
October 25th, 2011, 02:42 PM


Executive Member


Join Date: Mar 2007
Location: Las Vegas, NV
Posts: 8,683


Quote:
Originally Posted by zengrifter
well, i stand corrected! Zg


October 25th, 2011, 05:08 PM

Executive Member


Join Date: May 2007
Posts: 624


Quote:
Originally Posted by ericfarmer
Usually no more than 510%, but possibly as much as 25%. The attached plot shows the distribution of expected return (in % of initial wager) vs. penetration (in % of the 104 cards in the shoe), assuming CDZ strategy with ridiculously optimistic rules: S17, DOA, SPL4, RSA, with surrender.
The plot is a combination of simulation and combinatorial analysis: the blue points result from simulation of headsup play through 1000 shuffled shoes; each point is the expected return for a corresponding depleted shoe, assuming "perfect" play optimized for that depleted shoe. The red curves are 10, 20, 30, ..., 90th percentile curves of the blue points. So, for example, even at 80% penetration (only about 20 cards left), 90% of the time your EV is less than 9%.
Hope this helps,
Eric

Eric, is it possible for you to organize your data to display 2 plots representing average expected value at each penetration and average actual result of playing each hand with the best decisions? In other words you would be contrasting what has happened short term with what is expected long term.
The long term data would start with the same expected value which is full shoe EV at 0 cards pen. The short term data for 0 cards pen would be dependent on what actually happened on the first hand of the simmed shoe.
As an arbitrary example let's look at what might be plotted for 75 cards pen. Out of 1000 shoes simmed not every shoe would wind up with exactly 75 cards dealt for the next deal but probably at least some of them would. So the long term point for 75 cards would be the average predeal EV of the hands where exactly 75 cards have been dealt and the short term point would be the computed EV of the actual results of playing the simulated hands according to the best decision.
One thing to look out for is to choose a max pen so as not to ever run out of cards to keep from incorporating unreliable data.
I would expect that the long term plot would start with full shoe EV at 0 cards pen and EV tend to increase as pen increases. The short term plot should eventually look like the long term plot but since only a relatively small number of shoes are being simulated it will most likely have a relatively large degree of variation. The long term plot could have some variation too but should be far more stable because it is dependent upon computed values so shouldn't require an overwhelming number of simulated shoes in order to see what is happening.
Last edited by k_c; October 25th, 2011 at 05:11 PM.

October 27th, 2011, 07:10 AM

Member


Join Date: Jul 2011
Posts: 67


Quote:
Originally Posted by k_c
Eric, is it possible for you to organize your data to display 2 plots representing average expected value at each penetration and average actual result of playing each hand with the best decisions? In other words you would be contrasting what has happened short term with what is expected long term.

I suppose I can (I'll take a look tonight)... but I think it would take a lot more than 1000 shoes to get a reasonable comparison. Your 0 pen example illustrates this most clearly; for the expected return it is just a single point, but for the actual return it will be a spread of values. Without having done it yet, I am guessing that with only 1000 samples even the percentile curves will be pretty jumpy.

November 2nd, 2011, 08:10 PM

Executive Member


Join Date: May 2007
Posts: 624


Quote:
Originally Posted by ericfarmer
I suppose I can (I'll take a look tonight)... but I think it would take a lot more than 1000 shoes to get a reasonable comparison. Your 0 pen example illustrates this most clearly; for the expected return it is just a single point, but for the actual return it will be a spread of values. Without having done it yet, I am guessing that with only 1000 samples even the percentile curves will be pretty jumpy.

