivers each site of Online Poker The data in the OPD-UHH allows us to specify how many players in each country in thePoker world site play online poker for real moneyStart and how much rake they paidEnd to the operators whilePokerStars playing. Using these data we determined09/10/2009 the market sizes of the03/11/2010 different countries inFull dollar Tilt Poker terms and also the absolute and09/06/2009 relative prevalence of online03/11/2010 poker in a country. Everest Poker 08/13/2009 03/11/2010 MarketIPN (Boss Sizes Media) 07/27/2009 02/02/2010 Cake Poker 11/01/2009 07/02/2010 The OPD-UHH covers how often and how long a poker player played and if he played multiple tables at the same time. We linked this information to data about the averageIn the US$6 months rake of paid the data per collection player, hour,we obtained and table data for for 4,591,298 the different poker identitiespoker variants including and their stakes. This allowed us to determine how much US$ rake a player paid to the operator country of origin and their playing habits. Regarding the market shares of the observed poker sites these over the course of 6 months. As we also obtained the origin of the players, we could aggregate the rake paid for all observed players of each country. We then extrapolated

4 The poker sites Full Tilt Poker, Everest Poker and IPN (Boss Media) quote the country of origin while Poker Stars and Cake Poker specify the city of origin of the player. 5 Play money tables have not been taken into consideration. 6 For a more detailed description regarding the technical approach of the data collection see Sakai/Haruyoshi 2005. 10 UNLV Gaming Research & Review Journal ♦ Volume 16 Issue 1 5

this data to the whole market of each country by considering the market share of the observed operators in each country (88.4% in the USA, 57.4% in all other countries, resulting in 64.72% of the whole market). Multiplied by 2 we got the market size for online poker cash games for each country in 2010. Given that the operators earn about 70% of their revenues with cash games and 30% with tournaments we further extrapolated the market size to include the tournament revenues. The resulting gross market size is the rake paid by all online poker players in a given country. This is not identical with the players’ net losses: 1) Players get about 25-30% of Despite the prohibition of online their rake back in form of rakeback deals or bonuses and 2) players can win and lose money from other players, a cash flow we did not gambling and the introduction observe. To obtain the net losses, it is only necessary to account for of the Unlawful Internet the former point, which can be easily done be deducting 30% of the Gambling Enforcement Act gross market size. The latter point does not matter much for whole countries, because cash flows between the players converge to zero if (UIGEA) in 2006 theThe OPD US-UHH was covers the how observed often and player how long pool a poker are largeplayer (withoutplayed and rake, if he played poker multiple is a zero tables sum still by far the biggestat the same market time. We linked game). this information to data about the average US$ rake paid per player, hour, with 973.3 million US$and table in for 2010. the different pokerTable variants 3 shows and stakes. the ten10 This biggest allowed poker us to marketsdetermine inhow the much world US$ in rake dollar a player paid to the operatorterms over theof coursetheir gross of 6 m marketonths. As size we also and obtained their share the origin of the of thetotal players, market. we Despite the prohibition of online gambling and the introduction of could aggregate the rake paid for all observed players of each country. We then extrapolated this data to the Unlawful Internet Gambling Enforcement Act (UIGEA) in 2006 the US was still by thefar whole the biggest market of market each country with 973.3by considering million the US$ market in 2010.share of After the observed the Black operators Friday, in each when the countryUS sites (88.4% of Pokerstars, in the USA, 57.4%Full Tilt in all Poker other Absolutecountries, resulting Poker andin 64.72% Ultimate of the Bet whole were market). shut down, Multipliedthe US market by 2 we size got the has market dropped size fordramatically. online poker cashThe gamesremaining for each operators country in like 2010. Cake11 Given Poker that theor operatorsBodog Poker earn about which 70% still of their accept revenues US-players with cash hadgames a marketand 30% sharewith tournaments of less that12 we 10% further before the Black Friday. It will be interesting to see if these operators now rise and which level extrapolated the market size to include the tournament revenues.13 The resulting gross market size is the the US market will settle on. rake Thepaid bysecond all online largest poker market players inis aGermany given country. with This a market is not identical size of with 392 the million players’ US$. net losses: Other 1)big Players markets get about are France,25-30% of Russia, their rake Canada back in andform Great of rakeback Britain. deals Although or bonuses the14 and French 2) players market can winmay and now lose look money different from other after players, online a cash poker flow waswe did legalized, not observe. license, To obtain taxed, the net and losses, insulated it is only necessaryfromonline pokerthe to other wasaccount legalized, markets for the license, foinrmer 2010. taxed, point, and In whichitotal,nsulated can the from be players easilythe other done paid markets be 3.6 deducting in billion2010. In 30% total,US$ of the rakethe gross to the market operatorsplayers paid 3.6in billion2010 US$or 599 rake toUS$ the operators per player. in 2010 or 599 US$ per player. size. The latter point does not matter much for whole countries, because cash flows between the players

convergeTable 3 to zero if the observed player pool are large (without rake, poker is a zero sum game).

Table 3 shows the ten biggest poker markets in the world in dollar terms of their gross market size and Number of active online poker players, market share and market size per country their share of the total market. Despite the prohibition of online gambling and the introduction of the Unlawful Internet Gambling Enforcement Act (UIGEA) in 2006 the US was still by far the biggest Gross Market size 2010 Rank Country market with 973.3 million US$ in 2010. After theSize Black in mil. Friday, US$ perwhen year the US sites of Pokerstars,Share Full Tilt 26.95% Poker Absolute1 Poker andUSA Ultimate Bet were shut down, the973.30 US market size has dropped dramatically. 2 Germany 391.94 10.85% The remaining3 operatorsRussia like Cake Poker or Bodog Poker235.12 which still accept US-players6.51% had a market share of4 less that 10% beforeCanada the Black Friday. It will be interesting219.63 to see if these operators6.08% now rise and 5 France 187.35 5.19% which level the US market will settle on. 6 Great Britain 159.72 4.42% 7 Netherlands 152.80 4.23% The8 second largest marketSpain is Germany with a market size117.07 of 392 million US$. Other3.24% big markets are France,9 Russia, Canada Swedenand Great Britain. Although the French99.25 market may now look 2.75%different after 10 Finland 80.93 2.24% Total 3,611.59 100%

