Quote:
Originally Posted by Avaritia
heh, you’re going to catch me. I am not good at this and am paraphrasing from memory how I understand it (eek)
I think I should have said there are 2 expected values. There is an expected value hand vs hand and there is an expected win rate. Stdev bb/100 refers to win rate. Expected value hand for hand influences variance in expected win rate but it’s not the entire picture bc non showdown plays a part in wr. I think we misuse the term variance when we refer to AA losing to JJ but we use it correctly when we refer to winning/losing 100bbs in 100 hands.
I’m very close to approaching no f**king clue of what I’m talking about though. I used to know a lot more about this when it was fresh in my mind from college and when bright minds posted here often and I was truly interested in solving poker. (Most of that has died for me)
I’ve since learned to work on mental game, study common high level spots, and just shoot the ball
huh, had the popcorn all popped waiting for your explanation on that one. just shoot the ball is a nice save tho.
First - lol English. Variance has a ton of meanings, with at least a couple of meanings in the legal realm.
However, itt and in the forum in general, the word tends to be bastardized into a proxy for "run-bad" (and much less often - "run-good"). If we think our results suck over the last X hours compared to what we believe is our true edge, we say here the variance has been tough, or curse our variance, or poker variance is a killer, etc. This usage is related to the formal statistical definition, but people often use it itt without understanding that definition, even informally.
That definition, from the wiki page: In probability theory and statistics, variance is the expectation of the squared deviation of a random variable from its mean. Informally, it measures how far a set of (random) numbers are spread out from their average value.
For example, if a poker player had the following hourly results over 3 hours of play:
+400, -200, -800
The mean of those results is -200. The variance of these results is the average squared distance from the mean, or: (600^2 + 0^2 + 600^2)/3 = 240,000
Standard Deviation is the square root of variance, or in this case: ~489.90
research the Central Limit Theorem if you want to read up on how we can use observed mean and StnDev to make confidence interval estimates. Understand the CLT and a few distribution graphs and you are most of the way to earning 3 statistics credits (in the 80s, anyway).
(I'm ignoring the term "sample variance" in this poast as it is beyond scope and my understanding is rusty anyway.)
Last edited by sai1b0ats; 01-10-2018 at 02:16 AM.