Jump to content

another interesting article


Recommended Posts

I was researching the web and found this, well it seems that we will have to think a lot

The Card Clumping Myth

by Bryce Carlson

The whole card-clumping concept is largely a fraud, Jerry Patterson and his cult of voodoo Blackjack, notwithstanding.

Last year, I published an in-depth study cum expose on card clumping. It is reprinted, below, and it should answer most of your questions regarding this concept.

I have resisted getting involved in the newsgroup's card-clumping controversy because so much of what is written seems to be flash and flame, and so little appears to be a real search for truth. However, there are a lot of players out there who really do want to know the truth of the matter--and have no idea what or whom to believe. So, in an effort to shed some light on the matter I have conducted a series of computer studies, consisting of several billion hands of simulated Blackjack, that I believe go a long way toward clarifying the issues involved.

These simulations were run using the Omega II Blackjack Casino v1.2 for MS-DOS and Windows. This program has the ability to perform real-world shuffles (washes, riffles, strips, cuts, etc.) as they are done in a casino, as well as perform random card selections using a pseudo-random number generator. (P-RNG).

Before we get into the results of the simulations, however, let's take a look at the psychodynamics that I believe are driving the card-clumping "industry."

First off, *accurate* card counting is difficult; consequently, the vast majority of would-be card counters are, and always will be, losers. It's that simple. Becoming a winning counter takes a lot of hard work, and a lot of good judgment. Not surprisingly, therefore, many players yearn for an easier way. A shortcut that will allow them to target games they can beat using simple methods they can master in a few short hours. This hunger for a free lunch is not unique to Blackjack, but as I say in Blackjack for Blood, "...in the game of Blackjack, as in the game of life, winning is tough. It requires determination, preparation, and plenty of perspiration." But, unfortunately, "...this is not what most people (are) looking for.

" What they want, instead, is "...a simple rule for riches they (can) memorize on the taxi ride to the casino."

So, from the players' perspective, card-clumping systems are very seductive--they promise an easy alternative method for winning play.

Now, let's take a look at it from the perspective of the entrepreneurs who sell books, systems, programs and tapes hawking card-clumping "technology." What's in it for them? Answer: $$$. Big $$$.

Players WANT to believe in a simple "winning" concept such as card-clumping--and where there's a want, sooner or later someone will market a way.

Of course, authors of card-counting works also sell books, systems and programs to wanna-be winners. So, what's the difference? The answer is both simple and straightforward: Bryce Carlson, Stanford Wong, Arnold Synder, Kenny Uston, Peter Griffin, etc., have all based their works on accepted scientific principles, with a minimum of speculation, estimation, or guesswork. Publishers of card-clumping systems, on the other hand, have based their works almost entirely on plausible-sounding theories backed up by anecdotal testimonials. Science versus "religion." Fact versus faith. A modern paradigm for an age-old conundrum.

Now having said all this, you are likely expecting me to state categorically that there is nothing to the card-clumping concept. That it just doesn't happen. Well, surprise, I'm not prepared to do that. Based on the computer studies that follow, it does appear that under certain (unusual) circumstances, some non-random "clumping" effects are produced in some (unrealistically incomplete) multiple-deck shuffles. As we shall see, these effects are generally small, probably not exploitable, rarely (if ever) encountered in the real world--and such biases seem to favor the player as often as they favor the house. But, they are there.

Ready for more? OK, here are the studies, and the results.

Let's begin by discussing the Omega II Blackjack Casino's ability to perform real-world shuffles, as well as simple random card selections based on a P-RNG (pseudo-random number generator). The card-clumping system sellers and their faithful followers discount the fact that computer simulations have not backed up their claims of bias by stating that these effects only occur in games where the decks are actually shuffled as they would be in a casino, not in computer-simulated games where the cards are selected by a P-RNG. They have a point. Almost all Blackjack simulation programs do select cards with a P-RNG. Note, however, that the operative word is "almost." The Omega II Blackjack Casino v1.2 DOES do real-world shuffles as they are done in a casino. The card-shuffling routines in the Omega II Blackjack Casino have been thoroughly analyzed and tested. You can trust these results. And, just in case you don't trust what you can't see, the Blackjack Casino allows you to step through the various shuffle routines--all the while visually displaying the cards in their current sequence and order.