10 For a more detailed description of this procedure, see main report of the research project: Fiedler/Wilcke, 2011. 11Prevalence Our data were of fromOnline 09/09 Poker to 03/10, but we assumed that these data are representative for 2010. Probably the market grew a little in this time, but we neglected this and conservatively decided that it did not so we can give a numberBesides for the the whole market year size 2010. the prevalence of online poker is a key figure for describing the market for 12 According to PokerScout, the revenue of a poker site approximately consists of 70% cash games and 30% poker tournaments.online poker in a country. Prevalence can be measured in absolute terms (how many people play poker in 13 a Thiscountry) assumes and inthat relative the share terms of (how revenues many of people the operators play poker is 70% in relation in every to, country. for example, the population). 14 The exact amount differs from operator to operator and also depends on the playing volume of the player. The higher the playing volume, the higher the rakeback. We derived the number of poker players in a country as follows: First, we used the country data15 for the observed player identities UNLVand added Gaming the non identifiableResearch identities& Review proportionally. Journal ♦16 Volume Then, we 16 Issue 1 711

extrapolated the number by the market share of the observed sites during the data collection period. In a last step we calculated the number of active players by assuming that per 100 player identities 85 real persons exist (see reasoning above). The market shares are derived from the players in a country in relation to the total number of players.

15 For the players on Pokerstars and Cake Poker we obtained the city of origin. We used the city data base „World Cities“ from MaxMind to assign these players to countries. 16 For 11.38% of all observed player identities we could not determine their country of origin. Adding them proportionally to the observed player identities of each country implies that they are equally distributed among all countries. This may not be true as people from countries with stricter laws against online gambling have a higher incentive to hide their identity. 8

Prevalence of Online Poker Besides the market size the prevalence of online poker is a key figure for describing the market for online poker in a country. Prevalence can be measured in absolute terms (how many people play poker in a country) and in relative . . . there is a huge growth terms (how many people play poker in relation to, for example, the population). potential of the US market We derived the number of poker players in a country as follows: First, should online poker be we used the country data for the observed player identities and added legalized. the non identifiable identities proportionally. Then, we extrapolated the number by the market share of the observed sites during the data collection period. In a last step we calculated the number of active players by assuming that per 100 player identities 85 real persons exist (see reasoning above). The market shares are derived from the players in a country in relation to the total number of players. Table 4 lists the number of active poker players for the ten countries with the most online poker onlineTable poker 4 waslists legalized, the number license, of taxed, active and poker insulated players from forthe otherthe ten markets countries in 2010. with In total, the themost onlineplayers paidandpoker their3.6 playersbillion share ofUS$ andall rakeplayers their to thesharein theoperators world. of all inConcernin players 2010 or in599g the the US$ market world. per sizeplayer. Concerning the USA is by the far marketthe sizebiggest the market. USA Moreis by than far 1.4the million biggest people market. played More online than poker 1.4 for million real money people during played our data online pokercollectionTable 3for period real moneyfrom 09/09 during to 03/10. our Nearlydata collection every fourth period poker player from is 09/09 an American to 03/10. (23.71%). Nearly On the everysecond fourthplace is pokerthe German player market is an with American 581 thousand (23.71%). players, Onor 9.64% the second of all players place worldwide. is the German On the market with 581 thousand players, or 9.64% of all players worldwide. On the next ranks nextNumber ranks of follow active France online (445,860 poker players, players, market7.39%), shareRussia and (401,701 market players, size per 6.66%) country and Canada follow France (445,860 players, 7.39%), Russia (401,701 players, 6.66%) and Canada (401,701 players, players, 5.74%). 5.74%). Together, Together, the ten thecountries ten countries with the highest with numbthe highester of poker number players of account poker for 73.92% of all players worldwide. Gross Market size 2010 playersRank account for 73.92%Country of all players worldwide. Size in mil. US$ per year Share Table 14 USA 973.30 26.95% 2 Germany 391.94 10.85% 6.51% 10 countries3 with the highestRussia number of active poker players 235.12 4 Canada 219.63 6.08% 5 France 187.35 5.19%

Rank6 GreatCountry Britain Active players159.72 Share4.42% 71 NetherlandsUSA 1,429,943152.80 23.71%4.23% 82 GermanySpain 581,350117.07 9.64%3.24% 2.75% 93 SwedenFrance 445,86099.25 7.39% 104 FinlandRussia 401,70180.93 6.66%2.24% Total5 Canada 345,9713,611.59 5.74%100%

6 Great Britain 269,247 4.47% 4.20% Prevalence7 of Online PokerSpain 253,043 8 Netherlands 239,700 3.98% 9 Brazil 153,889 2.55% Besides the market size the prevalence of online poker is a key figure for describing the market for 10 Australia 129,714 2.15% online poker in a country. PrevalenceOther can be measured in absolute1,571,389 terms (how many people26.06% play poker in a country) and in relative termsTOTAL (how many people play poker5,490,908 in relation to, for example, the100% population).

