The Market for Online Poker

Transcription

1 The Market for Online Poker Ingo Fiedler Ann-Christin Wilcke Abstract The recent events Black Friday biggest online poker networks in USA were shut down demonstrated necessity to make decisions about regulation online poker. But although online poker is a gold mine data, until now nobody knows where players and ir money come from. It seems that knowledge about online poker market has not been able to keep up with speed its evolution in past years. This paper is first to shed light on this matter. We use data 4,591,298 poker identities from Online Poker Database University Hamburg (OPD-UHH) collected over a six months period from 09/2009 to 03/2010. We find that worldwide 6 million players paid 3.61 billion US$ rake to operators in USA is still by far biggest market with 1,429,943 active players and million US$ net revenues in With regard to number internet users in a country, Hungary is biggest relative market: One out 50 Hungarians with internet access plays online poker for real money. The two main drivers relative market size in a country are GDP per capita and culture. Using data from OPD-UHH, future research will be able to break down market also on a regional level within countries and to examine inter and intra country differences in playing habits online poker players. Keywords: online, poker, market, gambling, data, habits. Ingo Fiedler Research Associate Institute Law & Economics University Hamburg Ann-Christin Wilcke Research Associate Institute Law & Economics University Hamb Introduction Online gambling and online poker in particular is a relatively new phenomenon nearly nonexistent in During past years it has grown extraordinarily so that it is now an important factor in whole gambling market. However, even today it is ten neglected by old industry, legislators and researchers as an unwelcome black market just as if denying would cease its existence. But economic reality is different. Online gambling has tremendous cost advantages due to electronic instead brick and mortar operation and, even more important, its operators do not have to pay (high) taxes and license fees. In case poker, large player pools and corresponding network effects have also helped game to grow to a size not to be matched in fline world. From an academic perspective, most research in field poker has focused on playing strategy (e.g. Chen & Ankenman, 2006) and especially question wher poker is a game skill or a game chance (see e.g. Dreef et al. 2003, Cabot & Hannum 2005, Dedonno & Detterman 2008, or Turner 2008 and for an empirical point view see Fiedler & Rock 2009). The answer to this question is especially important from a legal perspective as most jurisdictions legalize games skill while y regulate games chance. However, skill/chance debate has not yet come to an end, and also skill does not seem to be best criterion for distinction between harmful and non-harmful games (Rock & Fiedler 2008). The recent events Black Friday biggest online poker networks in USA were shut down demonstrated necessity to make decisions about regulation online poker. Should it be prohibited? If so, what are best instruments to enforce a prohibition? Or should online poker be legalized like Italy and France have chosen lately? If so, what is optimal tax and how could player protection be implemented? Before answering se questions it may be worthwhile to gar more information about national markets as information about m is practically non- UNLV Gaming Research & Review Journal Volume 16 Issue 1 7

2 existent. We use data from Online Poker Database University Hamburg (OPD- UHH), a database including information on origin and playing habits 4,591,298 poker identities over a six months period. Before describing data in OPD-UHH we analyze worldwide online poker market and market shares various poker operators. We n present results about size whole market and for ten biggest markets in dollar terms. Afterwards we analyze prevalence online poker in absolute terms in relation to internet users. We also identify main drivers relative market size a country. Both sections provide new and important insights into online gambling market especially for legislators. We continue by highlighting limitations this study before we summarize results and give an outlook on furr research in last section. Market Size and Market Shares by Operators With exception an analysis poker marke ts in European Union (Fiedler & Wilcke 2012) academic research on prevalence and size online poker markets is still observation missing. However, industry by due PokerScout to observation which tacks active industry players at by PokerScout different operators which at tacks any given moment active players market at shares different operators operators are known. at any Table given 1 shows moment market market shares for shares nine biggest operators poker sites are in known. 2008, 2009 Table and shows (until August). market The shares market for shares are nine computed biggest in poker sites in 2008, 2009 and 2010 (until August). The market shares are computed in relation relation to monthly average players total market: 53,121 in 2008, 68,483 in 2009 and 71,441 in to monthly average players total market: 53,121 in 2008, 68,483 in 2009 and Note that a player is not counted in this statistic unless he is playing at moment scan by 71,441 in Note that a player is not counted in this statistic unless he is playing at PokerScout. The average number accounts for all differences in active players per daytime, week days moment scan by PokerScout. The average number accounts for all differences in and active months. players per daytime, week days and months. Table 1 The recent events Black Friday biggest online poker networks in USA were shut down demonstrated necessity to make decisions about regulation online poker. Market shares in online poker industry in 2008, 2009, and 2010 Site/Network a Data recorded for 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 Network b 2.35% 3.40% 3.03% No Everest Poker 4.85% 3.36% 1.80% Yes Microgaming c 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 Or 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. The online online poker poker industry industry is highly is dominated highly dominated by two biggest by providers: two biggest Pokerstars providers: and Full Tilt Pokerstars and Full Tilt Poker, and ir importance has increased over past years. Poker, and ir importance has increased over past years. In 2008 y had a combined market share In 2008 y had a combined market share 45.18% while in 2010 y already 45.18% while in 2010 y already accounted for nearly two thirds (62.69%) whole playing accounted for nearly two thirds (62.69%) whole playing volume in market. Correspondingly, volume market. Correspondingly, smaller operators smaller lost operators significance lost significance and dwindled and dwindled not only not only in relative in relative size but also in absolute size. While in 2008 on average 29,121 players were actively playing 8on smaller UNLV networks Gaming at any given Research moment, & Review number Journal decreased Volume to 26, Issue in

