It's assumed that your results over each set of 100 hands are independent and follow a Normal distribution with mean = win rate and standard deviation around 19.9. Over 27,000 hand sample, the mean result is 270*(winrate) and the standard deviation is sqrt(270)*(winrate). This comes from what happens when you add independent normally distributed random variables together.
So, to get the probability of being down 200 BB (or more) after 27k hands, you need to find the sum of the probabilities of all the outcomes <=-200 of a normally distributed variable with mean (270*winrate) and standard deviation around 320. If your a +2BB/hr player, your expected result for 27k hands is +540 BB. The result of -200BB is 740BB worse than the mean. 740 is a little less than 3 times the standard deviation. Because of the shape of the normal distribution, this translates to a little more than 1 in 400 chance that you will net -200 BB or worse over 27k hands.
Regarding the frequency of having a particular downswing: I have a formula for calculating the probability of reaching a particular "goal" at some time in the next N sets of 100 hands (I'm making the assumption that the result is observed only once every hundred hands). Its from Don Schlesinger's
Blackjack Attack You can email me at
stacksofblack@verizon.net if you would like to see it, its got hyperbolic sines and exponentials and stuff.
Another way you could do it would be to set up a simulation that creates runs of say 1 million hands calculates the number of times the bankroll dips X BB below a highpoint. However, you'd have to define what a downswing is. (when is the downswing over? do I have to recover back to the original point? If i lose 200 BB have i just experienced a 200 BB downswing AND a 150 BB downswing?) I can write some code in MATLAB for thsi and let you know what i come up with, if your interested.
HTH a guy who I could probably not help as much with a straight-up poker question.
KELVIN