John Imming's highly-regarded UBE also does such real-world shuffles. So, there are programs out there capable of performing realistic casino-style shuffles.

Although the Blackjack Casino is capable of performing a large number of different shuffle-related routines, the studies done here used only the following procedures: A wash of "new" decks (W); a two-block zone riffle of the entire pack ®; a strip of the entire pack (S); a random cut ©, and the introduction of a fresh pack into the game in new-deck order (F).

These procedures are performed by the Omega II Blackjack Casino in the following manner:

(W) Wash: The pack is randomly broken into packets of from 1 to 8 cards. The program then randomly puts these packets back together. The cards within a packet maintain their initial order, only the packets themselves are randomly reordered.

® Two-block zone riffle: The pack is divided into two approximately equal blocks. Then half-deck "picks" from each block are riffled (randomly interleaved) together to form a new stack. This procedure is repeated until all the cards are in this new stack.

(S) Strip: The program randomly strips small packets of from 1 to 4 cards off the top of the pack and places them in another stack. This procedure tends to reverse the order of the pack.

© Cut: The program performs a random cut. All possible cuts areequally likely.

(F) Fresh pack: A fresh pack is brought into the game in new-deck order.

The simulations were all performed assuming a 6-deck game with Las Vegas Strip rules, including double after splits, and resplitting of all pairs including Aces. Penetration varied slightly, but was always to a fixed number of rounds that generally totaled about 245 cards. The game was dealt face-up and blackjacks and busted hands were immediately "placed" in the discard tray (buffer). A fresh pack of 6 decks in new-deck order was brought in periodically, just as it would be in a real casino. From other computer studies, as well as from well-documented direct probability studies, we know that the theoretical expectation for this game, assuming flat bets and Basic Strategy play, is -.34% (of original bets) for the players. In other words, the house enjoys a slight edge in this game of +.34%, assuming Basic Strategy play.

Each computer simulation consisted of 100,000,000 (one hundred million) rounds. The Omega II Blackjack Casino is fast, so such extensive studies were feasible. The percent standard deviation for each player's expectation in each simulation was about +/- .011%. Each shuffle study consisted of seven individual simulations. Each simulation was similar except for the number of players (from 1 to 7 players).

Study #1 did not use real-world casino-style shuffles, but instead performed random card selections using a pseudo-random number generator (P-RNG). The results were virtually the same for all seven simulations (1 player, 2 players, 3 players, etc.). Since the results did not differ regardless of the number of players at the "table," only the results for the 7-player simulation are shown (below):

PLAYER 1 RESULT -.35% DELTA -.01%

PLAYER 2 RESULT -.33% DELTA +.01%

PLAYER 3 RESULT -.34% DELTA +.00%

PLAYER 4 RESULT -.35% DELTA -.01% } MEAN DELTA +.00%

PLAYER 5 RESULT -.35% DELTA -.01%

PLAYER 6 RESULT -.34% DELTA +.00%

PLAYER 7 RESULT -.34% DELTA +.00%

As expected, no biases or other unusual effects were obtained. The results are almost exactly as predicted by theory (-.34%).

Study #2 did use real-world casino-style shuffles. The shuffle wastypical of that performed in many casinos and consisted of the following shuffle sequences: For fresh packs brought into the game in new-deck order, the shuffle sequence was FWRRSRC (fresh pack, wash, zone-riffle, zone-riffle, strip, zone-riffle, cut). For reshuffles of the pack in play the shuffle sequence was RRSRC (zone-riffle, zone-riffle, strip, zone-riffle, cut). As with Study #1, the results were virtually the same for all seven simulations (1 player, 2 players, 3 players, etc.). Since the results did not differ regardless of the number of players at the "table," only the results for the 7-player simulation are shown (below):

