vpFREE2 Forums

vp non random results

#1

rockofjello333 wrote ....

We have been deeply suspicious for several years about the honesty
and randomness of casino VP machines. We have posted both specific
observations and data on anomalous results that were drawn from not
only our own experiences, but several other high-level VP players'
as well. The basic conclusion that we have both drawn, and often
articulated, is that there's far too much smoke for there not to be
some fire.

Rock,

If you have the specific observations, I'd be interested in seeing them. We can then determine
if these indicate a problem with the machine, just bad luck, or the data collection method is suspect.

It's easy to see 'anomalous results' after the fact. If you want to do an interesting exercise, next time you
play, record the results, hand by hand, for your session. If you play a couple of hours and record 1000 hands,
you should be able to see 'something funny' in the results when you are done. During almost any session,
you will be able to see 'something funny'.

If you want to prove your hypothesis, you need to figure out what you are looking for before you start. Write down
what you are trying to prove and how you are going to prove, collect the data and then analyse it. If you collect data, then
create your hypothesis, you can 'prove' just about anything.

Send me your data, either to vpfree or by private email and I'll take a look. We all want to believe that vp is fair. I'd be
happy to look at evidence to the contrary.

···

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#2

The data would be interesting to see. It would take a large sample
size to statisticlally check for the randomness of vp results.
Dale

--- In vpFREE@yahoogroups.com, "John.G.Zaroff" <John.G.Zaroff@d...>
wrote:

rockofjello333 wrote ....

We have been deeply suspicious for several years about the honesty
and randomness of casino VP machines. We have posted both specific
observations and data on anomalous results that were drawn from not
only our own experiences, but several other high-level VP players'
as well. The basic conclusion that we have both drawn, and often
articulated, is that there's far too much smoke for there not to be
some fire.

Rock,

If you have the specific observations, I'd be interested in seeing

them. We can then determine

if these indicate a problem with the machine, just bad luck, or the

data collection method is suspect.

It's easy to see 'anomalous results' after the fact. If you want

to do an interesting exercise, next time you

play, record the results, hand by hand, for your session. If you

play a couple of hours and record 1000 hands,

you should be able to see 'something funny' in the results when

you are done. During almost any session,

you will be able to see 'something funny'.

If you want to prove your hypothesis, you need to figure out what

you are looking for before you start. Write down

what you are trying to prove and how you are going to prove,

collect the data and then analyse it. If you collect data, then

create your hypothesis, you can 'prove' just about anything.

Send me your data, either to vpfree or by private email and I'll

take a look. We all want to believe that vp is fair. I'd be

···

happy to look at evidence to the contrary.

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******************

Note: The information contained in this message may be privileged

and confidential and thus protected from disclosure. If the reader of
this message is not the intended recipient, or an employee or agent
responsible for delivering this message to the intended recipient,
you are hereby notified that any dissemination, distribution or
copying of this communication is strictly prohibited. If you have
received this communication in error, please notify us immediately by
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#3

Being a confirmed skeptic, I decided the only way to determine the
machines' integrity was by actual results.

Problem is one person can't realistically generate "long term"
numbers. BUT, if you looked at hands with smaller cycles (occurs
every few hundred hands vs every 40,000 hands), maybe the
statisticians in this group could draw some better conclusions.

Therefore I decided to count all FH/FL/ST results - "specials" as I
call them, which frequently determine a winning/non-winning session
of Pickem.

Put another way, I wanted to know if FH/FL/ST occurs with the
mathematical expectancy show by Winpoker (which we take on faith as
being correct - It's the ONLY source of hand frequencies I've ever
been able to locate for Pickem).

Before the late John Frey passed away, he alluded to a group in AC
who were tracking their play. I had always meant to mail him
privately to get in touch with this group. My understanding was that
they were pooling their results to better approximate the long term.

Here are my results from a year's play at Pickem Poker.

I keep detailed notes every session, of the following:
- hands played, denomination.
- coins won/lost.
- dealt triplets.
- winning hands of Straight or better.

Hands played 126,691

Hand # times Actual Theo. Result Stats
                        Freq. Cycle Correlation

RF 1 126,691 351,818 > avg weak
SF 2 63,346 38,451 < avg weak
4K 50 2,534 2,361 < avg mediocre

FH 278 455 424 < avg ??
FL 371 341 314 < avg ??
ST 643 197 197 = avg ??
FH/FL/ST 1292 98 94 < avg ??

It's obviously silly to try to draw any conclusions on the big hands.
My questions for you math types are related to the "specials":

(1) I've read references that we need several million hands to
approximate the long term. If, say, 4 million hands (100 RF cycles)
is sufficient to draw meaningful conclusions, I ask you if 300 FH
cycles is sufficient to draw equally meaningful conclusions?

(2) If your response is negative, then I ask, how many hands do we
need to draw conclusions of FH/FL/ST integrity?

Finally, for the rest of the readers, is there anyone else out there
who has numbers they'd like to share/pool with me? If so please
contact me privately.

