Are Winrates Normally Distributed?

[sthief09]

oh i see what youre getting at. they have to be completely independent. by choosing blocks of hands, it assumes dependence since each has an equal likelihood of being picked and thus isnt random?

[sfer]

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It looks pretty close to normal here, but I think the way you did the sampling is not quite right.

You need to draw random samples from the total group of hands. Chopping them up into blocks is easier, but not appropriate. The way you have done it, we might find a different results using a different database.

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You probably want to draw largish random blocks of hands to randomize position as much as possible between the samples.

[MaxPower]

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It looks pretty close to normal here, but I think the way you did the sampling is not quite right.

You need to draw random samples from the total group of hands. Chopping them up into blocks is easier, but not appropriate. The way you have done it, we might find a different results using a different database.

[/ QUOTE ]

You probably want to draw largish random blocks of hands to randomize position as much as possible between the samples.

[/ QUOTE ]

OK, I figured out a way to do this in SPSS. I thought the data file would crash my computer but it doesn't

I have a data file with the amount I won/lost for 164,724 hands at 15/30. My win rate over these hands is a pitiful 1.13BB/100.

How large should the samples be and how many should I pull? I was thinking of selecting 10,000 samples of 1000 hands each.

Then I can I plot them and get the skewness, kurtosis, etc.

[IndieMatty]

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It looks pretty close to normal here, but I think the way you did the sampling is not quite right.

You need to draw random samples from the total group of hands. Chopping them up into blocks is easier, but not appropriate. The way you have done it, we might find a different results using a different database.

[/ QUOTE ]

You probably want to draw largish random blocks of hands to randomize position as much as possible between the samples.

[/ QUOTE ]

OK, I figured out a way to do this in SPSS. I thought the data file would crash my computer but it doesn't

I have a data file with the amount I won/lost for 164,724 hands at 15/30. My win rate over these hands is a pitiful 1.13BB/100.

How large should the samples be and how many should I pull? I was thinking of selecting 10,000 samples of 1000 hands each.

Then I can I plot them and get the skewness, kurtosis, etc.

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I just had a grim college statistics flashback when you mentioned SPSS. And I am now scared of this thread. You guys are so much smarter than me. [img]/images/graemlins/frown.gif[/img]

[MaxPower]

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It looks pretty close to normal here, but I think the way you did the sampling is not quite right.

You need to draw random samples from the total group of hands. Chopping them up into blocks is easier, but not appropriate. The way you have done it, we might find a different results using a different database.

[/ QUOTE ]

You probably want to draw largish random blocks of hands to randomize position as much as possible between the samples.

[/ QUOTE ]

OK, I figured out a way to do this in SPSS. I thought the data file would crash my computer but it doesn't

I have a data file with the amount I won/lost for 164,724 hands at 15/30. My win rate over these hands is a pitiful 1.13BB/100.

How large should the samples be and how many should I pull? I was thinking of selecting 10,000 samples of 1000 hands each.

Then I can I plot them and get the skewness, kurtosis, etc.

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I just ran 1000 samples of 1000 hands each from this list and from the graph and statistics, I am convinced that the win rates for these samples are normally distributed.

I can run a larger job overnight and then post some pretty pictures and stats for you.

[jetsonsdogcanfly]

I don't think that tells you anything particularly usefull. A distribution of randomly sampled means will necessarily converge to normality, but doesn't give you any new volatility information.

[Justin A]

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It looks pretty close to normal here, but I think the way you did the sampling is not quite right.

You need to draw random samples from the total group of hands. Chopping them up into blocks is easier, but not appropriate. The way you have done it, we might find a different results using a different database.

[/ QUOTE ]

You probably want to draw largish random blocks of hands to randomize position as much as possible between the samples.

[/ QUOTE ]

OK, I figured out a way to do this in SPSS. I thought the data file would crash my computer but it doesn't

I have a data file with the amount I won/lost for 164,724 hands at 15/30. My win rate over these hands is a pitiful 1.13BB/100.

How large should the samples be and how many should I pull? I was thinking of selecting 10,000 samples of 1000 hands each.

Then I can I plot them and get the skewness, kurtosis, etc.

[/ QUOTE ]

I just ran 1000 samples of 1000 hands each from this list and from the graph and statistics, I am convinced that the win rates for these samples are normally distributed.

I can run a larger job overnight and then post some pretty pictures and stats for you.

[/ QUOTE ]

Max,
Can you do this with sets of 100 hands? I understand that once the sample sizes get large enough the sampling distribution will be about normal, but I'm wondering how close the bb/100 statistic gets us there.

[sfer]

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It looks pretty close to normal here, but I think the way you did the sampling is not quite right.

You need to draw random samples from the total group of hands. Chopping them up into blocks is easier, but not appropriate. The way you have done it, we might find a different results using a different database.

[/ QUOTE ]

You probably want to draw largish random blocks of hands to randomize position as much as possible between the samples.

[/ QUOTE ]

OK, I figured out a way to do this in SPSS. I thought the data file would crash my computer but it doesn't

I have a data file with the amount I won/lost for 164,724 hands at 15/30. My win rate over these hands is a pitiful 1.13BB/100.

How large should the samples be and how many should I pull? I was thinking of selecting 10,000 samples of 1000 hands each.

Then I can I plot them and get the skewness, kurtosis, etc.

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I think 1K hand chunks is probably large enough.

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It looks pretty close to normal here, but I think the way you did the sampling is not quite right.

You need to draw random samples from the total group of hands. Chopping them up into blocks is easier, but not appropriate. The way you have done it, we might find a different results using a different database.

[/ QUOTE ]

Exactly.

It looks like the effect that you're seeing is caused by the fact that playing badly costs you more than playing well earns you. Your $/hand should still be normally distributed, but not if you break it up into temperal blocks because you're more likely to see effects of tilt or playing poorly (no offense intended) or bad tables, etc.

[PTjvs]

My gut instinct says this is because the raw +$/hand won is greater than the -$/hand you lose (due to some pots being multiway). Generally speaking, your small chunks of hands will break down as follows:

1) Many where you don't win big pots and you are slightly -$

2) Slightly fewer where you win enough hands to have a small +$

3) A few where you win a big hand or two & are very +$.

4) A few where you lose a few big pots & are very -$ (less however than your big +$ chunks, since due to multiway pots, the amount you WIN if big pots is larger than the amount you LOSE in the same big pot)

This should leader to a bell curve is NOT evenly distributed, but peaks on the - side, with the difference being made up by the curve coming down less steeply on postive end.

I hope I described that well, I'd do one in MS paint, but I'm a very bad artist.

if i am correct however, if you took the same data from HU play, it SHOULD look like a normal bell curve, as the effect of multiway play is completely eliminated.

jvs