Arnold Snyder tells how to judge the best blackjack card counting system and analyzes the Red 7, Hi Lo and KO blackjack card counting systems. Many developers of card counting systems manipulate data to artificially make their own card counting system look best.
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Battle of the Babies: A Comparison of the Red 7, Hi-Lo, and KO Card Counting Systems

By Arnold Snyder
(From Blackjack Forum Volume XIX #3, Fall 1999)
© Blackjack Forum 1999

 

[Editor's note: This is a technical article on how to best evaluate card counting systems. It addresses how system sellers sometimes fudge results to make their systems look better.

If you are new to card-counting, I recommend you start by reading Intro to Winning Blackjack and Card Counting: How It Works, Why It Works. For more information on card counting and blackjack basic strategy, and instructions for the Red 7 card counting system, see the end of this article.]

The Search for the "Best" Card Counting System

New players typically search anxiously for the “best card counting system” before learning their first count. This article will provide simulation data on the Red Seven Count for comparison to the Hi-Lo and KO counts. It will also discuss important issues in the comparison of different blackjack card counting systems.

John Auston used the approach that has recently become commonly known as “score” to compare the Red Seven (Red 7), Hi-Lo, and KO count systems. For those unfamiliar with this approach, I would describe it very briefly as an attempt to compare systems and games on an even playing field, assuming we define “even” as an equal and constant risk of ruin, assuming the same starting bankroll and the same betting limits for each system in identical games.

In order to accomplish this in the sim, unrealistic bets are forced. For example, if the optimal bet is $137 with one system, but $138 with another, and $134 with a third system, then these are the bets that are placed. In practice, if these were human players and even if they had very accurately devised their count betting strategies to reflect a similar risk of ruin for identical $10,000 bankrolls, all would likely have bets of either $125 or $150—some slightly underbetting their banks, some slightly overbetting.

I can already see a barrage of letters from players asking me to explain why anyone would want an analysis based on such an impractical, nay impossible, betting methodology. Briefly, the purpose of this type of analysis is not to tell us our win rate to the exact penny per hour, nor is it to suggest that we should attempt to mimic the impractical betting strategies in the real world so that we may obtain optimal results.

The purpose is simply to evaluate the potential profitability of applying a system to a game assuming a given level of risk—one way of dealing with the “best card counting system” comparisons. Let me provide one practical example.

It is not difficult for me to set up a computer simulation where the Hi-Lo Count will outperform the Advanced Omega II (a much stronger and more difficult count), even when both counts are being played accurately and employing the same betting spread. All I have to do is play around with the betting strategies so that Omega II is waiting too long to put its big bets on the table. If I simply raise the true count by one or two numbers where these bigger bets are placed, then Hi-Lo will appear to be a stronger system. But in fact, the Hi-Lo is simply being played more aggressively and with a higher risk of ruin.

I first learned about this aggression factor back in the early 1980s, when I was working with Dr. John Gwynn, Jr. In order to compare different count systems using the same betting spread in the same game, I asked Gwynn to produce data showing the full range of possible betting schemes for each system based on the various true counts. For example, with the Hi-Lo Count, spreading from 1-2-4 units in a single-deck game, he would produce data showing the bet raises to 2 and then 4 units at +1 and +2, then +1 and +3, then +2 and +3, then +2 and +4, etc.

The data Gwynn and I came up with showed nothing about risk of ruin, but it did show that a player who wanted to optimize his percent advantage over the house could do so by raising his bets at precisely the right counts. A player who wanted to optimize his dollar return, on the other hand, could do so by betting more aggressively (placing high bets earlier), even though this tactic would lower his percentage return.

Whenever I published Gwynn’s system comparison data, I always chose the betting scheme that would provide the highest percent advantage to the player. I did this because it was more realistic. A player with an unlimited bankroll, in fact, will show the highest dollar return if he places his high bet as soon as he has even the slightest fraction of a percent advantage over the house. Players with unlimited bankrolls, however, do not exist. Such hypothetical players have no risk of ruin because they can always dig out more money.

In the real world, it is more meaningful to optimize the percent advantage than the maximum potential dollar win. In optimizing the percent advantage from Gwynn’s data for many systems in various games back in the early to mid-80s, and then up into the mid-90s using John Imming’s RWC software, I discovered that the optimal betting spreads were never intuitive. If I was spreading from 1-2-4-8 units in a 6-deck game, one system might perform best by raising bets at +1, +2, +4, and +5; while another’s optimal betting scheme would raise at +2, +4, +5 and +6; while yet another might perform best at +1, +3, +4 and +6.

When I didn’t examine every possible betting scheme for each system, I would often see data that would seem illogical. A system with a lower betting correlation and playing efficiency would appear to outperform a technically superior system. In almost all cases, as soon as I would look at the results of the optimal betting scheme for that system, defining optimal as the scheme that would produce the greatest percent advantage, the greater profit potential of a technically superior system would exhibit itself.

Optimizing system performance to obtain the highest percent advantage for each system does not ensure that all systems being so compared are playing with the same level of risk. This was simply the best way I knew to compare the various systems’ ultimate levels of performance.