Brian

···

--- "John.G.Zaroff" <John.G.Zaroff@delphi.com> wrote:

If you want to prove your hypothesis, you need to figure out what
you are looking for before you start. Write down
what you are trying to prove and how you are going to prove,
collect the data and then analyse it. If you collect data, then
create your hypothesis, you can 'prove' just about anything.

Send me your data, either to vpfree or by private email and I'll
take a look. We all want to believe that vp is fair. I'd be
happy to look at evidence to the contrary.

_______________________________
Do you Yahoo!?
Win 1 of 4,000 free domain names from Yahoo! Enter now.
http://promotions.yahoo.com/goldrush

#4

I've quite often noticed when drawing 3 cards,that 3 to a royal, pops up.Seems this happens way too often.What are the odds of 3 to a royal popping up on 3 card draw?

···

----- Original Message -----
  From: John.G.Zaroff
  To: vpfree@yahoogroups.com
  Sent: Thursday, September 09, 2004 6:24 AM
  Subject: [vpFREE] vp non random results

  rockofjello333 wrote ....

  We have been deeply suspicious for several years about the honesty
  and randomness of casino VP machines. We have posted both specific
  observations and data on anomalous results that were drawn from not
  only our own experiences, but several other high-level VP players'
  as well. The basic conclusion that we have both drawn, and often
  articulated, is that there's far too much smoke for there not to be
  some fire.

  Rock,

  If you have the specific observations, I'd be interested in seeing them. We can then determine
  if these indicate a problem with the machine, just bad luck, or the data collection method is suspect.

  It's easy to see 'anomalous results' after the fact. If you want to do an interesting exercise, next time you
  play, record the results, hand by hand, for your session. If you play a couple of hours and record 1000 hands,
  you should be able to see 'something funny' in the results when you are done. During almost any session,
  you will be able to see 'something funny'.

  If you want to prove your hypothesis, you need to figure out what you are looking for before you start. Write down
  what you are trying to prove and how you are going to prove, collect the data and then analyse it. If you collect data, then
  create your hypothesis, you can 'prove' just about anything.

  Send me your data, either to vpfree or by private email and I'll take a look. We all want to believe that vp is fair. I'd be
  happy to look at evidence to the contrary.

  ****************************************************************************************

  Note: The information contained in this message may be privileged and confidential and thus protected from disclosure. If the reader of this message is not the intended recipient, or an employee or agent responsible for delivering this message to the intended recipient, you are hereby notified that any dissemination, distribution or copying of this communication is strictly prohibited. If you have received this communication in error, please notify us immediately by replying to the message and deleting it from your computer. Thank you.

  ****************************************************************************************

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#5

standard deviation = sqrt(cycles)
so at 10 cycles one standard deviation is 10 (the average) +/- 3.16
at 100 cycles one standard deviation is 100 +/- 10
one standard deviation is 68%
two standard deviations is 95%
three standard deviations is 99.7%
risk of ruin (chance of being below the distribution) is half again as
large:
1sd=15.87%,2sd=2.28%,3sd=0.14%

cycles 1sd 2sd 3sd
10 3.2 6.3 9.5
25 5 10 15
100 10 20 30
1000 32 63 95

full houses, flushes and straights are typically about 90 hand cycles
so by 100 cycles (9,000 hands) one would expect 100 +/-20 (80 to 120)
95% of the time and 100 +/-30 (70 to 130) 99.7% of the time

on a royal cycle of 40,000 hands, by a million hands (25 cycles), one
would expect to have 25 royals +/-10 (15 to 35) with 2.28% risk of
ruin (less than 15 or short more than 10 royals)

on an even game you would be down more than 10 royals 2.28% of the
time
if you were playing with a 1% average advantage you would be down half
of that assuming the royal returns 2% which it generally does

#6

for pick'em at 126,691 hands one would expect:

hand cycle average_results std_dev
rf 351817.7432 0.360104067 0.600086716
sf 38450.99491 3.29486923 1.815177465
4k 2360.82785 53.66380272 7.325558185
fh 424.3904671 298.5246131 17.27786483
fl 313.5907458 404.0010801 20.09977811
st 197.4125082 641.7577143 25.33293734
3k 33.31706292 3802.586089 61.66511242
2p 16.10626031 7865.947624 88.69017772
9+ 4.378761164 28933.06925 170.0972347
00 1.495977079 84687.79485 291.0116748

#7

jacks or better at 4,000 hands:

hand cycle average_results standard_deviation
4k 423.2729303 9.450167289 3.074112439
fh 86.8643158 46.04882872 6.785928729
fl 90.78932498 44.05804318 6.637623308
st 89.05221365 44.91746848 6.702049573
3k 13.43206823 297.7947946 17.25673186
2p 7.735214107 517.115615 22.74017623
J+ 4.660157329 858.3401199 29.29744221
0 1.833400128 2181.738693 46.70908577

#8

Since in all vp the payoff distribuion is very skewed the 1, 2, and
3 standard deviation rules do not apply. One has to look at the
distribution of the theoretical payoffs not the normal
approximation. The difference is a huge deal if you are measuring 1
or 2% advantages
Dale