I ran 2 sims in which player uses optimal strategy for the current composition. The only decision that is possibly not optimal is the decision of splitting but in the great majority of cases, if not all cases, the best decision will have been made. (Split EV is figured as playing the first split hand optimally and applying the same strategy to the second hand. The second hand in the sim will actually be played with knowledge of what was drawn on the first hand so actual split EV should be marginally greater than computed EV in the long run. Rules in sim allow for only 1 split)
First sim is 2 decks with cut card placed after 80th card for 10,000 shoes which turned out to be 150,986 rounds.
Simmed EV was ~ +.03% as compared to a full shoe EV ~ .35%. Simmed difference in EV ~ +.38%
Second sim is 2 decks with cut card placed after 5th card for 100,000 shoes which turned out to be 117,752 rounds.
Simmed EV was ~ .46% as compared to a full shoe EV ~ .35%. Simmed difference in EV ~ .11%
Either one of these simmed EVs may turn out to be more or less for a greater amount of data.
Is there a logical reason why a cut card at position 5 might lose EV while a cut card at position 80 gains EV? The only way 2 rounds can be played with cut card at position 5 is if neither player nor dealer draws even a single card, which means it's likely more high cards than low have been used. This means that whenever a second round is dealt player is forced to play at probably a reduced EV. As more rounds are dealt player has a better opportunity of encountering some +EV rounds. This illustrates that playing blackjack is not quite the same thing as just simply burning a random number of cards in preparation for the next round in which case you might expect more than full shoe EV in the long run at any pen since more info is available.
These sims were done using a program I used to test the .dll version of my CA, which includes a sim function. The test program didn't compute any predeal EVs but could have which would slow it up. The test program presently only outputs the results of playing simmed shoes using either best strategy or full shoe basic strategy. I thought that possibly your (Eric's) data might be able to show to at least a beginning extent of how predeal EV might vary with pen.
My .dll requires that the user supply shuffled shoes. My test program uses the pseudo random number generator from C++. My theory is that this is good enough for blackjack but in case I am wrong the .dll isn't bound to this. A problem with the prng in C++ is that the distribution of cards can be a little biased because the random number it generates is generally not exactly divisible by a given number of remaining cards when the modulo of remaining cards is used to restrict output to a given range. In order to address this problem I just reject a random number if it is out of range and then recursively generate another number until what is output is not out of range......
unsigned long getRandNum(const unsigned long &max) {
unsigned long maxRand = (unsigned long)(RAND_MAX + 1  (RAND_MAX + 1) % max);
unsigned long r = (unsigned long)rand();
if (r < maxRand)
return r % max;
else
return getRandNum(max);
}
void shuffle(short specificShoe[], const long &totCards) {
unsigned long i = (unsigned long)totCards;
while (i > 1) {
unsigned long j = getRandNum(i);
short temp = specificShoe[i  1];
specificShoe[i  1] = specificShoe[j];
specificShoe[j] = temp;
i; }
}
Programmatically I wish there was a better way to address this because it is remotely possble that every number may be out of range in which case the program would never finish. Anyway that's how I presently employ the C++ prng and hopefully it gives reasonable results.
Code:
Running simulation
Sim will use the following rules:
bjOdds: 1.5
Dealer stands on soft 17
Double on any 2 cards
Lose only original bet to dealer blackjack (full peek)
No surrender versus up card of ace
No surrender versus up card of ten
No surrender allowed versus up cards 2 through 9
Maximum splits allowed for pairs 2 through 10: 1
Maximum splits allowed for a pair of aces: 1
No doubling after splitting is allowed
Hitting split aces is not allowed
Doubling after splitting aces is not allowed
Press c or C to continue
Press x or X to exit this screen
Continue?
***** pen = 80/104 *****
Setting compute mode (mode setting for sim applies only to that sim)
Press b or B to use basic CD strategy,
any other key for optimal strategy: Optimal strategy
Decks? 2
Number of shoes to sim? 10000
Penetration? 80
Running sim 100.0% complete
Number of decks: 2
Number of rounds: 150986
Number of hands: 154385
Player blackjacks: 7213, expected = 7216.66
Dealer blackjacks: 7320, expected = 7216.66
Total result: 48.5, EV =~ +.032%
Full shoe EV =~ .35%
Press any key to continue
***** pen = 5/104 *****
Setting compute mode (mode setting for sim applies only to that sim)
Press b or B to use basic CD strategy,
any other key for optimal strategy: Optimal strategy
Decks? 2
Number of shoes to sim? 100000
Penetration? 5
Running sim 100.0% complete
Number of decks: 2
Number of rounds: 117752
Number of hands: 120150
Player blackjacks: 5538, expected = 5628.18
Dealer blackjacks: 5631, expected = 5628.18
Total result: 537.5, EV =~ .46%
Full shoe EV =~ .35%
Press any key to continue

November 3rd, 2011, 11:58 AM

Member


Join Date: Jul 2011
Posts: 67


Quote:
Originally Posted by k_c
First sim is 2 decks with cut card placed after 80th card for 10,000 shoes which turned out to be 150,986 rounds.
Simmed EV was ~ +.03% as compared to a full shoe EV ~ .35%. Simmed difference in EV ~ +.38%
Second sim is 2 decks with cut card placed after 5th card for 100,000 shoes which turned out to be 117,752 rounds.
Simmed EV was ~ .46% as compared to a full shoe EV ~ .35%. Simmed difference in EV ~ .11%
Either one of these simmed EVs may turn out to be more or less for a greater amount of data.