3 size but also in absolute size. While in 2008 on average 29,121 players were actively playing on smaller networks at any given moment, number decreased to 26,655 in This development shows power positive network effects in online poker market. The bigger player pool, 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 same time ( multitabling ) and refore generate significantly more traffic than players who only play at one table. Anor important factor reason for large and increasing market share PokerStars is price product in form rake structure, which is cheapest for players at Pokerstars. But this only applies to gross costs without considering rakeback or promotions which are used to set incentives for players to play more ten and stick to one site. Rakeback and bonuses effectively reduce costs for players and are, hence, also an important This development shows power positive network effects in online poker market. The bigger factor which drives market shares. Or factors include quality stware, player pool, more players are attracted to it just by its mere size. As a player, you will more likely security, reputation, ease deposits and withdrawals, and also interactions between find online (weak) opponents site and fline on a large casinos network (e.g. for your satellites favorite into game live type, events betting like structure Word and limit. Series This is Poker). even more true for players who play multiple tables at same time ( multitabling ) and refore generate Knowing significantly market more traffic shares than players different who only operators, play one table. next Anor step important is to look factor at revenue which is generated in market. The gambling consultant company H2GC reason for large and increasing market share PokerStars is price product in form estimated size online poker market in 2009 to be 2.4 billion US$ (H2GC rake structure, which is cheapest for players at Pokerstars. But this only applies to gross costs 2009). Anor way to determine revenues is to look at revenues stated in financial without considering reports rakeback stock or listed promotions companies which and are used extrapolate to set incentives it relation for players to to its play market more share. ten and For stick example, to one site. PartyGaming Rakeback and reported bonuses effectively to have earned reduce toger costs for with players its subsidiary and are, PartyPoker, hence, also an important million factor US$ which net drives revenues market shares. in 2009 Or (Partygaming factors include 2010). quality Extrapolated with 7.22% average market share PartyPoker in 2009, total market for online stware, security, reputation, ease deposits and withdrawals, and also interactions between online poker was 2.7 billion US$, close to estimate H2GC. site and fline casinos (e.g. satellites into live events like Word Series Poker). But se figures are only estimates. They also do not answer where money comes from and which biggest national markets with most players are. What Knowing market shares different operators, next step is to look at revenue which is was size US-market before Black Friday? Was it still largest market generated in market. The gambling consultant company H2GC estimated size online poker although legislator tried to enforce a prohibition online gambling with Unlawful market in 2009 Internet to be 2.4 Gambling billion US$ Enforcement (H2GC 2009). Act Anor (UIGEA) way to since determine 2006? revenues These are is to questions look at we revenues address stated with in financial data from reports 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 toger with its subsidiary PartyPoker, The Online Poker Database University Hamburg (OPD-UHH) million US$ net revenues in 2009 (Partygaming 2010). 1 Extrapolated with 7.22% average Online poker is a goldmine data as all operators show in ir lobby a lot market share PartyPoker in 2009, total market for online poker was 2.7 billion US$, close to information about people playing at ir tables. It is easy to observe origin estimate a player H2GC. (city 2 or country ), game type, betting structure and limit table y play at, and, course, date and time. Financed by city Hamburg, But se figures are only estimates. They also do not answer where money comes from and which Institute Law & Economics at University Hamburg collected se data in collaboration biggest national with markets independent with most market players are. spectator What was PokerScout size in US-market OPD-UHH. before A stware Black Friday? electronically Was it still gared largest market data although players legislator for tried to following enforce a prohibition poker networks: online Pokerstars, gambling 3 with Full Tilt Unlawful Poker, Internet Everest Gambling Poker, Enforcement IPN (Boss Act Media) (UIGEA) and since Cake 2006? Poker. These This are stware scanned each cash game table mentioned poker sites and wrote questions we address with data from OPD-UHH which we describe next. displayed information into a SQL database. The data collection was conducted for each poker site during a period six months. It took about ten minutes to scan all tables an operator and collect information about players sitting at tables. This implies about 6 data points per hour or 25,920 in course six months and allows not only to determine session lengths players but also to analyze differences in time. The main period data collection 1 The gross revenue was million US$ million US$ were paid in bonuses to players (so called rakeback ), meaning that players got an average effective discount on price 21.6%. 2 Note that extrapolation assumes that revenues per player PartyPoker are representative for industry. 3 As UIGEA applies to definition remote gambling in Wire Act it does not necessarily include poker. UNLV Gaming Research & Review Journal Volume 16 Issue 1 9 4

