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THE GAMBLING HABITS OF ONLINE POKER PLAYERS

Authors: Ingo Fiedler;

THE GAMBLING HABITS OF ONLINE POKER PLAYERS

Abstract

Online poker is a data goldmine. Recording actual gambling behavior gives rise to a host of research opportunities. Still, investigations using such data are rare with the exception of nine pioneering studies by Harvard Medical School which are reviewed here. This paper fills part of the vacuum by analyzing the gambling habits of a sample of 2,127,887 poker playing identities at Pokerstars over a period of six months. A couple of playing variables are operationalized and were analyzed on their own as well as connected with each other in form of the playing volume ($ rake a player has paid in a time frame).The main findings confirm the results of the Harvard studies: most online poker players only play a few times and for very low stakes. An analysis of the relationship between the playing habits shows that they reinforce each other with the exception of the playing frequency which moderates gambling involvement. The average values of the playing habits are considerably higher due to a small group of intense players: the 99% percentile player has a playing volume that is 552 times higher than that of the median player (US$2,685), and 1% of the players account for 60% of playing volume (10% for even 91%). This group is analyzed more thoroughly, and a discussion shows that the first impulse to peg intense players as (probable) pathological gamblers is wrong. Rather, future research is needed to distinguish problem gamblers from professional players. 

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Top 10%
Top 10%
Average
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