PLAYER 1 RESULT -.32% DELTA +.02%

PLAYER 2 RESULT -.35% DELTA -.01%

PLAYER 3 RESULT -.31% DELTA +.03%

PLAYER 4 RESULT -.33% DELTA +.01% } MEAN DELTA +.01%

PLAYER 5 RESULT -.35% DELTA -.01%

PLAYER 6 RESULT -.32% DELTA +.02%

PLAYER 7 RESULT -.34% DELTA +.00%

In this 7-player simulation, a fresh 6-deck pack was introduced every 40 rounds. As can be seen, little if any bias is evident. Given the large number of rounds (100,000,000), the player results do vary slightly more than would be expected on statistical grounds, and this minor increased variance probably is due to non-random effects. But these effects, if they exist, are very, very small, seem to favor neither the players as a group nor the house, and are of no practical significance, whatever.

Study #3. The card-clumping "gurus" generally blame the wash (W) for producing most of the biases they claim exist in multiple-deck games. To test for this, the above study was run, again, except that this time when a new 6-deck pack was introduced into the game, NO wash was performed. The fresh pack shuffle, therefore, consisted of FRRSRC. Reshuffles of the pack in play did not change (RRSRC).

This time non-random effects, though small, were evident. Furthermore, these effects varied based, primarily, on the number of players at the table. Therefore, all seven simulations are presented below:

Simulation #1. One (1) player. Fresh pack every 1120 rounds. Penetration to 43 rounds per "shoe."

PLAYER 1 RESULT -.44% DELTA -.10% } MEAN DELTA-.10%

Simulation #2. Two (2) players. Fresh pack very 960 rounds. Penetration to 29 rounds per "shoe."

PLAYER 1 RESULT -.43% DELTA -.09%

PLAYER 2 RESULT -.38% DELTA -.04% } MEAN DELTA -.07%

Simulation #3. Three (3) players. Fresh pack every 800 rounds. Penetration to 22 rounds per "shoe."

PLAYER 1 RESULT -.35% DELTA -.01%

PLAYER 2 RESULT -.33% DELTA +.01% } MEAN DELTA -.01%

PLAYER 3 RESULT -.37% DELTA -.03%

Simulation #4. Four (4) players. Fresh pack every 640 rounds.

Penetration to 18 rounds per "shoe."

PLAYER 1 RESULT -.31% DELTA +.03%

PLAYER 2 RESULT -.34% DELTA +.00%

PLAYER 3 RESULT -.32% DELTA +.02% } MEAN DELTA +.02%

PLAYER 4 RESULT -.31% DELTA +.03%

Simulation #5. Five (5) players. Fresh pack every 560 rounds.

Penetration to 15 rounds per "shoe."

PLAYER 1 RESULT -.25% DELTA +.09%

PLAYER 2 RESULT -.22% DELTA +.12%

PLAYER 3 RESULT -.27% DELTA +.07% } MEAN DELTA +.10%

PLAYER 4 RESULT -.25% DELTA +.09%

PLAYER 5 RESULT -.23% DELTA +.11%

Simulation #6. Six (6) players. Fresh pack every 480 rounds.

Penetration to 13 rounds per "shoe."

PLAYER 1 RESULT -.19% DELTA +.15%

PLAYER 2 RESULT -.16% DELTA +.18%

PLAYER 3 RESULT -.22% DELTA +.12%

PLAYER 4 RESULT -.17% DELTA +.17% } MEAN DELTA +.16%

PLAYER 5 RESULT -.20% DELTA +.14%

PLAYER 6 RESULT -.16% DELTA +.18%

Simulation #7. Seven (7) players. Fresh pack every 440 rounds.

Penetration to 11 rounds per "shoe."

PLAYER 1 RESULT -.11% DELTA +.23%

PLAYER 2 RESULT -.13% DELTA +.21%

PLAYER 3 RESULT -.16% DELTA +.18%

PLAYER 4 RESULT -.12% DELTA +.22% } MEAN DELTA +.21%

PLAYER 5 RESULT -.13% DELTA +.21%

PLAYER 6 RESULT -.15% DELTA +.19%

PLAYER 7 RESULT -.14% DELTA +.20%

Clearly, there is some (small) bias present in this study. In addition, it appears that the more players at the table, the better off the players are. With one or two players, there appears to be a small bias of about .1% against the players. With three or four players, any biases, if present, appear to cancel out, resulting in no net effect (except, for the peculiar increased variance of results noted in Study #2, above). With five, six, or seven players at the table, there appears to be a small (.1% to .2%) net bias working for the players.