4 The data collection was conducted for each poker site during a period six months. It took about ten minutes to scan all tables an operator and collect information about players sitting at tables. This implies about 6 data points per hour or 25,920 in course six months and allows not only to determine session lengths players but also to analyze differences in time. The main period data collection took place during September 2009 and March Table 2 depicts exact starting took place during September 2009 and March Table 2 depicts exact starting and ending and ending points points for for each site. Note that that period period data data collection collection had to be had extended to be extended due to technical due to problems technical such problems as downs such server, as downs stware updates server, and stware disconnections. updates and disconnections. Table 2 Starting and ending points data collection for each site Poker site Start End PokerStars 09/10/ /11/2010 Full Tilt Poker 09/06/ /11/2010 Everest Poker 08/13/ /11/2010 IPN (Boss Media) 07/27/ /02/2010 Cake Poker 11/01/ /02/2010 The Online Poker Database University Hamburg (OPD-UHH) Online In poker 6 months is a goldmine data data collection as all operators we obtained show ir data lobby for a 4,591,298 lot information poker about identities including In 6 ir months country data origin collection and we ir obtained playing data for habits. 4,591,298 Regarding poker identities market including shares ir people playing at ir tables. It is easy to observe origin a player (city or country 4 ), game type, country observed origin poker and ir sites playing se habits. are 64.72% Regarding all poker market players shares worldwide observed (88.27% poker sites se betting structure and limit table y play at, and, course, date and time. Financed by city US-players and 57.4% players from all or countries) who were playing during Hamburg, period Institute data collection. Law & Economics If this number at University is extrapolated Hamburg to collected total se market, data in we get collaboration a number with 7,094,095 independent different market spectator player identities. PokerScout Note in OPD-UHH. that one real A stware person can have multiple electronically 4 The poker player sites gared Full identities Tilt Poker, data if Everest he opens players Poker accounts and for IPN following (Boss with Media) more poker quote than networks: one country operator Pokerstars, origin and Full while many Tilt Poker Stars players and Cake have Poker tried specify out city more origin than one poker player. Poker, site and refore have multiple accounts. However, 5 Everest Poker, IPN (Boss Media) and Cake Poker. This stware scanned each cash game table 5 Play money tables have not been taken into consideration. to be counted multiple times in OPD-UHH he has to have played with se 6 For mentioned a more detailed poker description sites and wrote regarding displayed technical information approach into a data SQL collection database. see accounts actively during data collection period. This may be true for 6 Sakai/Haruyoshi three groups players: 1) High volume players (pressional and addicted gamblers), 2) Players on 5 The data collection was conducted for each poker site during a period six months. It took about ten high limits as y may need more than one site to find opponents and 3) People trying minutes to scan all tables an operator and collect information about players sitting at tables. out different sites to see which one suits m best. The high volume players have a huge incentive This implies to about play 6 at data just points one per network hour 25,920 as in bonuses/rebates course six months y can and get allows increase not only with to ir determine playing session volume. lengths Players on players high but limits also to are analyze quite differences rare (which in time. is The reason main period why y may data have collection to switch took sites place during to find September enough competition) and March The data Table reveals 2 depicts that for exact example starting only and ending 0.36% points for each No Limit site. Note Holdem that players period are data on collection high stakes had to (see be extended table 11 due in to technical appendix). We refore reason that most people with multiple player identities in our problems such as downs server, stware updates and disconnections. sample come from 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 Table 2 participating in online poker cash games for real money. Starting and ending points data collection for each site Results: Market Sizes, Prevalence, and Drivers Online Poker The data in OPD-UHH allows us to specify how many players in each country in Poker world site play online poker for real money Start and how much rake y paid End to operators while PokerStars playing. Using se data we determined 09/10/2009 market sizes 03/11/2010 different countries in Full dollar Tilt Poker terms and also absolute and 09/06/2009 relative prevalence online 03/11/2010 poker in a country. Everest Poker 08/13/ /11/2010 IPN (Boss Media) 07/27/ /02/2010 Market Sizes Cake Poker 11/01/ /02/2010 The OPD-UHH covers how ten and how long a poker player played and if he played multiple tables at same time. We linked this information to data about average In US$ 6 months rake paid data per collection player, hour, we obtained and table data for for 4,591,298 different poker identities poker variants including and ir stakes. This allowed us to determine how much US$ rake a player paid to operator country origin and ir playing habits. Regarding market shares observed poker sites se over course 6 months. As we also obtained origin players, we could aggregate rake paid for all observed players each country. We n extrapolated 4 The poker sites Full Tilt Poker, Everest Poker and IPN (Boss Media) quote country origin while Poker Stars and Cake Poker specify city origin player. 5 Play money tables have not been taken into consideration. 6 For a more detailed description regarding technical approach data collection see Sakai/Haruyoshi UNLV Gaming Research & Review Journal Volume 16 Issue 1 5

5 Despite prohibition online gambling and introduction Unlawful Internet Gambling Enforcement Act (UIGEA) in 2006 US was still by far biggest market with million US$ in this data to whole market each country by considering market share observed operators in each country (88.4% in USA, 57.4% in all or countries, resulting in 64.72% whole market). Multiplied by 2 we got market size for online poker cash games for each country in Given that operators earn about 70% ir revenues with cash games and 30% with tournaments we furr extrapolated market size to include tournament revenues. The resulting gross market size is rake paid by all online poker players in a given country. This is not identical with players net losses: 1) Players get about 25-30% ir rake back in form rakeback deals or bonuses and 2) players can win and lose money from or players, a cash flow we did not observe. To obtain net losses, it is only necessary to account for former point, which can be easily done be deducting 30% gross market size. The latter point does not matter much for whole countries, because cash flows between players converge to zero if The OPD-UHH covers how observed ten and player how long pool a poker are large player (without played and rake, if he played poker multiple is a zero tables sum at same time. We linked game). this information to data about average US$ rake paid per player, hour, and table for different poker Table variants 3 shows and stakes. ten 10 This biggest allowed poker us to markets determine in how much world US$ in rake dollar a player paid to operator terms over course ir gross 6 months. market As size we also and obtained ir share origin total players, market. we Despite prohibition online gambling and introduction could aggregate rake paid for all observed players each country. We n extrapolated this data to Unlawful Internet Gambling Enforcement Act (UIGEA) in 2006 US was still by whole market each country by considering market share observed operators in each far biggest market with million US$ in After Black Friday, when country US sites (88.4% Pokerstars, in USA, 57.4% Full Tilt in all Poker or Absolute countries, resulting Poker and in 64.72% Ultimate Bet whole were market). shut down, Multiplied US market by 2 we size got has market dropped size for dramatically. online poker cash The games remaining for each operators country in like Cake 11 Given Poker that or operators Bodog Poker earn about which 70% still ir accept revenues US-players with cash had games a market and 30% share with tournaments less that 12 we 10% furr before Black Friday. It will be interesting to see if se operators now rise and which level extrapolated market size to include tournament revenues. 13 The resulting gross market size is US market will settle on. rake paid by all online poker players in a given country. This is not identical with players net losses: The second largest market is Germany with a market size 392 million US$. Or 1) big Players markets get about are France, 25-30% Russia, ir rake Canada back in and form Great rakeback Britain. deals Although or bonuses 14 and French 2) players market can win may and now lose look money different from or after players, online a cash poker flow was we did legalized, not observe. license, To obtain taxed, net and losses, insulated it is only necessary from online poker to or was account legalized, markets for license, former in taxed, point, and In which insulated total, can from be players easily or done paid markets be 3.6 deducting billion In 30% total, US$ rake gross to market size. operators players paid 3.6 The latter in billion point 2010 US$ does or 599 rake to not matter US$ operators much player. in 2010 or 599 US$ per player. for whole countries, because cash flows between players converge Table 3 to zero if observed player pool are large (without rake, poker is a zero sum game). Table 3 shows ten biggest poker markets in world in dollar terms ir gross market size and Number active online poker players, market share and market size per country ir share total market. Despite prohibition online gambling and introduction Unlawful Internet Gambling Enforcement Act (UIGEA) in 2006 US was still by far biggest Gross Market size 2010 Rank Country market with million US$ in After Size Black in mil. Friday, US$ per when year US sites Pokerstars, Share Full Tilt Poker Absolute 1 Poker and USA Ultimate Bet were shut down, % US market size has dropped dramatically. 2 Germany % The remaining 3 operators Russia like Cake Poker or Bodog Poker which still accept US-players 6.51% had a market share 4 less that 10% before Canada Black Friday. It will be interesting to see if se operators 6.08% now rise and 5 France % which level US market will settle on. 6 Great Britain % 7 Nerlands % The 8 second largest market Spain is Germany with a market size million US$. Or 3.24% big markets are France, 9 Russia, Canada Sweden and Great Britain. Although French market may now look 2.75% different after 10 Finland % Total 3, % 10 For a more detailed description this procedure, see main report research project: Fiedler/Wilcke, Prevalence Our data were from Online 09/09 Poker to 03/10, but we assumed that se data are representative for Probably market grew a little in this time, but we neglected this and conservatively decided that it did not so we can give a number for whole year Besides market size prevalence online poker is a key figure for describing market for According to PokerScout, revenue a poker site approximately consists 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 This country) assumes and in that relative share terms (how revenues many people operators play poker is 70% in relation in every to, country. for example, population). 14 The exact amount differs from operator to operator and also depends on playing volume player. The higher playing volume, higher rakeback. We derived number poker players in a country as follows: First, we used country data 15 for observed player identities UNLV and added Gaming non identifiable Research identities & Review proportionally. Journal 16 Volume Then, we 16 Issue 1 7 extrapolated number by market share observed sites during data collection period. In a last step we calculated number active players by assuming that per 100 player identities 85 real persons exist (see reasoning above). The market shares are derived from players in a country in relation to total number players. 11

