A modest improvement in winrate estimation after Poker EV..

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I used PokerEV to adjust the all in hands that fell outside 3 standard deviations from my mean winrate.

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Why didn't you adjust all of the all-in hands?


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I did this because these were the number of hands I checked and verified and I didn't want to do all of them.

Also, as I said in the article, the allin hands for smaller amounts didn't affect the calculated winrate or standard deviation much.

But as far as actual % changes in winrate and standard deviation for this particular dataset isn't as much the point as seeing what other's have.

My analysis was more of a case report which I had hoped would spark enough interest for a 2+2 epidemiological study. If you look through the Software thread on Poker EV (and other forums), you get an idea of the average winrates people see. It might be interesting to see what others report for standard deviations calculated from PokerEV-adjusted datasets.

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Other thoughts in general

What I'm am hypothesizing is that the average and standard deviation from the Poker EV are better predictors of the mean of the population and confidence intervals than the same from the raw data. I putting this as a hypothesis, not this is absolutely wrong or right.

Here are the choices for assessing the mean and standard deviation of the population. Numbers which are necessary for assessing the true mean winrates of a player, confidence intervals of that mean, bankroll considerations, etc...

1. Standard way. Average and standard deviation from raw data.
2. What I suggested in my article. Average and standard deviations from Poker EV-adjusted data.
3. Average from Poker EV and standard deviation from raw data.
4. Bayes choice. I suppose you could take the average from Poker EV and a Bayes standard deviation based on the standard deviations from
2+2 posters, those with similar winrates, similar playing styles or some combination.
5. I suppose there are other ways.