--- In vpFREE@yahoogroups.com, "nightoftheiguana2000"
<nightoftheiguana2000@y...> wrote:

standard deviation = sqrt(cycles)
so at 10 cycles one standard deviation is 10 (the average) +/- 3.16
at 100 cycles one standard deviation is 100 +/- 10
one standard deviation is 68%
two standard deviations is 95%
three standard deviations is 99.7%
risk of ruin (chance of being below the distribution) is half

again as

large:
1sd=15.87%,2sd=2.28%,3sd=0.14%

cycles 1sd 2sd 3sd
10 3.2 6.3 9.5
25 5 10 15
100 10 20 30
1000 32 63 95

full houses, flushes and straights are typically about 90 hand

cycles

so by 100 cycles (9,000 hands) one would expect 100 +/-20 (80 to

120)

95% of the time and 100 +/-30 (70 to 130) 99.7% of the time

on a royal cycle of 40,000 hands, by a million hands (25 cycles),

one

would expect to have 25 royals +/-10 (15 to 35) with 2.28% risk of
ruin (less than 15 or short more than 10 royals)

on an even game you would be down more than 10 royals 2.28% of the
time
if you were playing with a 1% average advantage you would be down

half

···

of that assuming the royal returns 2% which it generally does

#9

rosspark100 wrote:

Since in all vp the payoff distribuion is very skewed the 1, 2, and
3 standard deviation rules do not apply. One has to look at the
distribution of the theoretical payoffs not the normal
approximation. The difference is a huge deal if you are measuring 1
or 2% advantages

I've seen allusion to this "skewness" before.

A discussion of vp deviation can reference many things. If we're
talking about the frequency with which any single hand occurs, I
believe (though am willing to own up to being less than 100%
confident) that the normal distribtion applies. And so, one could
measure that frequency over a statistically sitnificant number of
hands for a measure of game fairness.

But total game return is skewed (nor normally distributed) because of
the differing frequency of the hands whose payout comprise total game
return.

And, over the VERY long term, even game return approximates normal
distributed, no?

- Harry

#10

for hands < cycle normal distribution doesn't apply
for hands > cycle normal distribution applies
for example, at hands less than royal cycle greater than straight
flush cycle you should subtract the royal contribution from the er and
variance ... etc.
short term skewed curves:
http://www.jazbo.com/videopoker/curves.html
exact risk of ruin and bankroll calculation:
http://www.gamblingtools.net/vp/vpanalyzer.html
distribution simulator:
http://www.lotspiech.com/GamblersRuin.html

Since in all vp the payoff distribuion is very skewed the 1, 2, and
3 standard deviation rules do not apply. One has to look at the
distribution of the theoretical payoffs not the normal
approximation. The difference is a huge deal if you are measuring 1
or 2% advantages
Dale

--- In vpFREE@yahoogroups.com, "nightoftheiguana2000"
<nightoftheiguana2000@y...> wrote:
> standard deviation = sqrt(cycles)
> so at 10 cycles one standard deviation is 10 (the average) +/-

3.16

···

--- In vpFREE@yahoogroups.com, "rosspark100" <rosspark100@h...> wrote:

> at 100 cycles one standard deviation is 100 +/- 10
> one standard deviation is 68%
> two standard deviations is 95%
> three standard deviations is 99.7%
> risk of ruin (chance of being below the distribution) is half
again as
> large:
> 1sd=15.87%,2sd=2.28%,3sd=0.14%
>
> cycles 1sd 2sd 3sd
> 10 3.2 6.3 9.5
> 25 5 10 15
> 100 10 20 30
> 1000 32 63 95
>
> full houses, flushes and straights are typically about 90 hand
cycles
> so by 100 cycles (9,000 hands) one would expect 100 +/-20 (80 to
120)
> 95% of the time and 100 +/-30 (70 to 130) 99.7% of the time
>
> on a royal cycle of 40,000 hands, by a million hands (25 cycles),
one
> would expect to have 25 royals +/-10 (15 to 35) with 2.28% risk of
> ruin (less than 15 or short more than 10 royals)
>
> on an even game you would be down more than 10 royals 2.28% of the
> time
> if you were playing with a 1% average advantage you would be down
half
> of that assuming the royal returns 2% which it generally does

#11

--- In vpFREE@yahoogroups.com, "Harry Porter" <harry.porter@v...>
wrote:

I've seen allusion to this "skewness" before.

an example of skewness:

let's take 10 cycles, the normal hit distribution is 10 +/- 3.16 for
68.26% of the data

using the poisson distribution, we can solve for the exact
distributions:
prob=exp(-cycles) x cycles^hits / hits!

hits probability
0 0.0045400%
1 0.0453999%
2 0.2269996%
3 0.7566655%
4 1.8916637%
5 3.7833275%
6 6.3055458%
7 9.0079226%
8 11.2599032%
9 12.5110036%
10 12.5110036%
11 11.3736396%
12 9.4780330%
13 7.2907946%
14 5.2077104%
15 3.4718070%
16 2.1698794%

10 +/-3 (7 to 13) = 73.4323002%
10 +/-6 (4 to 16) = 96.2622340%