6 Prevalence Online Poker Besides market size prevalence online poker is a key figure for describing 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 terms (how many people play poker in relation to, for example, population). We derived number poker players in a country as follows: First, we used country data for observed player identities and added non identifiable identities proportionally. Then, we extrapolated number by market share observed sites during data collection period. In a last step we calculated number active players by assuming that per 100 player identities 85 real persons exist (see reasoning above). The market shares are derived from players in a country in relation to total number players. online Table Table poker 4 lists 4 was lists legalized, number number license, active taxed, poker active and players poker insulated for players from ten for countries or ten markets with countries in most online with In total, poker most online players paid and ir poker 3.6 players billion share US$ all and rake players ir to in share operators world. all in Concerning players 2010 or in 599 US$ market world. per size player. USA is by far Concerning market size biggest market. USA More is by than far 1.4 million biggest people market. played More online than poker 1.4 for million real money people during played our data online poker collection Table 3 for period real money from 09/09 during to 03/10. our Nearly data collection every fourth period poker player from is 09/09 an American to 03/10. (23.71%). Nearly On every second fourth place is poker German player market is an with American 581 thousand (23.71%). players, On or 9.64% second all players place worldwide. is German On market Number with active 581 online thousand poker players, or market 9.64% share all and players market worldwide. size per country On next ranks next ranks follow France (445,860 players, 7.39%), Russia (401,701 players, 6.66%) and Canada follow France (445,860 players, 7.39%), Russia (401,701 players, 6.66%) and Canada (401,701 players, 5.74%). Toger, ten countries with highest number poker players account players, 5.74%). Toger, ten countries with highest number poker players for 73.92% account all players for 73.92% worldwide. all players worldwide. Gross Market size 2010 Rank Country Size in mil. US$ per year Share Table 14 USA % 2 Germany % 3 Russia % 10 countries with highest number active poker players 4 Canada % 5 France % Rank 6 Great Country Britain Active players Share 4.42% 71 Nerlands USA 1,429, % 4.23% 82 Germany Spain 581, % 3.24% 9 Sweden % 3 France 445, % 104 Finland Russia 401, % 2.24% Total 5 Canada 345,971 3, % 100% 6 Great Britain 269, % 7 Spain 253, % Prevalence Online Poker 8 Nerlands 239, % 9 Brazil 153, % Besides market size prevalence online poker is a key figure for describing market for 10 Australia 129, % online poker in a country. Prevalence Or can be measured in absolute 1,571,389 terms (how many people 26.06% play poker in a country) and in relative terms TOTAL (how many people play poker 5,490,908 in relation to, for example, 100% population). We derived number prevalence poker players online in poker a country disregards as follows: First, information we used country size data 15 for The absolute prevalence online poker disregards information size a country. To know a country. observed To player know identities how large and added proportion non identifiable online identities poker proportionally. players is in 16 a Then, given we country, how large proportion online poker players is in a given country, number players has to be extrapolated number number players by has market to be related share to observed population sites during country. data collection But period. as not In a related to population country. But as not everybody has access to internet, it is more everybody last step we calculated has access to number internet, active players it is more by assuming meaningful that per to 100 relate player identities number 85 to real number meaningful to internet relate users number in to a country. number Table internet 5 depicts users in a country. proportion Table 5 depicts online poker persons exist (see reasoning above). The market shares are derived from players in a country in players proportion in a online country poker per players internet a country users % per internet (1 users. out 0.307% 326) (1 out all people 326) worldwide all people relation to total number players. with worldwide internet with access internet have access played have played online online poker poker for for real money during 6 months 6 months data collection. Accounting for information internet users in a country changes ranking 15 For players on Pokerstars and Cake Poker we obtained city origin. We used city data base World Cities countries. from Among MaxMind to countries assign se with players more to than countries. 100,000 internet users proportion online poker 16 For 11.38% all observed player identities we could not determine ir country origin. Adding m players is highest for Hungary with 1.983%. One out 50 Hungarians with internet access plays online proportionally to observed player identities each country implies that y are equally distributed among all countries. poker for This real money. may not For be true as USA, people it is from striking countries that it with is by stricter far laws biggest against market online in gambling absolute have terms, a higher but incentive to hide ir identity. 12 UNLV Gaming Research & Review Journal Volume 16 Issue re is a huge growth potential US market should online poker be legalized.