We derived the number of poker players in a country as follows: First, we used the country data15 for TheThe absolute absolute prevalence prevalence of online of online poker disregards poker disregards the information the informationof the size of a of country. the size To ofknow a country.the observed To player know identities how large and addedthe proportion the non identifiable of online identities poker proportionally. players is in16 a Then, given we country, how large the proportion of online poker players is in a given country, the number of players has to be theextrapolated number the of number players by has the marketto be relatedshare of tothe the observed population sites during of the the country.data collection But period.as not In a related to the population of the country. But as not everybody has access to the internet, it is more everybodylast step we calculated has access the tonumber the internet, of active playersit is more by assuming meaningful that per to 100 relate player the identities number 85 to real the numbermeaningful of to internet relate the users number in toa thecountry. number Table of internet 5 depicts users in the a country. proportion Table of5 depicts online the poker persons exist (see reasoning above). The market shares are derived from the players in a country in playersproportion in of a onlinecountry poker per players internet in a countryusers. 0.307%per internet (1 users. out of 0.307% 326) (1of out all of people 326) of worldwide all people relation to the total number of players. withworldwide internet with accessinternet haveaccess played have played online online poker poker for for real real money during during the the 6 months 6 months of the ofdata collection. Accounting for the information of the internet users in a country changes the ranking of the 15 For the players on Pokerstars and Cake Poker we obtained the city of origin. We used the city data base „World Cities“countries. from Among MaxMind the to countries assign these with players more tothan countries. 100,000 internet users the proportion of online poker 16 For 11.38% of all observed player identities we could not determine their country of origin. Adding them players is highest for Hungary with 1.983%. One out of 50 Hungarians with internet access plays online proportionally to the observed player identities of each country implies that they are equally distributed among all countries.poker for Thisreal money.may not Forbe true the asUSA, people it is from striking countries that itwit ish by stricter far the laws biggest against market online in gambling absolute have terms, a higher but incentive to hide their identity. 12 UNLV Gaming Research & Review Journal ♦ Volume 16 Issue 1 89 the data collection. Accounting for the information of the internet users in a country changes the ranking of the countries. Among the countries with more than 100,000 rankinternet only 36usersth considering the proportion the number of online of internet poker users players in a country. is highest 0.596% for of Hungaryall American with internet 1.983%. Onerank out only of 36 50th consideringHungarians the numberwith internet of internet access users in plays a country. online 0.596% poker of all for American real money. internet For users (1 out of 168) are online poker players. On the one hand that means that the restrictions of the theusers USA, (1 out it isof striking168) are online that pokerit is byplayers. far theOn thebiggest one hand market that means in absolute that the restrictions terms, butof the rank UIGEAonlyrank only 36th were 36 consideringth effectiveconsidering to thesome the number numberextent of even internet of beforeinternet users the in usersBlacka country. Friday.in a0.596% country. On ofthe all other0.596% American hand of internetthat all means American that rankUIGEA only 36 wereth considering effective to the some number extent of eveninternet before users the in Black a country. Friday. 0.596% On the of other all American hand that internet means that thereinternetusers is (1 a out hugeusers of 168)growth (1 areout potentialonline of 168) poker of are theplayers. USonline marketOn thepoker oneshould handplayers. online that means pokerOn thethat be onethelegalized restrictions hand. that of meansthe that the usersthere (1 isout a ofhuge 168) growth are online potential poker of players. the US marketOn the shouldone hand online that meanspoker be that legalized the restrictions. of the restrictionsUIGEA were effectiveof the UIGEAto some extent were even effective before the to Black some Friday. extent On eventhe other before hand thatthe meansBlack that Friday. On TableUIGEA 5 were effective to some extent even before the Black Friday. On the other hand that means that thethereTable other is a 5huge hand growth that potential means of that the USthere market is a should huge online growth poker potential be legalized of. the US market should onlinethere is apoker huge growth be legalized. potential of the US market should online poker be legalized. 10Table countries 5 with the highest proportion of online poker players per internet users (only countries with Table10 countries5 with the highest proportion of online poker players per internet users (only countries with more10 morecountries than than 100,000 with100,000 the internet highest internet users)proportion users) in in 2010 2010 of online poker players per internet users (only countries with 10 countries with the highest proportion of online poker players per internet users (only countries with morerank onlythan 100,00036th considering internet users) the number in 2010 of internet users in a country. 0.596% of all American internet moreRankRank than 100,000CountryCountry internet users) inActive Active2010 playersplayers InternetInternet user user Players/internetPlayers/internet user user users1 1(1 out ofHungary 168)Hungary are online poker players.122,482122,482 On the one hand6,176,4006,176,400 that means that the restrictions1.983%1.983% of the Rank2 CountryEstonia Active players19,212 Internet969,700 user Players/internet1.981% user UIGEA2 were effectiveEstonia to some extent 19,212even before the Black Friday.969,700 On the other hand that1.981% means that Rank1 3 Country Active players Internet user Players/internet user 3 HungaryPortugalPortugal 122,482100,075100,075 6,176,4005,168,8005,168,800 1.983%1.936%1.936% there21 is4 a hugeHungaryEstonia growthDenmark potential of the122,48219,212 US90,532 market should online6,176,400969,7004,750,500 poker be legalized.1.981%1.983% 1.906% 4 Denmark 90,532 4,750,500 1.906% 32 5 PortugalEstoniaIceland 100,07519,2124,996 5,168,800969,700301,600 1.936%1.981%1.657% 5 Iceland 4,996 301,600 1.657% Table43 65 DenmarkPortugalNetherlands 100,07590,532239,700 4,750,5005,168,80014,872,200 1.906%1.936%1.612% 6 Netherlands 239,700 14,872,200 1.612% 54 7 DenmarkIcelandFinland 90,5324,99671,543 4,750,500301,6004,480,900 1.657%1.906%1.597% 765 8 NetherlandsIcelandFinlandCyprus 239,7004,99671,5436,445 14,872,200301,600433,8004,480,900 1.612%1.657%1.486%1.597% 108 76countries 9 Netherlands withFinlandCyprusNorway the highest proportion239,70071,54364,5356,445 of online poker 14,872,200players4,480,9004,431,100433,800 per internet users 1.597%1.612%(only1.456% 1.486%countries with 987 10 FinlandNorwayCyprusSlovenia 71,5436,44564,53518.899 4,480,900433,8001,298,5004,431,100 1.486%1.597%1.455%1.456% more than 100,000 internet users) in 2010 1098 SloveniaNorwayCyprus… 64,5356,44518.899… 4,431,100433,8001,298,500… 1.456%1.486%…1.455% 10 9 36 SloveniaNorway…USA 18.89964,5351,429,943… 1,298,5004,431,100239,893,600… 1.455%1.456%0.596% … 36Rank10 SloveniaCountryUSA… … Active18.8991,429,943… … players 1,298,500Internet239,893,600… … user Players/internet1.455%… …0.596% user … … … … 36 1 HungaryUSATOTAL… 1,429,9436,029,930122,482… 239,893,6001,965,162,3166,176,400… 0.596%0.307%1.983% … Source36 internet user:USA Internet World Stats, 1,429,943for June 2010. 