This number-of-players-dependent bias pattern was not expected (not by me, anyway). To see whether or not it was repeatable, and whether small changes could alter it, I ran the entire 7-simulation study, again, this time varying the penetration and frequency of fresh-pack introductions, somewhat. The results, though varying slightly from the previous study, showed the same pattern of increasing bias favoring the players as the number of players at the "table" increased, with the break-even point at three or four players. This effect, though small, seems to be both real and persistent (at least, when all players use Basic Strategy). It is interesting to note that a player's position at the table does not seem to be a factor correlating with expectation.

Encouraged (though not necessary happy) with these results, I next ran a series of studies degrading the shuffle more and more in an attempt to get biased results large enough to be potentially meaningful and exploitable.

From the perspective of the card-clumping community, the results of these subsequent studies were disappointing. As the shuffle got more and more primitive, only a slight increase in the bias effect was noted. As before, with one or two players, it hurt the players, with three or four players, it seemed to virtually disappear, and with five, six, or seven players, the players were somewhat favored.

Finally, in an attempt to get a bias effect large enough to mean anything, I ran a study using a VERY primitive shuffle. For fresh packs the shuffle sequence was FRC (fresh pack, zone-riffle, cut). For reshuffles of packs in play the sequence was simply RC (zone-riffle, cut). Note, the lack of a wash (W) with fresh packs. There is no casino in the world, that I know of, that uses a shuffle this primitive and incomplete. Furthermore, even with this unrealistically incomplete shuffle, if a wash (W) were introduced into the fresh-pack shuffle sequence, any bias virtually disappeared.

Here are the results of this final study.

Simulation #1. One (1) player. Fresh pack every 1120 rounds.

Penetration to 43 rounds per "shoe."

PLAYER 1 RESULT -1.10% DELTA -.76% } MEAN DELTA -.76%

Simulation #2. Two (2) players. Fresh pack every 960 rounds.

Penetration to 29 rounds per "shoe."

PLAYER 1 RESULT -.80% DELTA -.46%

PLAYER 2 RESULT -.68% DELTA -.34% } MEAN DELTA -.40%

Simulation #3. Three (3) players. Fresh pack every 800 rounds.

Penetration to 22 rounds per "shoe."

PLAYER 1 RESULT -.44% DELTA -.10%

PLAYER 2 RESULT -.43% DELTA -.09% } MEAN DELTA -.09%

PLAYER 3 RESULT -.41% DELTA -.07%

Simulation #4. Four (4) players. Fresh pack every 640 rounds.

Penetration to 18 rounds per "shoe."

PLAYER 1 RESULT -.29% DELTA +.05%

PLAYER 2 RESULT -.36% DELTA -.02%

PLAYER 3 RESULT -.31% DELTA +.03% } MEAN DELTA +.02%

PLAYER 4 RESULT -.34% DELTA +.00%

Simulation #5. Five (5) players. Fresh pack every 560 rounds.

Penetration to 15 rounds per "shoe."

PLAYER 1 RESULT -.18% DELTA +.16%

PLAYER 2 RESULT -.15% DELTA +.19%

PLAYER 3 RESULT -.13% DELTA +.21% } MEAN DELTA +.16%

PLAYER 4 RESULT -.24% DELTA +.10%

PLAYER 5 RESULT -.18% DELTA +.16%

Simulation #6. Six (6) players. Fresh pack every 480 rounds.

Penetration to 13 rounds per "shoe."