7 data collection. Accounting for information internet users in a country changes ranking countries. Among countries with more than 100,000 rank internet only 36 users th considering proportion number online internet poker users players in a country. is highest 0.596% for Hungary all American with internet 1.983%. One rank out only th considering Hungarians number with internet access users in plays a country. online 0.596% poker all for American real money. internet For users (1 out 168) are online poker players. On one hand that means that restrictions users USA, (1 out it is striking 168) are online that poker it is by players. far On biggest one hand market that means in absolute that restrictions terms, but rank UIGEA only rank only 36th were th effective considering to some number extent even internet before internet users users Black a country. Friday. in a 0.596% country. On all or 0.596% American hand internet that all means American that rank UIGEA only 36 were re internet is a huge users th considering effective to some number extent even internet before users in Black a country. Friday % On or all American hand that internet means that users (1 out 168) growth (1 are out potential online 168) poker are players. US online market On poker one should hand players. online that means poker On that be one legalized. restrictions hand that means that users re (1 is out a huge 168) growth are online potential poker players. US market On should one hand online that means poker be that legalized. restrictions restrictions UIGEA were effective UIGEA to some extent were even effective before to Black some Friday. extent On even or before hand that means Black that Friday. On Table UIGEA or 5 were hand effective that to means some extent that even re before is a huge Black growth Friday. potential On or hand that US means market that re Table is a 5 huge growth potential US market should online poker be legalized. should online re is a poker huge growth be legalized. potential US market should online poker be legalized. 10 Table countries 5 with highest per internet users (only countries with Table 10 countries 5 with highest proportion online poker players per internet users (only countries with more 10 more countries than than with 100,000 highest internet proportion users) in in 2010 online poker players per internet users (only countries with 10 countries with highest proportion online poker players per internet users (only countries with more rank only than 100, th considering internet users) number in 2010 internet users in a country % all American internet more Rank Rank than 100,000 Country internet users) in Active 2010 players Internet user user Players/internet user user users 1 1 (1 out 168) Hungary are online poker players. 122,482 On one hand 6,176,400 that means that restrictions 1.983% 1.983% Rank UIGEA 2 2 Country were effective Estonia Estonia Active players to some extent 19,212 Internet 969,700 user Players/internet even before Black Friday. 969, % user Rank On or hand that 1.981% means that Hungary Country Portugal Portugal Active 122, , ,075 players Internet 6,176,400 5,168,800 user Players/internet 1.983% 5,168, % user re % 2 is 4 a huge Hungary Estonia growth Denmark potential 122,482 19,212 US 90,532 market should online 6,176, ,700 4,750,500 poker be legalized % 1.983% 1.906% 4 Denmark 90,532 4,750, % 32 5 Portugal Estonia Iceland 100,075 19,212 4,996 5,168, , , % 1.981% 1.657% 5 Iceland 4, , % Table Denmark Portugal Nerlands 100,075 90, ,700 4,750,500 5,168,800 14,872, % 1.936% 1.612% Nerlands Denmark 239,700 14,872, % Iceland Finland 90,532 4,996 71,543 4,750, ,600 4,480, % 1.906% 1.597% Nerlands Iceland Finland Cyprus 239,700 4,996 71,543 6,445 14,872, , ,800 4,480, % 1.657% 1.486% 1.597% countries 9 Nerlands with highest proportion online poker players per internet users (only countries with Finland Cyprus Norway 239,700 71,543 64,535 6,445 14,872,200 4,480,900 4,431, , % 1.612% 1.456% 1.486% 7 10 Finland Norway Cyprus Slovenia 71,543 6,445 64, ,480, ,800 1,298,500 4,431, % 1.597% 1.455% 1.456% more than 100,000 internet users) in Slovenia Norway Cyprus 64,535 6, ,431, ,800 1,298, % 1.486% 1.455% Slovenia Norway USA ,535 1,429,943 1,298,500 4,431, ,893, % 1.456% 0.596% 36 Rank 10 Slovenia Country USA Active ,429,943 players 1,298,500 Internet 239,893,600 user Players/internet 1.455% 0.596% user 361 Hungary USA TOTAL 1,429,943 6,029, , ,893,600 1,965,162,316 6,176, % 0.307% 1.983% Source 36 internet user: USA 2 TOTAL Estonia Internet World Stats, 1,429,943 for June 6,029, ,893,600 19,212 1,965,162, % 969, % 0.307% Source 3 internet user: TOTAL Portugal Internet World Stats, for 6,029,930 June 100, ,965,162, % 5,168, % Source Drivers internet user: TOTAL Internet prevalence World Stats, for 6,029,930 June Online Poker 1,965,162, % Source 4 internet user: Denmark Internet World Stats, for June 90, ,750, % Drivers 5 Iceland prevalence Online 4,996 Poker Drivers To understand prevalence drivers Online market Poker for Poker online poker, it is 301,600 most useful to analyze 1.657% relative size Drivers To 6 understand Nerlands prevalence drivers Online 239,700 Poker 14,872, % To 7 understand markets because Finland drivers absolute market numbers market 71,543 for mostly for online depend online poker, it poker, 4,480,900 is most size it useful a country. is most to analyze We useful focused to 1.597% relative on analyze size To relative understand size drivers markets market because for online poker, absolute it is most numbers useful to mostly analyze depend relative on size size relative To 8 understand prevalence Cyprus drivers online absolute poker market and identified numbers 6,445 for online that mostly poker, two depend it is main on most 433,800 drivers useful size to for a analyze this number country. We relative are 1.486% GDP a country. We focused on relative prevalence online poker and identified focused size per markets because absolute numbers mostly depend on size a country. We focused on that on capita 9 markets and culture. because Norway A simple absolute regression numbers 64,535 model mostly with depend proportion on 4,431,100 size poker a country. players We per focused internet 1.456% on users per relative two prevalence main drivers online for poker this and and identified are that GDP that two per two main capita main drivers drivers and for culture. this for number this A number are simple GDP are per regression GDP per 10 Slovenia ,298, % model relative country prevalence and its GDP with proportion online per capita poker yielded and poker identified a significant players that influence: with per two internet main If drivers GDP users for per poker per this capita number rises players country are by per internet and GDP 1,000 its per US$, capita and culture. A simple regression model with proportion poker players per internet users per users GDP per per capita proportion 36 capita and culture. yielded A online simple poker USA a significant regression players influence: model increases with by ,429,943 If proportion percentage GDP a 239,893,600 per poker points capita players (t=5.786, rises per internet p<.001). by 1,000 users 17 If GDP per capita rises 0.596% US$, per country and its GDP per capita yielded a significant influence: If GDP per capita rises by 1,000 by US$, 1,000 US$, country proportion and its GDP per online capita poker yielded players a significant increases influence: by If GDP percentage capita rises points by 1,000 (t=5.786, US$, Table proportion 6 online poker players increases by by percentage percentage points points (t=5.786, (t=5.786, p<.001). p<.001). 17 p<.001). 17 proportion TOTAL online poker players increases 6,029,930 by percentage 1,965,162,316 points (t=5.786, p<.001) % Source internet user: Internet World Stats, for June Table 6 Table Results Table 6 a simple linear regression regarding influence GDP per capita on relative Drivers prevalence Online Poker Results 17 See appendix a simple for linear operationalization regression regarding simple linear influence regression. GDP per capita on relative prevalence online poker 10 prevalence 17 See To appendix understand for online operationalization poker drivers market simple for linear online regression. poker, it is most useful to analyze relative size See appendix for operationalization simple simple linear linear regression. regression. markets because absolute numbers mostly depend on size a country. We focused 10 on Sample (n=161) relative prevalence online poker and identified Sample that (n=161) two main drivers for this number are GDP per Regression Variable capita and culture. A simple regression Regression coefficient model with proportion t-value poker players per Significance internet users per Variable country and its GDP per capita yielded coefficient a significant influence: t-value If GDP per capita rises Significance by 1,000 US$, Constant proportion online poker players increases by percentage points (t=5.786, p<.001). 17 GDP Constant per capita in GDP US$ per capita in Table US$ Goodness fit R² =.177; adjusted R² =.171; F-value= (p<.000) Goodness fit R² =.177; adjusted R² =.171; F-value= (p<.000) 17 See appendix We were for also operationalization able to find evidence that simple linear culture regression. 18 has a significant influence on proportion poker We players were per also internet able to UNLV users find evidence Gaming a country. that Research An analysis culture & Review 18 has variance a Journal significant (ANOVA) Volume influence with 16 on Issue a F-Value 1proportion poker (p<.001) players confirmed per internet that users re in a are country. significant An analysis differences variance in mean-values (ANOVA) with each a F-Value cultural group (see table (p<.001) 7). We confirmed controlled that effect re are for significant GDP per capita differences by using in it as mean-values a covariate (F =12.753, each cultural p<.001). group GDP (see table per capita 7). We and controlled culture explain effect R²=50.4% for GDP per capita variation by using between it as a covariate relative (F poker =12.753, prevalence p<.001). in 13