239,893,600 0.596% 2 TOTALEstonia… 6,029,930…19,212 1,965,162,316…969,700 … 1.981%0.307% TOTAL… 6,029,930… 1,965,162,316… 0.307%… Source3 internet user:Portugal Internet World Stats, for June100,075 2010. 5,168,800 1.936% SourceDrivers internet user:ofTOTAL the Internet prevalence World Stats, forof6,029,930 JuneOnline 2010. Poker 1,965,162,316 0.307% Source4 internet user:Denmark Internet World Stats, for June 90,5322010. 4,750,500 1.906% Drivers of the prevalence of Online Poker DriversDrivers5 To understandof thetheIceland prevalence prevalence the drivers of of theOnline Online market4,996 Poker for Poker online poker, it is301,600 most useful to analyze the 1.657%relative size Drivers6 of theNetherlands prevalence of Online239,700 Poker 14,872,200 1.612% ofTo the understand markets because the the drivers absolute of numbers the market mostly fordepend online on the poker, size of ita country.is most We useful focused to on analyze the theTo Torelative7 understandunderstand sizeFinland thethe of drivers driversthe markets of of the the market market because71,543 for for online onlinethe poker, absolute poker, it is itmost 4,480,900 numbersis most useful useful to mostly analyze to analyze dependthe relative 1.597%the onrelative size the sizesize relativeTo8 understand prevalenceCyprus the driversof online of pokerthe market and identified6,445 for online that poker, the twoit is mainmost433,800 driversuseful to for analyze this number the relative are1.486% GDP size per ofofof the thea country. marketsmarkets because We focused the the absolute absolute on numbersthe numbers relative mostly mostly prevalence depend depend on the on of sizethe online ofsize a country.of poker a country. We and focused Weidentified focused on the onthat the of thecapita9 markets and culture. becauseNorway A the simple absolute regression numbers 64,535model mostly with depend the proportion on the4,431,100 size of ofpoker a country. players We per focusedinternet1.456% onusers the per relativetherelative two prevalenceprevalence main drivers ofof online online for poker pokerthis and number and identified identified are that GDP that the thetwo per twomain capita main drivers driversand for culture. this for number this A number aresimple GDP are perregression GDP per 10 Slovenia 18.899 1,298,500 1.455% modelrelativecountry prevalencewith and theits GDP proportionof online per capita poker ofyielded and poker identified a significant players that the influence:per two internet main If driversthe users GDP for per perthis capita numbercountry rises are by and GDP 1,000 its per US$, GDP capitacapita andand culture. AA… simple simple regression regression model model… with with the theproportion proportion of poker… of poker players players per internet per internet users17… per users per percapitathe capita proportionand culture. yielded of A online simple a significant poker regression players modelinfluence: increases with bythe 0.009 Ifproportion the percentage GDP of poker per points pcapitalayers (t=5.786, risesper internet p<.001). by 1,000 users per US$, countrycountry36 andand its GDPGDPUSA per per capita capita yielded yielded a1,429,943 significanta significant influence: influence: If 239,893,600the If GDP the GDP per capita per capita rises by rises 1,0000.596% by US$,1,000 US$, thecountry proportion and its GDP of… per online capita pokeryielded playersa significant… increases influence: by If the0.009 GDP… percentage per capita rises points by 1,00017 (t=5.786,… US$, thethe proportion Tableproportion 6 ofof onlineonline poker poker players players increases increases by by0.009 0.009 percentage percentage points points (t=5.786, (t=5.786, p<.001). p<.001). 17 p<.001).the proportion ofTOTAL online poker players increases6,029,930 by 0.009 percentage1,965,162,316 points (t=5.786, p<.001).170.307% Source internet user: Internet World Stats, for June 2010. TableTable 66 ResultsTable 6 of a simple linear regression regarding the influence of GDP per capita on the relative 17 ResultsDrivers See ofappendix aof simple the for prevalence linearthe operationalization regression of Online regarding of the simple Poker the linear influence regression. of GDP per capita on the relative prevalence of online poker 10 17 See appendix for the operationalization of the simple linear regression. 17prevalence To understand of online the poker drivers of the market for online poker, it is most useful to analyze the relative size 17 See See appendixappendix forfor thethe operationalization operationalization of ofthe the simple simple linear linear regression. regression. of the markets because the absolute numbers mostly depend on the size of a country. We focused 10on the Sample (n=161) 10 10 relative prevalence of online poker and identified that the two main drivers for this number are GDP per RegressionSample (n=161) Variablecapita and culture. A simple regressionRegressioncoefficient model with the proportiont-value of poker players perSignificance internet users per Variablecountry and its GDP per capita yieldedcoefficient a significant influence: t-value If the GDP per capita risesSignificance by 1,000 US$, Constant .192 4.123 .000 17 the proportion of online poker players increases by 0.009 percentage points (t=5.786, p<.001). GDPConstant per capita in .192 4.123 .000 1000GDP US$per capita in .009 5.786 .000 1000 Table US$ 6 .009 5.786 .000 Goodness of fit R ² = .177; adjusted R² = .171; F-value= 33. 477 (p<.000) Goodness of fit R ² = .177; adjusted R² = .171; F-value= 33.477 (p<.000) 17 See appendixWe were for also the operationalizationable to find evidence of the that simple the linear culture regression.18 has a significant influence on the proportion 18 of pokerWe players were per also internet able toUNLV users find evidenceGamingin a country. thatResearch Anthe analysisculture & Review of has variance aJournal significant (ANOVA) ♦ Volume influence with 16 on Issuea F the-Value 1proportion of 1013 of14.114 poker (p<.001) players confirmedper internet that users there in aare country. significant An analysis differences of variance in the mean (ANOVA)-values withof each a F -culturalValue of group (see14.114 table (p<.001) 7). We confirmed controlled thatthe effectthere arefor significantGDP per capita differences by using in theit as mean a covariate-values (Fof =12.753,each cultural p<.001). group (seeGDP table per capita 7). We and controlled culture explainthe effect R² =50.4%for GDP of per the capita variation by using between it as thea co relativevariate (Fpoker =12.753, prevalence p<.001). in theGDP countries. per capita and culture explain R²=50.4% of the variation between the relative poker prevalence in the countries. Table 7 Table 7 Results of the ANOVA regarding the influence of culture on the relative prevalence of online poker Results of the ANOVA regarding the influence of culture on the relative prevalence of online poker Sample (n=161) Sum of Sample (n=161) SquaresSum of df Mean Square F Sig. Squares df Mean Square F Sig. Between Groups 17.974 8 2.247 19.324 .000 ConstantBetween Groups 17.9742.136 18 2.1362.247 18.37519.324 .000 GDPConstant per capita 1.4832.136 1 1.4832.136 12.75318.375 .000 CulturalGDP per groupcapita 11.5121.483 71 1.6451.483 14.11412.753 .000 WithinCultural Groups group 17.67311.512 1527 1.645 .116 14.114 .000 TOTALWithin Groups 35.64717.673 161152 .116 TOTAL 35.647 161 Fit of the model R² = .504; adjusted R² = .478 Fit of the model R² = .504; adjusted R² = .478

