Abstract
Poker is a competitive, social game of skill and luck, which presents players with numerous challenging strategic and interpersonal decisions. The adaptation of poker into a game played over the internet provides the unprecedented opportunity to quantitatively analyze extremely large numbers of hands and players. This paper analyzes roughly twenty-seven million hands played online in small-stakes, medium-stakes and high-stakes games. Using PokerTracker software, statistics are generated to (a) gauge the types of strategies utilized by players (i.e. the ‘strategic demography’) at each level and (b) examine the various payoffs associated with different strategies at varying levels of play. The results show that competitive edges attenuate as one moves up levels, and tight-aggressive strategies––which tend to be the most remunerative––become more prevalent. Further, payoffs for different combinations of cards, varies between levels, showing how strategic payoffs are derived from competitive interactions. Smaller-stakes players also have more difficulty appropriately weighting incentive structures with frequent small gains and occasional large losses. Consequently, the relationship between winning a large proportion of hands and profitability is negative, and is strongest in small-stakes games. These variations reveal a meta-game of rationality and psychology which underlies the card game. Adopting risk-neutrality to maximize expected value, aggression and appropriate mental accounting, are cognitive burdens on players, and underpin the rationality work––reconfiguring of personal preferences and goals––players engage into be competitive, and maximize their winning and profit chances.
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Notes
While the controversial UIGEA (Unlawful Internet Gambling Enforcement Act) in the United States, and similar restrictions on money flows in online commerce and poker in other jurisdictions, have tempered this growth somewhat in recent years, both online and live poker remain much larger than they were prior to the internet boom.
Due to computational limitations, only 120,000 of these NL50 players could be rated by PokerTracker. Regardless, this is still an enormous sample.
While this methodology is ideal for understanding the actual behaviors and consequences of players, from a “pure poker” standpoint the theoretical relationships between strategies, win rates and variance (i.e. standard deviations of win rates) would be better handled by simulation studies, which could keep player abilities, interactions, games and luck constant.
Other implications of this is that players will tend to play more tentatively and protect small wins, and play more aggressively during a losing session, desperately trying to get even at any risk or cost.
The only way to get information about folded hands would be to receive data directly from online poker companies themselves. Thus far, such sites have been very reluctant to release data.
Poker is rife with emotion work (Hochschild 1983) as well, from concealing one’s true feelings about the situations they are in (i.e. “keeping a poker face”) and dealing with adversity and maintaining emotional control and staying off “tilt”, where emotions cloud the ability of a player to make intelligent and optimal decisions (Browne 1989).
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Acknowledgments
The author thanks Tony Puddephatt, H. Ted Welser, Robb Willer and the Cornell Social Psychology Lab Group for helpful feedback and suggestions in regards to this project. The author also acknowledges Kim Burlingame, Janet Heslop and the Cornell Institute for Social and Economic Research for providing the computing resources and support necessary to make this project possible. Finally, the author is very grateful to HandHQ.com for providing access to the data which this research is based upon.
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Siler, K. Social and Psychological Challenges of Poker. J Gambl Stud 26, 401–420 (2010). https://doi.org/10.1007/s10899-009-9168-2
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DOI: https://doi.org/10.1007/s10899-009-9168-2