Standard Deviation vs. Monte Carlo Simulation

[Poseidon65]

I've read that a lot of players think it's important to know what your standard deviation is, so you know whether or not your swings are within ranges that are to be expected given your variance.

However, it doesn't seem that standard deviation is very useful for this purpose. This is because the distribution of each indiviaul hand is not even close to normally distributed. I suppose you could argue (using the Central Limit Theorem) that if you play enough hands, the non-normality of the distribution isn't really relevant. I'm not sure to what degree it's relevant or not, so I thought I'd try something that might be more robust than standard deviation here.

So I've been logging the result of every Paradise hand I've played for the last few days (about 450 hands). Once I play a few thousand hands, I should enough data to determine with good accuracy the distribution of an individual hand is, for me, at least.

Once I have this, let's say I play 100 hands, I get some result for those hands (for example, down 25BB), and I want to know the probability of that happening. I use a Monte Carlo simulation, drawing 100 hands randomly from my distribution, and do so 1,000,000 times. Now, I can simply see what percentage of these 1,000,000 results lie above -25BB for the 100 hands.

I wonder if anyone's ever thought of doing this, or if it sounds like a waste of time. I definitely want to see how normal the distribution of an entire session is, and this seems like a pretty good way to find out. It also seems that this would be more accurate in finding out just how good or bad a particular session really is.