18 We operationalized culture according to the classification of Huntington 1996: 1=Western, 2=Orthodox, 3=Islamic,18 We operationalized 4=African, 5= culture Latin American,according 6=Sinic, to the 7=Hindu,classification 8=Buddhist, of Huntington 0=Others; 1996: see appendix.1=Western, 2=Orthodox, 3=Islamic, 4=African, 5= Latin American, 6=Sinic, 7=Hindu, 8=Buddhist, 0=Others; see appendix. 11 11

Results of a simple linear regression regarding the influence of GDP per capita on the relative prevalence of online poker

Sample (n=161) Regression Variable coefficient t-value Significance

Constant .192 4.123 .000 GDP per capita in 1000 US$ .009 5.786 .000

Goodness of fit R² = .177; adjusted R² = .171; F-value= 33.477 (p<.000)

We Wewere were also also able able to findfind evidence evidence that thatthe culture the culture18 has a significanthas a significant influence oninfluence the proportion on theof poker proportion players per of internetpoker playersusers in a percountry. internet An analysis users inof variancea country. (ANOVA) An analysis with a Fof-Value variance of (ANOVA)14.114 (p<.001) with confirmed a F-Value that ofthere 14.114 are significant (p<.001) differences confirmed in the that mean there-values are of significanteach cultural group differences in the mean-values of each cultural group (see table 7). We controlled the (see table 7). We controlled the effect for GDP per capita by using it as a covariate (F =12.753, p<.001). effect for GDP per capita by using it as a covariate (F =12.753, p<.001). GDP per capita andGDP cultureper capita explain and culture R²=50.4% explain R of²=50.4% the variation of the variation between between the therelative relative poker poker prevalence prevalence in in the countries. countries.

Table 7

Results of the ANOVA regarding the influence of culture on the relative prevalence of online poker

Sample (n=161) Sum of Results of a simple linear regressionSquares regarding df the influenceMean of Square GDP per capita onF the relative Sig. prevalence of online poker Between Groups 17.974 8 2.247 19.324 .000 Constant 2.136 1 2.136 18.375 .000 GDP per capita 1.483 Sample1 (n=161)1.483 12.753 .000 Cultural group 11.512 Regression 7 1.645 14.114 .000 WithinVariable Groups 17.673 coefficient 152 t -.116value Significance TOTAL 35.647 161 Constant .192 4.123 .000 GDPFit of perthe capitamodel in R² = .504; adjusted R² = .478 1000 US$ .009 5.786 .000 The results imply that online poker is only a factor in western and orthodox The results imply that online poker is only a factor in western and orthodox countries. With the countries.18Goodness of With fit the exception R² of= the.177; countries adjusted R from² = .171; Latin F-value= America, 33.477 the (p<.000) markets in all exception We operationalized of the countries culture from according Latin America, to the theclassification markets inof all Huntington other countries 1996: are 1=Western, negligible 2=Orthodox, (see table other3=Islamic, countries 4=African, are 5= negligibleLatin American, (see 6=Sinic, table 7=Hindu, 8). That 8=Buddhist, can either 0=Others; mean seethat appendix. these markets have 8). That can either mean that these markets have an enormous18 growth potential or that people of these an enormousWe were growth also able potential to find evidence or that that people the culture of these has culturala significant groups influence are onnot the interested proportion11 cultural groups are not interested in online poker at all. The directions of these results could be confirmed inof pokeronline players poker per at internet all. The users directions in a country. of Anthese analysis results of variancecould be (ANOVA) confirmed with ain F -aValue Turkey-test of whichin a Turkey compares-test which the compares mean values the mean (for values detailed (for detailed information information see seetable table 12 12 in in the the appendix). 14.114 (p<.001) confirmed that there are significant differences in the mean-values of each cultural group (see table 7). We controlled the effect for GDP per capita by using it as a covariate (F =12.753, p<.001). Table 8 GDP per capita and culture explain R²=50.4% of the variation between the relative poker prevalence in theDescriptive countries. statistics of the ANOVA regarding the influence of culture on the relative prevalence of

Tableonline 7 poker

Results of the ANOVA regardingSample the influence (n=161 of) culture on the relative prevalence of online poker 95%-confidence interval Standard SampleStandard (n=161) Lower Upper Culture N Mean Sum deviation of error bound bound Squares df Mean Square F Sig. Western 47 .638 .452 .040 .558 .718 BetweenOrthodox Groups 13 .450 17.974 .326 8 .077 2.247.348 .651 19.324 .000 Islamic 41 .076 .108 .043 -.009 .161 Constant 2.136 1 2.136 18.375 .000 African 23 .079 .059 .058 -.035 .193 GDPLatin per capita 1.483 1 1.483 12.753 .000 CulturalAmerican group 20 .185 11.512 .100 7 .062 1.645.063 .308 14.114 .000 WithinSinic Groups6 .019 17.673 .018 152.113 -.116.204 .242 TOTALHindu 5 .112 35.647 .196 161.124 -.132 .356 Buddhist 6 .128 .157 .113 -.095 .351

FitTOTAL of the model161 .289 .369 R ² = .504; adjusted R² = .478

An analysis of the influence of the law on the prevalence on online poker yielded no significant 18 We operationalized culture according to the classification of Huntington 1996: 1=Western, 2=Orthodox, 3=Islamic,results. But 4=African, as law and 5= theLatin enforcement American, 6=Sinic, of law are7=Hindu, hard to 8=Buddhist, quantify this 0=Others; result maysee appendix. not be taken for full 14value. In ourUNLV eyes Gamingit mainly Researchsays that law & Reviewwhich proh Journalibits online ♦ Volume poker 16 was Issue not enforced1 at the time of the11 data collection.