8 Constant GDP per capita in 1000 US$ Goodness fit R² =.177; adjusted R² =.171; F-value= (p<.000) We We were were also able to find evidence that that culture culture 18 has a has significant a significant influence influence on proportion on poker proportion players per internet poker players users in a per country. internet An analysis users in variance a country. (ANOVA) An analysis with a F-Value variance (ANOVA) (p<.001) with confirmed a F-Value that re are significant (p<.001) differences confirmed in that mean-values re are significant each cultural group differences in mean-values each cultural group (see table 7). We controlled (see table 7). We controlled 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 GDP per capita and culture explain R²=50.4% variation between relative poker prevalence in and culture explain R²=50.4% variation between relative poker prevalence in countries. Table 7 Results ANOVA regarding influence culture on relative prevalence online poker Sample (n=161) Sum Results a simple linear regression Squares regarding df influence Mean Square GDP per capita on F relative Sig. prevalence online poker Between Groups Constant GDP per capita Sample 1 (n=161) Cultural group Regression Within Variable Groups coefficient 152 t-value.116 Significance TOTAL Constant GDP Fit per capita model in R² =.504; adjusted R² = US$ 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 countries. Goodness With fit exception R² =.177; countries adjusted R² from =.171; Latin F-value= America, (p<.000) markets in all 18 exception We operationalized countries culture from according Latin America, to classification markets in all Huntington or countries 1996: are 1=Western, negligible 2=Orthodox, (see table or 3=Islamic, countries 4=African, are 5= negligible Latin American, (see 6=Sinic, table 7=Hindu, 8). That 8=Buddhist, can eir 0=Ors; mean see that appendix. se markets have an 8). That enormous can eir growth mean that potential se markets We were also able to find evidence or that have that people an enormous culture se 18 growth has cultural potential a significant groups or that influence are people on not interested se proportion 11 in cultural poker online groups players poker are per at not internet all. interested The users directions in online poker in a country. An se at all. analysis results The directions variance could be se (ANOVA) confirmed results could with in a F-Value a be Turkey-test confirmed which in a Turkey-test compares which compares mean values mean (for values detailed (for detailed information information see see table table in in appendix) (p<.001) confirmed that re are significant differences in mean-values each cultural group (see table 7). We controlled 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% variation between relative poker prevalence in Descriptive countries. statistics ANOVA regarding influence culture on relative prevalence Table online 7 poker Results ANOVA regarding Sample influence (n=161) culture on relative prevalence online poker 95%-confidence interval Standard Sample Standard (n=161) Lower Upper Culture N Mean Sum deviation error bound bound Squares df Mean Square F Sig. Western Between Orthodox Groups Islamic Constant African GDP Latin per capita Cultural American group Within Sinic Groups TOTAL Hindu Buddhist Fit TOTAL model R² =.504; adjusted R² =.478 An analysis influence law on prevalence on online poker yielded no significant 18 We operationalized culture according to classification Huntington 1996: 1=Western, 2=Orthodox, 3=Islamic, results. But 4=African, as law and 5= Latin enforcement American, 6=Sinic, law are 7=Hindu, hard to 8=Buddhist, quantify this 0=Ors; result may see appendix. not be taken for full 14 value. In our UNLV eyes Gaming it mainly Research says that law & Review which prohibits Journal online Volume poker 16 was Issue not enforced 1 at time 11 data collection. Limitations

