Chapter 2: Predicting the Future: Variance and Sample Outcome
Hey peeps, very late, but finally here, a disussion on Chapter 2 of the book. The assignment for this week can be found here
Some discussion obviously I want to hear your opinion on it for my 1k post :)
Do you think the distribution of poker winrates follows the central limit theorem? After all, they are not really random.
How do you deal with having periods where you play on tilt affect your WR/variance, or do you think that is part of your poker distribution and that it all "evens out"?
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No comments yet? To make 3c) a bit easier, just can assume you are playing 100,000 hands and then calculate your ROR and tell me which has less ROR.
"Do you think the distribution of poker winrates follows the central limit theorem? After all, they are not really random."
Are you talking about the winrates of a particular individual (ie, session to session winrates) or the winrates of the aggregate of poker players? In the first case, I believe the results are "random enough" (in a very non-mathematical measure). Table conditions are always shifting, the player's state of mind is always shifting, and his skill (presumably) increases with time. However, despite all of these things (and assuming that the sessions are "long enough"), I think the central limit theorem should eventually take over and you'll get a bell curve-like distribution.
In the second case, I expect a bi-modal distribution. Even though there are many players who are approximately break-even, there are very many very bad players, who will create one large negative peak, and another smaller peak where the winning players congregate.
(I don't believe there are many "break-even" players out there... I believe many of the "break-even players" turn the corner and become winning players, and many of the remaining ones simply give up and do something else with their time.)
"How do you deal with having periods where you play on tilt affect your WR/variance, or do you think that is part of your poker distribution and that it all "evens out"?"
I believe that it all evens out if I continue to play well, but this does not mean that poor luck does not affect my play. If I start playing poorly due to tilt, I stop playing because this causes me to get a random result from my "tilt-playing distribution" as opposed to my "good-playing distribution."
Unlike Aaron I do believe the distribution of poker winrates will follow the central limit theorem? By that I mean there will be a bell curve and not some multi-nodal distribution.
We are dealing with a relatively large population here, and I will also posit that the majority of players are losers who can afford it. Fast losers die quickly and go away while I hypothesize that winners will tend to move up in stakes until they become one of the slow bleeders (sort of a poker version of the Peter Principle). I do certainly believe that the curve will be somewhat flatter in the center around the mean and at some point will start to fall quickly off a cliff for winners. The tail will be longer for losers.
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