Limitations Although the study yields many findings there are some limitations. First, we only collected data about cash games, poker tournaments have not been taken into consideration. As mentioned in footnote 12 the revenue of a poker site approximately consists of 70% cash games and 30% poker tournaments.

12

An analysis of the influence of the law on the prevalence on online poker yielded no significant results. But as law and the enforcement of law are hard to quantify this result may not be taken for full value. In our eyes it mainly says that law which prohibits online poker was not enforced at the time of the data collection.

Limitations Although the study yields many findings there are some limitations. First, we only collected data about cash games, poker tournaments have not been taken into consideration. As mentioned in footnote 12 the revenue of a poker site approximately consists of 70% cash games and 30% poker tournaments. The market size calculations of this study assume that this relation is identical for every operator, which might not be true as the tournaments require a larger player pool and the smaller poker networks probably earn a lesser percentage with them. The OPD-UHH also excludes pure tournament players and, hence, the numbers of poker players in the different countries we derived in Thethis market study sizeunderestimate calculations of the this true study value. assume However, that this relation it is reasonable is identical for that every there operator, are just which a very mightfew players not be true playing as the tournamentsonly tournaments require a largerand whom player poolwe doand not the smallerhave any poker data networks on. probably earn Second,a lesser percentage the data withon thethem. origin The OPD is self-UHH reported also excludes by the pure players. tournament It is players therefore and, possiblehence, the numbersthat not of all po playersker players stated in the their different correct countries country. we derived But asin thiswithdrawing study underestimate funds is the only true possiblevalue. if the correct personal data are submitted, we reason that the cases of wrong information However, it is reasonable that there are just a very few players playing only tournaments and whom we do are rare. not haveThird, any as data PokerStars on. and Cake Poker do not give information about the country of a player but only of their city. To assign the players to countries we used the city database Second, the data on the origin is self reported by the players. It is therefore possible that not all players “World Cities” by Maxmind. In doing so we faced two general challenges: 1) Many city statednames their in correctthe world country. exist But multiple as withdrawing times. funds 2) The is only city possible data base if the only correct lists personal cities data having are a submitted,population we ofreason at least that the50,000. cases ofTo wrong solve information these issues are rare. we developed an algorithm to assign player identities to countries by taking the size of the cities into consideration. In the end, weThird, were as able PokerStars to successfully and Cake Poker assign do 92%not give of information the poker aboutplayers the countrystating ofa acity player of butorigin only to of theirtheir city. country To assign of origin.the players 8% to remained countries we as used not theassignable. city database “World Cities” by Maxmind.19 In doingFinally, so we faced we facedtwo general the problem challenges: that 1) Many one playercity names can in have the world more exist than multiple one poker times. identity2) The cityby registeringdata base only at lists multiple cities having sites. a Itpopulation may also of beat least possible 50,000. that To solvemore these than issues one personwe developed use the same player identity (for example family members). Considering different qualitative an algorithm to assign player identities to countries by taking the size of the cities into consideration. In arguments (see footnote 8), we estimate that for 100 poker identities 85 real persons theexist. end, We we wereadmit able this to numbersuccessfully is notassign an 92% exact of theempirical poker players value stating but ana city estimation of origin to and their countrytherefore of origin. open 8%to subjectiveremained as interpretation.not assignable.

SummaryFinally, we and faced Perspectives the problem that one player can have more than one poker identity by registering at multipleThe sites. online It may poker also marketbe possible has that grown more rapidlythan one personin the usepast the years. same playerBut until identity now (for there example familyhave onlymembers). been Considering estimates differentabout the qualitative total market arguments size (see and footnote nobody 8), knew we estimate how wherethat for 100the pokerplayers identities and their 85 real money persons come exist. from,We admit which this numbercountries is not are an theexact biggest empirical markets, value but and an where estimationthe proportion and therefore of people open togambling subjective online interpretation. on poker games in relation to the internet users is highest. We were first to answer these questions by using data from the Online Poker Database of the University of Hamburg (OPD-UHH) which includes information about approximately 4.6 million onlineSummary poker playersand Perspectives and their countries of origin. This study is able to shed light on the online poker market. It is the first time that theThe market online can poker be market broken has down grown to rapidly countries in the pastwhich years. provides But until important now there have insights only beenfor local estimateslegislators about who, the totalfor example,market size want and nobody to evaluate knew how their where regulation the players of and online their gambling.money come We from,first whichgave countriesan overview are the of biggest the structure markets, andof thewhere online the proportion poker and of peoplethe marketgambling shares online of on the pokerdifferent games poker in relation sites to in the the internet years users 2008-2010. is highest. We We thenwere fidescribedrst to answer the these data questions in the OPD-by using dataUHH from and the the Online methodology Poker Database of gatheringof the University it. We of wereHamburg able (OPD to identify-UHH) which 4,591,298 includes poker identities and extrapolated this number to 6,029,980 different players who paid 3.6 information about approximately 4.6 million online poker players and their countries of origin. billion US$ rake to the operators in 2010 (on average 599 US$ per player). Hence, online poker has evolved to become a huge market. This is especially true for western and