9 An analysis influence law on prevalence on online poker yielded no significant results. But as law and enforcement 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 time data collection. Limitations Although study yields many findings re are some limitations. First, we only collected data about cash games, poker tournaments have not been taken into consideration. As mentioned in footnote 12 revenue a poker site approximately consists 70% cash games and 30% poker tournaments. The market size calculations this study assume that this relation is identical for every operator, which might not be true as tournaments require a larger player pool and smaller poker networks probably earn a lesser percentage with m. The OPD-UHH also excludes pure tournament players and, hence, numbers poker players in different countries we derived in The this market study size underestimate calculations this true study value. assume However, that this relation it is reasonable is identical for that every re operator, are just which a very might few players not be true playing as tournaments only tournaments require a larger and whom player pool we do and not smaller have any poker data networks on. probably earn Second, a lesser percentage data with on m. origin The OPD-UHH is self reported also excludes by pure players. tournament It is players refore and, possible hence, numbers that not all poker players players stated in ir different correct countries country. we derived But as in this withdrawing study underestimate funds is only true possible value. if correct personal data are submitted, we reason that cases wrong information However, it is reasonable that re are just a very few players playing only tournaments and whom we do are rare. not have any data on. Third, as PokerStars and Cake Poker do not give information about country a player but only ir city. To assign players to countries we used city database Second, data on origin is self reported by players. It is refore possible that not all players World Cities by Maxmind. In doing so we faced two general challenges: 1) Many city stated ir correct country. But as withdrawing funds is only possible if correct personal data are names in world exist multiple times. 2) The city data base only lists cities having a submitted, population we reason at least that 50,000. cases To wrong solve information se issues are rare. we developed an algorithm to assign player identities to countries by taking size cities into consideration. In end, Third, as PokerStars and Cake Poker do not give information about country a player but only we were able to successfully assign 92% poker players stating a city origin to ir city. country To assign origin. players 8% to remained countries we as used not assignable. city database World Cities by Maxmind. 19 In doing Finally, so we faced we faced two general problem challenges: that 1) Many one player city names can in have world more exist than multiple one poker times. identity 2) The city by registering data base only at lists multiple cities having sites. a It population may also be at least possible 50,000. that To solve more se than issues one person we developed use same player identity (for example family members). Considering different qualitative an algorithm to assign player identities to countries by taking size cities into consideration. In arguments (see footnote 8), we estimate that for 100 poker identities 85 real persons end, we were able to successfully assign 92% poker players stating a city origin to ir exist. We admit this number is not an exact empirical value but an estimation and country refore origin. open 8% to subjective remained as interpretation. not assignable. Finally, we faced problem that one player can have more than one poker identity by registering at Summary and Perspectives multiple The sites. online It may poker also market be possible has that grown more rapidly than one person in use past years. same player But until identity now (for re example family have only members). been Considering estimates different about qualitative total market arguments size (see and footnote nobody 8), knew we estimate how where that for 100 poker players identities and ir 85 real money persons come exist. from, We admit which this number countries is not are an exact biggest empirical markets, value but and where estimation proportion and refore people open to gambling subjective online interpretation. on poker games in relation to internet users is highest. We were first to answer se questions by using data from Online Poker Database University Hamburg (OPD-UHH) which includes information about approximately 4.6 million online Summary poker players and Perspectives and ir countries origin. This study is able to shed light on online poker market. It is first time that The online poker market has grown rapidly in past years. But until now re have only been market can be broken down to countries which provides important insights for local estimates legislators about who, total for example, market size want and nobody to evaluate knew how ir where regulation players and online ir gambling. money come We from, first gave which an countries overview are biggest structure markets, and where online proportion poker and people market gambling shares online on poker different games poker in relation sites to in internet years users is highest. We n were first described to answer se data questions in OPD- by using data UHH from and Online methodology Poker Database garing University it. We were Hamburg able (OPD-UHH) to identify 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 ir countries origin. billion US$ rake to 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 furr information about this database see: UNLV Gaming Research & Review Journal Volume 16 Issue

