[StellarWind]

Well I see that your post is still here.

Maybe I was mistaken in thinking this was just a clever off-topic plug for your investment business website [img]/images/graemlins/confused.gif[/img]?

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I believe there is a major, possibly even fatal flaw with all the work currently done on gambling variance - the use of standard deviation as the measure of the variance being measured. SD assumes the distribution of observations is symmetrical around the mean; i.e., that the normal distribution is the correct statistical model to use. However, this is often not the case:

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I'm very surprised to hear that the normal distribution is not a good approximation for ring-game poker, blackjack, and similar gambling activities. Do you have any evidence or explanation for this?

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a successful player will have a significantly positively skewed distribution of returns, and vice versa for the consistent loser. This means that even if the better player has a high SD, much of it will be "good" variance. Again, vice versa for the loser.

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This is just a restatement of the obvious fact that the distribution of a winning player's results will be centered on a positive mean (his win rate). It in no way shows that the distribution is not normal.

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This means that even if the better player has a high SD, much of it will be "good" variance.

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Again, why would I expect favorable and unfavorable variance events to be anything other than equal (on average)? Surely you are not going to define "bad" variance to mean losing? A month where you only win half as much as your average rate is certainly an example of unfavorable variance.

All the research I've read on bankroll requirements fully integrates a player's win rate as an important parameter. Everyone knows that a winning player's results are "positively skewed". Unless you can back up your assertion that results are not approximately normally distributed, you have added nothing.