I think the last statement above is the key. That is, I think 10^5 samples is simply not enough to get a good handle on estimated expected value. If the standard deviation for a round is approximately 1 (and probably higher; WizardOfOdds tells me that it's 1.1418 for a 6deck round), then even with 150,000 rounds your simmed EV has a sample standard deviation of at least 0.25%.
In other words, simply running the same setup again (with a different seed and/or RNG) will likely yield very different results.
Re using std::rand(), I tend not to trust an LCG for statistical stuff. But here also, the most definite way to be sure that your RNG, sample size, etc., is not adversely affecting your results is to simply *run it again* with a different RNG, and verify that your results don't change (to within your desired/quoted accuracy).
I have an easytouse Twister implementation (including generating random integers in a specified [0,n) range) that I can pm you if you want.

November 3rd, 2011, 12:47 PM

Executive Member


Join Date: May 2007
Posts: 624


Quote:
Originally Posted by ericfarmer
I think the last statement above is the key. That is, I think 10^5 samples is simply not enough to get a good handle on estimated expected value. If the standard deviation for a round is approximately 1 (and probably higher; WizardOfOdds tells me that it's 1.1418 for a 6deck round), then even with 150,000 rounds your simmed EV has a sample standard deviation of at least 0.25%.
In other words, simply running the same setup again (with a different seed and/or RNG) will likely yield very different results.
Re using std::rand(), I tend not to trust an LCG for statistical stuff. But here also, the most definite way to be sure that your RNG, sample size, etc., is not adversely affecting your results is to simply *run it again* with a different RNG, and verify that your results don't change (to within your desired/quoted accuracy).
Well, what I was thinking was a graph of computed predeal EV versus pen might not require a great deal of data to see a trend. After all we're dealing with computed values and that's as long term as it gets. The only variable is the simmed shoe compositions at he beginning of each round. I think that the starting shoe composition is a pretty powerful force in preventing wild compositions from appearing with a lot of frequency. If that's the case then an overwhelming number of shoes wouldn't seem to be necessary to see the trend.
I agree with you that the validity of results based upon what actually occurs would be questionable without more data. Basically a small amount of this kind of data is showing a shorter term result.
I have an easytouse Twister implementation (including generating random integers in a specified [0,n) range) that I can pm you if you want.

Well, what I was thinking was a graph of computed predeal EV versus pen might not require a great deal of data to see a trend. After all we're dealing with computed values and that's as long term as it gets. The only variable is the simmed shoe compositions at he beginning of each round. I think that the starting shoe composition is a pretty powerful force in preventing wild compositions from appearing with a lot of frequency. If that's the case then an overwhelming number of shoes wouldn't seem to be necessary to see the trend.
I agree with you that the validity of results based upon what actually occurs would be questionable without more data. Basically a small amount of this kind of data is showing a shorter term result.

November 3rd, 2011, 08:02 PM


ChemMeister


Join Date: Oct 2008
Posts: 780


Quote:
Originally Posted by ericfarmer
I think the last statement above is the key. That is, I think 10^5 samples is simply not enough to get a good handle on estimated expected value. If the standard deviation for a round is approximately 1 (and probably higher; WizardOfOdds tells me that it's 1.1418 for a 6deck round), then even with 150,000 rounds your simmed EV has a sample standard deviation of at least 0.25%.
In other words, simply running the same setup again (with a different seed and/or RNG) will likely yield very different results.
Re using std::rand(), I tend not to trust an LCG for statistical stuff. But here also, the most definite way to be sure that your RNG, sample size, etc., is not adversely affecting your results is to simply *run it again* with a different RNG, and verify that your results don't change (to within your desired/quoted accuracy).
I have an easytouse Twister implementation (including generating random integers in a specified [0,n) range) that I can pm you if you want.

Quote:
Originally Posted by k_c
Well, what I was thinking was a graph of computed predeal EV versus pen might not require a great deal of data to see a trend. After all we're dealing with computed values and that's as long term as it gets. The only variable is the simmed shoe compositions at he beginning of each round. I think that the starting shoe composition is a pretty powerful force in preventing wild compositions from appearing with a lot of frequency. If that's the case then an overwhelming number of shoes wouldn't seem to be necessary to see the trend.
I agree with you that the validity of results based upon what actually occurs would be questionable without more data. Basically a small amount of this kind of data is showing a shorter term result.