PLAYER 1 RESULT -.17% DELTA +.17%

PLAYER 2 RESULT -.19% DELTA +.15%

PLAYER 3 RESULT -.23% DELTA +.11%

PLAYER 4 RESULT -.14% DELTA +.20% } MEAN DELTA +.19%

PLAYER 5 RESULT -.10% DELTA +.24%

PLAYER 6 RESULT -.09% DELTA +.25%

Simulation #7. Seven (7) players. Fresh pack every 440 rounds.

Penetration to 11 rounds per "shoe."

PLAYER 1 RESULT +.19% DELTA +.53%

PLAYER 2 RESULT +.11% DELTA +.45%

PLAYER 3 RESULT +.10% DELTA +.44%

PLAYER 4 RESULT +.15% DELTA +.49% } MEAN DELTA +.47%

PLAYER 5 RESULT +.16% DELTA +.50%

PLAYER 6 RESULT +.12% DELTA +.46%

PLAYER 7 RESULT +.09% DELTA +.43%

As with the previous study, I was suspicious of the trend toward a player-favored bias as the number of players at the "table" increased. So, as before, I ran the entire 7-simulation study, again, varying the penetration and frequency of fresh-pack introductions, somewhat. As before, the results, though varying slightly, continued to show the same pattern of increasing bias favoring the players as the number of players at the "table" increased, with the break-even point at three or four players. Also, as be fore, a player's position at the table does not seem to be a factor correlating with expectation.

With this final study, we, at last, seem to have an effect worthy of the term "bias." As to whether or not it is exploitable is another story. As noted, to get significant non-random effects resulting in a noticeable bias it was necessary to limit the shuffle to an unrealistically incomplete zone-riffle, cut (RC) sequence that is not found anywhere in the world that I know of. Also, as noted, even with this primitive shuffle, the introduction of a simple wash (W) in fresh-pack introductions virtually eliminated the bias, completely.

The card-clumping "gurus" have claimed that orthodox researchers have failed to detect biases in the deal because their studies have been distorted by unrealistic conditions (such as the use of P-RNG's in simulations). It appears, however, that it is the card-clumping wonks, themselves, who are trading in unrealistic conditions. You could search the world over and never find a casino so careless as to use the simplistic shuffles necessary to produce a meaningful bias.

We're not quite through yet.

The argument could be made, however, that just because the AVERAGE bias produced by realistic casino shuffles is too small to matter, it does not necessarily follow that meaningful--exploitable--opportunities do not arise for clump trackers, any more than the fact that Basic Strategy expectation is on AVERAGE close to zero means that meaningful--exploitable--opportunities do not arise for card counters.

That's a plausible-sounding argument. But there are serious problems with it. To begin with, to the extent that card-clumping concepts are valid, card-counting concepts are not. They are essentially opposites. In card-counting theory, the best predictor of the next card being "big" is that the last several cards have been "small" (a "plus" count). In card-clumping theory, the best predictor of the next card being "big" is that the last several cards have also been "big" (a "big"-card clump). Consequently, if card-clumping theory were valid, multiple-deck team play wouldn't work. A "Big Player" being called into a "plus" shoe would generally walk into an ambush of dealer three- and four-card 20s and 21s. But that's not what happens. Team play *does* work. Kenny Uston's teams made a fortune with it. I have done very well with it; and teams, led by players you've never heard of, are out, tonight, making money with team play. This fact, alone, argues strongly against the card-clumping concept.

Here's another important point: If the pack were often strongly "polarized" with biased clumps not conforming to a normal distribution, Basic Strategy would be very ineffectual--especially in "small"-card clumps. Consequently, the very fact that any kind of reasonable shuffle produces (as the above studies have shown) at best (worst?) a nominally detectable average bias against Basic Strategy play is strong evidence that no such biased clumping or polarization of the pack occurs.

I know that none of this is going to have the slightest impact on the Jerry Patterson's, or any of the other financially- or emotionally-invested faithful in the card-clumping cult.

They will argue that these studies are far from comprehensive. That I didn't look hard enough, or long enough, or in the right places. And that, in any case, I DID find the elusive biases the orthodox cognoscenti say don't exist. Perhaps; and I do look forward to further research and results. But it's not up to us to prove that real-world biases can't exist--it's up to them to prove that they can, and that they do. And that is something they have never done. And probably never will.