19 For further information about this database see: http://www.maxmind.com/app/worldcities. UNLV Gaming Research & Review Journal ♦ Volume 16 Issue 1 1315 orthodox countries where most of the business takes place – even when controlling for GDP – per capita. We also assigned the observed poker identities to their country of origin and derived the market size of the most important poker countries: Even though the USA had been the biggest market with 1.4 million players paying 973 million US$ rake in 2010 (before Black Friday), there are 35 countries with a higher prevalence of online poker in relation to the number of internet users in that country. For example, Hungary has the highest proportion of online poker players in relation to the number of internet users (1.98%). The relatively low prevalence of players in the USA could be seen as evidence that the regulation of online poker and the UIGEA deterred at least some of the potential players even before Black Friday. On the other hand this comparison with the prevalence of online poker in other countries But the most important cause means that there is a lot of potential in the American online poker for a regulated market being market. However, these numbers are mostly generated from totally free a smaller market is a fenced (black) markets. However, a regulated market will probably not yield the full potential of the market. One reason is taxes, which increase the market, which excludes non- rake, make the product more expensive, and lead to reduced demand. residents from the player pool Another reason might be player protection which helps addicted (e.g. Italy or France). gamblers to stay away from the game as addicts tend to play longer sessions, more often and more intensely than non-addicts (e.g. see Productivity Commission 2010) and, hence, are an important source of revenue (Williams & Wood 2004). But the most important cause for a regulated market being a smaller market is a fenced market, which excludes non-residents from the player pool (e.g. Italy or France). This sharply reduces the player pools and, hence, the positive network effects of large player pools where it is possible to find enough people to play with for every game type, every limit and at any daytime. The results presented in this paper did not include information on the market after the Black Friday when the most important poker operators in the USA were shut down. Further research has to determine the effects of this event and clarify if the players from the USA stopped playing, switched to other operators still accepting US players, or even avoid the country identification and log in to Pokerstars or Full Tilt Poker with a proxy- IP and deposit with an international credit card. This study only scratches on the surface of the possibilities the detailed data in the ODP-UHH allow. It is possible not only to break down the market on a national level but also to regions within countries. Future studies will also be able to analyze the playing habits and to determine for example the session lengths and the playing frequency of a player and which limits and how many tables the player plays simultaneously. This information can then be used for further analyses regarding inter and intra country differences. By conducting time series analyses it is also be possible to identify how many players tend to play more often and more intensively over time and how many decrease the intensity of play and what kind of factors may give a hint at which group a player belongs to. This may not only be interesting for the industry but also from the viewpoint of excessive and compulsive gambling.

16 UNLV Gaming Research & Review Journal ♦ Volume 16 Issue 1 This study only scratches on the surface of the possibilities the detailed data in the ODP-UHH allow. It is possible not only to break down the market on a national level but also to regions within countries. Future studies will also be able to analyze the playing habits and to determine for example the session lengths and the playing frequency of a player and which limits and how many tables the player plays simultaneously. This information can then be used for further analyses regarding inter and intra country differences. By conducting time series analyses it is also be possible to identify how many players tend to play more often and more intensively over time and how many decrease the intensity of play and what kind of factors may give a hint at which group a player belongs to. This may not only be interesting for the industry but also from the viewpoint of excessive and compulsive gambling.

Measurement Appendix Measurement Appendix

Table 9

Operationalization of s variables of the simple linear regression regarding relative market size and GDP per capita

Variable Description independent variable GDP per Capita in thousand US-Dollar

dependent variable Proportion of poker players proportion of poker players in a country per internet users in percentage

Table 10

Operationalization of variables of ANOVA

Variable Description dependent variable Proportion of poker players proportion of poker players in a country per internet users in percentage

factor Cultural group 1=Western, 2=Orthodox, 3=Islamic, 4=African, 5= Latin American, 6=Sinic, 7=Hindu, 8=Buddhist, 0=Others

Covariate GDP per capita 15 In thousand US-Dollar

Table 11

Players per Stake for No Limit Holdem

Limit (Small Blind/Big Blind in $) Stakes % of players 0,01/0,02-0,05/0,10 Micro 48.43% 0,10/0,20- 0,5/1 Low 41.24% 0,75/1,50-5/10 Mid 9.97% 8/16-500/1000 High 0.36%

Table 12

Influence of Culture – Results of Turkey-Test

Sample (n=161) Average Standard 95%-confidence interval Culture (I) Culture (J) difference (I-J) error p-value Lower bound Upper bound

Orthodox .176 .111 .760 -.165 .516 Islamic .717* .076 .000 .484 .949 African .713* .090 .000 .436 .990 Western Latin American .577* .094 .000 .287 .867 Sinic .789* .153 .000 .318 1.261 Hindu .670* .166 .002 .159 1.182 Buddhist .649* .153 .001 .178 1.121 Western -.176 .111 .759 -.516 .165 Islamic UNLV.541* Gaming.113 Research .000 & Review.195 Journal .887♦ Volume 16 Issue 1 17 African .538* .123 .001 .160 .915 Orthodox Latin American .401* .126 .037 .014 .789 Sinic .614* .175 .013 .077 1.150 Hindu .495 .186 .144 -.077 1.067 Buddhist .474 .175 .127 -.063 1.011 Western -.717* .076 .000 -.949 -.484 Orthodox -.541* .113 .000 -.887 -.195 African -.003 .092 1.000 -.287 .280 Islamic Latin American -.140 .097 .833 -.436 .157 Sinic .073 .155 1.000 -.403 .548 Hindu -.046 .168 1.000 -.561 .469 Buddhist -.067 .155 1.000 -.542 .408 Western -.713* .090 .000 -.990 -.436 Orthodox -.538* .123 .001 -.915 -.160 Islamic .003 .092 1.000 -.280 .287 African Latin American -.136 .108 .912 -.469 .196 Sinic .076 .162 1.000 -.423 .574 Hindu -.043 .175 1.000 -.579 .494

16

7=Hindu, 8=Buddhist, 0=Others

Covariate GDP per capita In thousand US-Dollar

Table 11

Players per Stake for No Limit Holdem

Limit (Small Blind/Big Blind in $) Stakes % of players 0,01/0,02-0,05/0,10 Micro 48.43% 0,10/0,20- 0,5/1 Low 41.24% 0,75/1,50-5/10 Mid 9.97% 8/16-500/1000 High 0.36%

Table 12

Influence of Culture – Results of Turkey-Test

Sample (n=161) Average Standard 95%-confidence interval Culture (I) Culture (J) difference (I-J) error p-value Lower bound Upper bound