10 orthodox countries where most business takes place even when controlling for GDP per capita. We also assigned observed poker identities to ir country origin and derived market size most important poker countries: Even though USA had been biggest market with 1.4 million players paying 973 million US$ rake in 2010 (before Black Friday), re are 35 countries with a higher prevalence online poker in relation to number internet users in that country. For example, Hungary has highest proportion online poker players in relation to number internet users (1.98%). The relatively low prevalence players in USA could be seen as evidence that regulation online poker and UIGEA deterred at least some potential players even before Black Friday. On or hand this comparison with prevalence online poker in or countries means that re is a lot potential in American online poker market. However, se numbers are mostly generated from totally free (black) markets. However, a regulated market will probably not yield full potential market. One reason is taxes, which increase rake, make product more expensive, and lead to reduced demand. Anor reason might be player protection which helps addicted gamblers to stay away from game as addicts tend to play longer sessions, more ten and more intensely than non-addicts (e.g. see Productivity Commission 2010) and, hence, are an important source revenue (Williams & Wood 2004). But most important cause for a regulated market being a smaller market is a fenced market, which excludes non-residents from player pool (e.g. Italy or France). This sharply reduces player pools and, hence, positive network effects 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 market after Black Friday when most important poker operators in USA were shut down. Furr research has to determine effects this event and clarify if players from USA stopped playing, switched to or operators still accepting US players, or even avoid 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 surface possibilities detailed data in ODP-UHH allow. It is possible not only to break down market on a national level but also to regions within countries. Future studies will also be able to analyze playing habits and to determine for example session lengths and playing frequency a player and which limits and how many tables player plays simultaneously. This information can n be used for furr 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 ten and more intensively over time and how many decrease intensity play and what kind factors may give a hint at which group a player belongs to. This may not only be interesting for industry but also from viewpoint excessive and compulsive gambling. But most important cause for a regulated market being a smaller market is a fenced market, which excludes nonresidents from player pool (e.g. Italy or France). 16 UNLV Gaming Research & Review Journal Volume 16 Issue 1

11 play more ten and more intensively over time and how many decrease intensity play and what kind factors may give a hint at which group a player belongs to. This may not only be interesting for industry but also from viewpoint excessive and compulsive gambling. Measurement Appendix Measurement Appendix Table 9 Operationalization s variables simple linear regression regarding relative market size and GDP per capita Variable GDP per Capita Proportion poker players Description independent variable in thousand US-Dollar dependent variable proportion poker players in a country per internet users in percentage Table 10 Operationalization variables ANOVA Variable Proportion poker players Cultural group GDP per capita Description dependent variable proportion poker players in a country per internet users in percentage factor 1=Western, 2=Orthodox, 3=Islamic, 4=African, 5= Latin American, 6=Sinic, 7=Hindu, 8=Buddhist, 0=Ors Covariate In thousand US-Dollar 15 Table 11 Players per Stake for No Limit Holdem Limit (Small Blind/Big Blind in $) Stakes % 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 Culture Results Turkey-Test Culture (I) Culture (J) Average Sample (n=161) Standard 95%-confidence interval difference (I-J) error p-value Lower bound Upper bound Orthodox Islamic.717* African.713* Western Latin American.577* Sinic.789* Hindu.670* Buddhist.649* Western Islamic.541* African.538* Orthodox Latin American.401* Sinic.614* Hindu Buddhist Western -.717* Orthodox -.541* UNLV Gaming Research & Review Journal Volume 16 Issue 1 17

12 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 Culture Results Turkey-Test Culture (I) Culture (J) Average Sample (n=161) Standard 95%-confidence interval difference (I-J) error p-value Lower bound Upper bound Orthodox Islamic.717* African.713* Western Latin American.577* Sinic.789* Hindu.670* Buddhist.649* Western Islamic.541* African.538* Orthodox Latin American.401* Sinic.614* Hindu Buddhist Western -.717* Orthodox -.541* African Islamic Latin American Sinic Hindu Buddhist Western -.713* Orthodox -.538* Islamic African Latin American Sinic Hindu Buddhist Western -.577* Orthodox -.401* Latin- American Islamic African Sinic Hindu Buddhist Western -.789* Orthodox -.614* Islamic Sinic African Latin American Hindu Buddhist Western -.670* Orthodox Islamic Hindu African Latin American Sinic Buddhist Western -.649* Orthodox Islamic Buddhist African Latin American Sinic Hindu Table 12: Results Turkey-Test. *: p < References Cabot, A., & Hannum, R. (2008). Poker: Public Policy, Law, Mamatics and Future an American Tradition. T.M. Cooley Law Review, 22, Chen, B., & Ankenman, J. (2006). The Mamatics Poker. Pittsburgh : ConJelCo. DeDonno, M.A., & Detterman, D.K. (2008). Poker Is a Skill. Gaming Law Review and Economics, 12, Dreef, M., & Borm, P., & van der Genugten, B. (2003). On Strategy and Relative Skill in Poker. 18 International UNLV Gaming Game Research Theory Review, & Review 5, Journal Volume 16 Issue 1 Fiedler, I.,& Wilcke, A.-C. (2012). Online Poker in European Union. Gaming Law Review and Economics, 16, Fiedler, I., & Wilcke, A.-C. (2011). Der Markt für Onlinepoker: Spielerherkunft und Spielerverhalten.

13 References Cabot, A., & Hannum, R. (2008). Poker: Public Policy, Law, Mamatics and Future an American Tradition. T.M. Cooley Law Review, 22, Chen, B., & Ankenman, J. (2006). The Mamatics Poker. Pittsburgh : ConJelCo. DeDonno, M.A., & Detterman, D.K. (2008). Poker Is a Skill. Gaming Law Review and Economics, 12, Dreef, M., & Borm, P., & van der Genugten, B. (2003). On Strategy and Relative Skill in Poker. International Game Theory Review, 5, Fiedler, I.,& Wilcke, A.-C. (2012). Online Poker in European Union. Gaming Law Review and Economics, 16, 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, Haruyoshi, S. (2005). Internet Poker: Data Collection and Analysis, Brown University. H2GC. (2009). egambling data bulletin Q3/Q4 report. H2 Gambling Capital. Internet World Stats. (June 2010). Huntington, S. P. (1996). The Clash 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, Turner, N. E. (2008). Viewpoint: Poker Is an Acquired Skill. Gaming Law Review and Economics, Williams, R. J., & Wood, R. T. (2004). The proportion gaming revenue derived from problem gamblers: Examining issues in a Canadian context. Analyses Social Issues and Public Policy, 4, UNLV Gaming Research & Review Journal Volume 16 Issue 1 19

14 20 UNLV Gaming Research & Review Journal Volume 16 Issue 1