Caveat emptor. Let the buyer beware.

Link to comment
Share on other sites

  • 3 months later...

Well I read through this nonsense and found it to be the very same false math card counting gurus have been perpetrating on an unsuspecting audience for decades.

For instance Bryce Carlson says exactly what the rest of his card counting clan say; Ignore empirical data and instead believe his computer program's SIMULATED evidence. They assume nobody knows what "empirical data" means.

Well I'll tell you what it means. They are saying: Ignore ACTUAL CASINO DATA and instead believe their simulated data. What does "simulated" mean? An attempt at duplicating casino strategy with your computer rather than believe REAL WORLD data.

In other words, don't believe what you see in real casinos with your own eyes. Instead, believe what they THINK is happening but, in fact, isn't. Not even close.

Card counting gurus teach: "It doesn't matter whether you win or lose as long as you make the correct play."

Good God, they don't even understand why they are there! Winning is EVERYTHING!

I would invite Mr. Carlson to actually go to a real casino and record what is actually happening. A place he has probably never been. He would soon throw his simulated data right where it belongs - in the trash. Go to Atlantic City on a Saturday night of a 3 day weekend. Go to ANY 8 deck table. They will ALL be full. That is when and where most people play real cards with real money in the real world. In other words empirical data. Not pretend simulated hog wash but real world.

Count lows following lows and highs following highs. It should be 50/50 according to Mr Carlson. But its no where near 50 50. Count dealer breaks. It should be 28% according to Mr Carlson. Not even close. In Mr Carlson's pretend world, the dealer breaks more than one out of 4 hands. Wouldn't THAT be nice! But in this real world you are lucky if she breaks one out of ten. Often she doesn't break for an entire shoe.

It won't be long before he sees ten tens in a row come out of the dealer shoe and if he hangs around long enough he'll see twenty. THAT never happened on your computer did it Bryce. But there it is right in front of your eyes. EMPIRICAL data, real world - not simulated make believe. We are not trying to beat your computer. Your computer doesn't pay off. We are trying to beat a casino. THAT is where the money is. Try actually going in some time. Try your card counting in that world. I have! You haven't. It sucks! Your best player, Kenny Uston, went broke trying. I beat their pants off playing full time.

What are the odds of just 15 tens in a row Mr Bryce? Each ten has a 4 out of 13 chance of occurring. How often do you see 15 Banks in a row in Baccarat? Each Bank has better than a 50% chance of occurring. But you seldom see it, do you? But 15 tens - any Saturday night, again and again. How did that happen? The cards can't be clumped can they? No of course not. Your computer said so. We'll just pretend we didn't see that. Time after time. And how are you going to beat that game now, Bryce? It's short 15 tens just as sure as if they threw them on the floor before the game started. Don't bother asking me. Ask your computer. Good luck with that.

Clumping isn't about ALWAYS playing clumping as you surmise.

Clumping is about knowing how to play clumped cards when they are clumped and knowing how to play random cards when they are random. You can't play the same way all the time and expect to win. Yet card counters do exactly that.

And Mr Bryce, with all the teaching you guys do, why is it that none of you can come up with a single winning counter year end after expenses? NONE of you.

OK Mr. Bryce, you are at the second of 8 decks and the count is +10. What are you going to do? Real world. Raise your bet? What if the count goes to +35? How many times did you raise your bet? You have no idea where those excess tens are. You don't even know which side of the cut off card they are on. And when you finally get your 20, so will the dealer. THAT is real world.

Look, you stick to your pretend world. That is what you are good at. But the rest of us - We live in the real world. Empirical data is meaningless? Real world is Meaningless?. Attempts at simulation - now THAT is meaningless! How much money did you walk in with? How much money did you walk out with ? THAT is what counts! Real World.

So we make money for teaching what we know from real world experience. So what! You guys wrote a zillion card counting books on simulated data and all the same. What was that? Finger exercise?

Edited by ECD
Link to comment
Share on other sites

  • 10 months later...
  • 3 months later...