Orthodox .176 .111 .760 -.165 .516 Islamic .717* .076 .000 .484 .949 African .713* .090 .000 .436 .990 Western Latin American .577* .094 .000 .287 .867 Sinic .789* .153 .000 .318 1.261 Hindu .670* .166 .002 .159 1.182 Buddhist .649* .153 .001 .178 1.121 Western -.176 .111 .759 -.516 .165 Islamic .541* .113 .000 .195 .887 African .538* .123 .001 .160 .915 Orthodox Latin American .401* .126 .037 .014 .789 Sinic .614* .175 .013 .077 1.150 Hindu .495 .186 .144 -.077 1.067 Buddhist .474 .175 .127 -.063 1.011 Western -.717* .076 .000 -.949 -.484 Orthodox -.541* .113 .000 -.887 -.195 African -.003 .092 1.000 -.287 .280 Islamic Latin American -.140 .097 .833 -.436 .157 Sinic .073 .155 1.000 -.403 .548 Hindu -.046 .168 1.000 -.561 .469 Buddhist -.067 .155 1.000 -.542 .408 Western -.713* .090 .000 -.990 -.436 Orthodox -.538* .123 .001 -.915 -.160 Islamic .003 .092 1.000 -.280 .287 African Latin American -.136 .108 .912 -.469 .196 Sinic .076 .162 1.000 -.423 .574 Hindu -.043 .175 1.000 -.579 .494 Buddhist -.064 .162 1.000 -.562 .435 Western -.577* .094 .000 -.867 -.287 Orthodox -.401* .126 .037 -.789 -.014 16 Islamic .140 .097 .833 -.157 .436 Latin- African .136 .108 .912 -.196 .469 American Sinic .212 .165 .902 -.294 .718

Hindu .094 .177 .999 -.450 .637

Buddhist .073 .165 1.000 -.434 .579 Western -.789* .153 .000 -1.261 -.318 Orthodox -.614* .175 .013 -1.150 -.077 Islamic -.073 .155 1.000 -.548 .403 Sinic African -.076 .162 1.000 -.574 .423 Latin American -.212 .165 .902 -.718 .294 Hindu -.119 .214 .999 -.777 .540 Buddhist -.140 .204 .997 -.768 .488 Western -.670* .166 .002 -1.182 -.159 Orthodox -.495 .186 .144 -1.067 .077 Islamic .046 .168 1.000 -.469 .561 Hindu African .043 .175 1.000 -.494 .579 Latin American -.094 .177 .999 -.637 .450 Sinic .119 .214 .999 -.540 .777 Buddhist -.021 .214 1.000 -.679 .638 Western -.649* .153 .001 -1.121 -.178 Orthodox -.474 .175 .127 -1.011 .063 Islamic .067 .155 1.000 -.408 .542 Buddhist African .064 .162 1.000 -.435 .562 Latin American -.073 .165 1.000 -.579 .434 Sinic .140 .204 .997 -.488 .768 Hindu .021 .214 1.000 -.638 .679 Table 12: Results of Turkey-Test. *: p < .05.

References

Cabot, A., & Hannum, R. (2008). Poker: Public Policy, Law, Mathematics and the Future of an American Tradition. T.M. Cooley Law Review, 22, 443-513.

Chen, B., & Ankenman, J. (2006). The Mathematics of Poker. Pittsburgh : ConJelCo.

DeDonno, M.A., & Detterman, D.K. (2008). Poker Is a Skill. Gaming Law Review and Economics, 12, 31-36.

Dreef, M., & Borm, P., & van der Genugten, B. (2003). On Strategy and Relative Skill in Poker. 18 ♦ InternationalUNLV Gaming Game Research Theory Review,& Review 5, 83Journal-103. Volume 16 Issue 1

Fiedler, I.,& Wilcke, A.-C. (2012). Online Poker in the European Union. Gaming Law Review and Economics, 16, 21-26.

Fiedler, I., & Wilcke, A.-C. (2011). Der Markt für Onlinepoker: Spielerherkunft und Spielerverhalten. Norderstedt: BOD Verlag,

Fiedler, I., & Rock, J.-P. (2009). Quantifying skill in games – Theory and empirical evidence for poker. Gaming Law Review and Economics, 13, 50-57.

Haruyoshi, S. (2005). Internet Poker: Data Collection and Analysis, Brown University.

17

References Cabot, A., & Hannum, R. (2008). Poker: Public Policy, Law, Mathematics and the Future of an American Tradition. T.M. Cooley Law Review, 22, 443-513.

Chen, B., & Ankenman, J. (2006). The Mathematics of Poker. Pittsburgh : ConJelCo.

DeDonno, M.A., & Detterman, D.K. (2008). Poker Is a Skill. Gaming Law Review and Economics, 12, 31-36.

Dreef, M., & Borm, P., & van der Genugten, B. (2003). On Strategy and Relative Skill in Poker. International Game Theory Review, 5, 83-103.

Fiedler, I.,& Wilcke, A.-C. (2012). Online Poker in the European Union. Gaming Law Review and Economics, 16, 21-26.

Fiedler, I., & Wilcke, A.-C. (2011). Der Markt für Onlinepoker: Spielerherkunft und Spielerverhalten. Norderstedt: BOD Verlag,

Fiedler, I., & Rock, J.-P. (2009). Quantifying skill in games – Theory and empirical evidence for poker. Gaming Law Review and Economics, 13, 50-57.

Haruyoshi, S. (2005). Internet Poker: Data Collection and Analysis, Brown University.

H2GC. (2009). eGambling data bulletin. 2008 Q3/Q4 report. H2 Gambling Capital.

Internet World Stats. (June 2010). http://www.internetworldstats.com/list2.htm.

Huntington, S. P. (1996). The Clash of Civilizations. New York: Simon & Schuster.

Partygaming, 2010: 2009 full year results presentation.

Productivity Commission 2010, Gambling - Productivity Commission Inquiry Report, Australian Government.

Rock, J.-P., & Fiedler, I. (2008). Die Empirie des Online-Pokers – Bestimmung des Geschicklichkeitsanteils anhand der kritischen Wiederholungshäufigkeit. Zeitschrift für Wett- und Glücksspielrecht, 3, 412-422.

Turner, N. E. (2008). Viewpoint: Poker Is an Acquired Skill. Gaming Law Review and Economics, 12. 229-30.

Williams, R. J., & Wood, R. T. (2004). The proportion of gaming revenue derived from problem gamblers: Examining the issues in a Canadian context. Analyses of Social Issues and Public Policy, 4,33–45.

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