[stinkypete]

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from looking at the q-q plot, it seems that the upper tail of the poker data is fatter than the gaussian, and the lower is thinner. shouldn't this result in large downswings happening less often than a normal assumption would predict?

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yes, but not really. what it really suggests is that really bad 100-hand stretches would happen less often than a normal distribution would predict. big downswings occur over 10,000 or more hands, and the sample size should reflect that if you want to know how likely big downswings are. over that many hands though, it should be very very close to a normal distribution, considering that this is quite close as well (even though everyone's saying it's not for some reason... just look at the damn graph, it's pretty close).

anyway, i say "yes, but not really" because the chart with 10,000 samples should be shaped somewhere between this and a normal distribution. it should differ from a normal distribution in the same way, though not by nearly as much. (someone correct me if i've misunderstood on this)

on the other hand, while the shape suggests that really terrible downswings should happen less than in a normal distribution, it also suggests that pain-in-the-ass break-even streaks should be longer on average than a normal distribution suggests, since that's what will happen when you don't get any of those rare, winrate boosting surges that are illustrated on the far right of the graph. you can see this by noting that the graph is "fatter" on the left side of the mean than the right side (the bars near the mean are above the normal on the left side, and below the normal on the right side).

edited some errors. i dont know why i can't type what i'm thinking the first time.