All of Carlson's diatribe is based on his FLAWED computer simulations.

By his own admission, his card-wash algorithm is based on his approximation of what HE things would work as a wash, rather than what the dealer's do.

The dealers do NOT break cards into 1 - 8 packets when the wash the cards. All you have to do is WATCH a REAL card wash to know that he is WRONG.

Only Boris for Blackjack CORRECTLY simulates a wash. I wrote the 1st wash code in 1992 and have added nearly a dozen washes (ALL DIFFERENT) since then.

As for his shuffle procedures, we don't have time for me to critique his descriptions, however ONE thing I can tell you is that his software in no way simulates the nearly dozen different shuffle machines on the market. My favorites are the MD-1 & MD-2 shufflers which I can tell you from live play and Boris play consistently produce clump-card opportunities.

The only thing Carlson has in his favor is he has found a lot of confused donks who buy his simulation data because he has produced some thorough statistics.

Unfortunately, no matter how detailed your statistics are, if they are based on FLAWED simulations then no matter how detailed the reporting procedures are, the results will still be WRONG.

I think most of you here have encountered enough card-clumping to know that he lives in the same DREAM WORLD he accuses of of living in.

When was the last time Carlson played 8 Deck Blackjack; especially in Atlantic City? I always get a kick out of the losing whiners who (for the last 28 years) have been blaming Ken Uston for the reason they can't win at A.C. Blackjack. Kenny warned them in 1977, 1981 & again in 1982 about clumped cards. They all said he was wrong. The reason they don't win has NOTHING to do with poor penetration (last I czeched, A.C. casinos are giving 85%+ penetration) and it isn't the "bad rules". The reason haven't won for 25 years is because they are whining donks who don't live/play in the REAL world.

Everyday I play Blackjack I give thanks to the Blackjack Gawdz for the creation of card-clumping.

- BORIS

Link to comment
Share on other sites

Yeah, Boris is right. I read some of Carlson's stuff. He doesn't live in the real world. He lives in a computer simulated make believe world. Anyone who denies clumping is denying real world history in favor of make believe. Calson needs to spend some time in a real casino. What he says is hapening is not even close to what is really happening.

Before I created NBJ, I had a team of 20 professionals record real world BJ data in AC for two years. The results were alarming and completely different from all published statistics. For instance, the real dealer average hand was 19.2 not 18.2. The difference is caused by real world clumping. But think about just how important that one statistic really is. All of Basic Strategy is designed around an 18.2 average dealer hand. Shit in - shit out. You might want to think about that the next time you get a pair of 8s. Even if you get your 2 18's the avg dealer hand beats you and now you've bet twice as much money! B.S. is riddled with such flaws. NBJ corrects them and gets you playing to the real statistics.

Here's some others: The average player merely breaks even on his book doubles and loses on his splits. Ha, and those were supposed to be your advantage over the dealer.

And another: Your book tells you to never insure but I win more than half my insurance bets and they pay 2 to 1. But you have to know when to insure and the cards you are holding have absolutly nothing to do with it. I was once ejected from the Claridge for getting every single insure/ don't insure decision right for 5 hours straight. They said that was impossible W/O cheating - but it isn't.

BJ is a real world game. The FIRST thing you have to do is live in the real world. Make believe doesn't cut it.

Link to comment
Share on other sites

  • 1 month later...
Hello,

That's Amazing and interesting article...! very nice...!

Thanks johan! If you want to play this game successfully you first need to play what is really there, not what your computer says SHOULD be there but never actually IS.

Link to comment
Share on other sites

  • 1 month later...

Join the conversation

You can post now and register later. If you have an account, sign in now to post with your account.
Note: Your post will require moderator approval before it will be visible.

Guest
Reply to this topic...

×   Pasted as rich text.   Paste as plain text instead

  Only 75 emoji are allowed.

×   Your link has been automatically embedded.   Display as a link instead

×   Your previous content has been restored.   Clear editor

×   You cannot paste images directly. Upload or insert images from URL.

×
×
  • Create New...

Important Information

Terms of Use