I just read this thread about Gus Hansen´s losses on FTP.
http://forumserver.twoplustwo.com/29...78/index2.html
Given that the numbers seemed so huge, I tried to find out how much he *actually* lost in terms of bb/100. I just used a somewhat random, but not totally improbable number of $600 for the average big blind (he did play lots of 200/400, 500/1000 heads-up, 1000/2000 FLO8, 2000/4000 FLO8 etc.) and the average loss rate would be 2bb/100 in that scenario. (I do realize how this is a bit of a simplification, in that Hansen won most of his money from playing nosebleed FLO8 and 500/1000 heads-up PLO before FTP went down and he suffered insane losses once FTP came back.)
Now for my actual question: I did put both the numbers for loss rate and hands played into this variance simulator
http://pokerdope.com/poker-variance-calculator/. I assumed that Hansen might actually turn out to be a break even player (EV bb/100 at 0) playing insanely volatile poker (Standard deviation of 250 hehe). This would obviously still be a pretty stupid thing to do, but vastly different from whales who get crushed and have no shot whatsoever at winning over decent samples.
What I came up with is this:
Quote:
Variance in numbers
EV (»?«) 0.00 BB/100
Standard deviation (»?«) 250.00 BB/100
Hands(»?«) 1600000
Expected winnings (»?«) 0.00 BB
Standard deviation after 1600000 hands (»?«) 31623 BB
1.98 BB/100
70% confidence interval (»?«) [-31623 BB, 31623 BB]
[-1.98 BB/100, 1.98 BB/100]
95% confidence interval (»?«) [-63246 BB, 63246 BB]
[-3.95 BB/100, 3.95 BB/100]
Probability of loss after 1600000 hands (»?«) 50.0000%
Probability of running at or above observed win rate (10.00 BB/100) over 1600000 hands with a true win rate of 0.00 BB/100 (»?«) 0.0000%
Probability of running below observed win rate (10.00 BB/100) over 1600000 hands with a true win rate of 0.00 BB/100 (»?«) 100.0000%
Minimum bankroll for less than 5% risk of ruin (»?«) Infinity BB
Again, I do realize that my "model" is based on several (simplified) assumptions that may or may not be true. Now that I go those results, the question is how to interpret them. Let´s assume the underlying data are *somewhat* correct, would it be correct make the following interpretation: if Gus is a break-even player with massive variance, the chances of him winning or losing somewhere between +1,98bb/100 and -1,98bb/100 over 1.6m hands are 70 % while the chances of him winning or losing somewhere between +3,98bb/100 and -3,98bb/100 are 95 %? If this is correct, would it also be correct to asssume that a 2bb/100 loser with those stats might be a break-even player on the very bottom end of potential variance?
I´m just playing devil´s advocate here (obv. no clue how these games actually play), but I thought it would be a fun exercise amid all the Gus Hansen bashing.

Any comments/criticisms are appreciated.
Last edited by mumpfmampf; 08-12-2014 at 04:06 AM.