I ran a perfect play sim(2D 80 card pen, s17 DAS) using the same dll that KC using a Mersenne Twister RNG, I ran the sim for 500k shoes. EV was +0.759%

November 4th, 2011, 04:57 AM

Member


Join Date: Jul 2011
Posts: 67


Quote:
Originally Posted by k_c
Well, what I was thinking was a graph of computed predeal EV versus pen might not require a great deal of data to see a trend. After all we're dealing with computed values and that's as long term as it gets. The only variable is the simmed shoe compositions at he beginning of each round. I think that the starting shoe composition is a pretty powerful force in preventing wild compositions from appearing with a lot of frequency. If that's the case then an overwhelming number of shoes wouldn't seem to be necessary to see the trend.
I agree with you that the validity of results based upon what actually occurs would be questionable without more data. Basically a small amount of this kind of data is showing a shorter term result.

Hmmm, I may have misunderstood your initial question, then. This sounds like you would like to *sim* a bunch of shoe compositions, but *compute* EV (via CA) for each of those shoes, and look at those trends. If so, then I think we are already on the same page; the blue points in the plot in my initial post were exactly that, computed EVs for corresponding depleted shoes based on simulated headsup "optimal" play. (Where "optimal" has the usual caveat for splitting; I just used CDZ.)
Did I miss something?

November 4th, 2011, 09:43 AM


ChemMeister


Join Date: Oct 2008
Posts: 780


Quote:
Originally Posted by ericfarmer
Hmmm, I may have misunderstood your initial question, then. This sounds like you would like to *sim* a bunch of shoe compositions, but *compute* EV (via CA) for each of those shoes, and look at those trends. If so, then I think we are already on the same page; the blue points in the plot in my initial post were exactly that, computed EVs for corresponding depleted shoes based on simulated headsup "optimal" play. (Where "optimal" has the usual caveat for splitting; I just used CDZ.)
Did I miss something?

The main problem is that as the penetration increases the number of possible shoe compositions dramatically increases. So I am not sure if one could significant trends with only 1000 shoes sim.
I have played in the past with something similar but for 1deck game where I enumerate all the possible deck compositions at various penetrations. Calculate the probability of each penetration. Calculate the predeal EV of each penetration. Group each penetration according based on a running count
The following graphs show the different evs for all the possible compositions at 3 different penetrations for a 1D game (S17) using perfect play composition dependent combinatorial analysis. I however use Running counts instead of True Counts. The graphs on the left hand side show the range of evs for a given RC, while the ones on the right show the sum of weighted EVs (p_i*ev_i).
For the tables:
EN[] give the number of each denomination for composition k
AN[] gives the predeal ev, the probability, and the running count of composition k
PenAn[] gives the probability of a running count k, the sum of weighted EVs (p_i*ev_i)
It can be seen clearly that irrespective of the magnitude or sign of the running count, on average we are moving to a positive regime, where at 10 cards remaining in the deck we will be virtually playing with an advantage at all times.
[/QUOTE]
Last edited by iCountNTrack; November 4th, 2011 at 09:54 AM.

November 4th, 2011, 03:15 PM

Executive Member


Join Date: May 2007
Posts: 624


Quote:
Originally Posted by ericfarmer
Hmmm, I may have misunderstood your initial question, then. This sounds like you would like to *sim* a bunch of shoe compositions, but *compute* EV (via CA) for each of those shoes, and look at those trends. If so, then I think we are already on the same page; the blue points in the plot in my initial post were exactly that, computed EVs for corresponding depleted shoes based on simulated headsup "optimal" play. (Where "optimal" has the usual caveat for splitting; I just used CDZ.)
Did I miss something?

I gathered that your data in fact computed predeal EV for all compositions. All I was suggesting was to parse the values by however many cards have been dealt by averaging predeal EV of the number of data points at each pen level. Since the game is dealt heads up, one player versus dealer, the first 2 pen levels would be 0 cards (full shoe) and 4 cards because the initial hand requires at least 4 cards to be dealt in a heads up game. However, the first round may use 5,6,.... cards so in that case the next data point would at 5,6,... cards pen. When you graph the values you would be plotting average computed values on yaxis versus pen on xaxis. Basically it would be a graph of long term values (computed predeal EV) versus pen for simulated compositions. I don't know how many simulated compositions would be necessary to get a stable result since I'm not too much of a statistical person but it seems to me that an overwhelming number of shoes wouldn't be required.
Also while you're at it you could plot the corresponding actual results, if they are available, on the same graph which would probably jump all over the place since it is shorter term.
I'm not that good at explaining things but hopefully this is understandable. Basically what I had in mind is just a different way of displaying your data that could maybe display the approximate value of perfect play at each pen level.
P.S.
I don't think the splitting method would make much difference in results.

Thread Tools 

Display Modes 
Linear Mode

Posting Rules

You may not post new threads
You may not post replies
You may not post attachments
You may not edit your posts
HTML code is Off



All times are GMT 6. The time now is 10:30 PM.