Misleading Simulation Data for the KO Count

One of the worst examples of misleading simulation data from ill-chosen betting schemes can be found in Knock-Out Blackjack by Olaf Vancura and Ken Fuchs. They designed a unique method of attempting to simulate equivalent levels of risk in their system comparisons that produced data that computer programmers used to refer to as GIGO—garbage in, garbage out. The system comparison charts in Chapter Five of the 1996 edition and again in the Appendix of the 1998 edition would lead one to believe that the KO Count was superior to or equal to just about every other counting system on the planet, and especially powerful in one-deck games.

For example, the chart below reproduces the simulation data provided by Knock-Out Blackjack comparing the win rates for KO vs. Red 7, Hi-Lo, and Omega II, assuming 1-5 spreads in the one and two-deck games, 1-8 in the 6-deck games, and 1-10 in the 8-deck games, with all systems using the 16 most important strategy indices.

The Simulation Data Provided by Knock-Out Blackjack

  1-deck 2-deck 6-deck 8-deck
KO 1.53 1.11 0.62 0.52
Red7 1.46 1.08 0.61 0.50
Hi-Lo 1.47 1.08 0.61 0.52
Omega 1.52 1.15 0.68 0.57

When I first looked at this data back in 1996, my initial thought was, “Impossible! KO beating Omega II in a single-deck game? And beating Hi-Lo in all games?” I did not know whether or not it might beat Red Seven, but it was illogical to me that it would perform so powerfully, so consistently, in comparison with the balanced counts. I knew that Red Seven performed close to Hi-Lo in shoe games, and did occasionally outperform it, but never in single deck.

I ran some single-deck simulations myself using John Imming’s software, setting up the game and system conditions as described by Vancura and Fuchs, and quickly confirmed what I already knew—that KO was similar to Red Seven in performance, but notably less profitable than Hi-Lo and Omega II. In fact, it also slightly under-performed Red Seven throughout all the tests I ran.

I called Anthony Curtis, who was distributing the KO book through Huntington Press (now publisher of the second edition) and told Curtis that I thought the authors may have jerry-rigged the sims to make KO appear stronger than it actually was. I told him that in the one-deck sims I was running, Red Seven outperformed KO, not by much, but slightly.

Curtis assured me that he felt the authors were honest and that their simulation data was real, with no intention to skew the system comparison data. At this point, I had never met Olaf Vancura or Ken Fuchs, so I did not know if these guys were legitimate experts or big phonies. I have met and corresponded with both of them since, and I now know that, in fact, they are both gentlemen and scholars with no intent to deceive.

Here’s where Vancura and Fuchs went wrong. KO’s imbalance makes it strongest and most accurate when the running count is at its pivot. So, the authors, very logically, set up their sims so that KO was placing its high bets precisely at this point. The other systems, however, were then forced in their simulations to play with the same “average bet” that KO used.

The KO counting system is actually very easy to use and very strong. It is similar in strength to the Red Seven, which itself is close to the Hi-Lo in strength within certain confines. In fact, Hi-Lo is notably superior to both Red Seven and KO in one and two-deck games, and from many professional players’ perspectives, where accurate bet-sizing according to Kelly principles is important, Hi-Lo is also far superior in shoe games.

For most casual players, however, I still believe the unbalanced counting systems are the best choice because they're simpler, can be played longer without costly errors, and allow the player to focus more on heat, getting away with a big bet spread, and other factors that matter more to your win rate than the count system you use.

I have been hesitant to publicly criticize Vancura’s and Fuch’s less than brilliant system comparisons in Knock Out Blackjack because the book really is one of the better ones on the market. The system is good. The explanations of blackjack and card counting are clear. There’s no get-rich-quick b.s., and I believe there was no intent to deceive.

I certainly don’t want to push new players away from Knock Out Blackjack and into the arms of one of the con-man books out there. Also, many serious players are already aware of why the KO system looks so strong in the sims Vancura and Fuchs provide in their book. There has been a lot of sim data posted on the various Internet blackjack sites that refute the findings in the KO book.

Also, anyone who looks at John Auston’s “World’s Greatest Blackjack Simulation” reports can see that KO’s strength is about what one would expect of a level one unbalanced counting system.

John Auston's Comparison of the Red 7, KO, Hi Lo and Omega II Card Counting Systems

In the chart below, I am reproducing John Auston’s “World’s Greatest Blackjack Simulation” data for comparing KO with Red Seven, Hi-Lo, and Omega II in the same games that Vancura and Fuchs used in their book. The single-deck penetration was 65% and all other games were 75%. Note that in the single and double deck games, Auston did not provide sim data for a 1-5 spread, so I used his data for 1-4 in single deck, and 1-6 in double.

Auston’s Data on KO, Red Seven, Hi-Lo, and Omega II

  1-deck 2-deck 6-deck 8-deck
KO 1.32 1.29 0.34 0.20
Red7 1.34 1.24 0.44 0.39
Hi-Lo 1.38 1.30 0.34 0.26
Omega 1.64 1.54 0.51 0.56

Since the World’s Greatest Blackjack Simulation reports were published in 1997, many players have asked me why the KO data does not show any indication of the consistent superiority to other systems that KO is purported to exhibit in Knock Out Blackjack. The answer is simply that John Auston did not set up his WGBJS sims so that all systems had to conform to the “average bet” specs of the optimal KO betting strategy.

A few interesting points on Auston’s WGBJS data. Note that Omega II is in a class by itself, solidly trouncing the level one systems’ results in all games, as expected. Hi-Lo, KO and Red Seven go back and forth in their exhibitions of strength relative to each other.

Hi-Lo is slightly superior in single deck, while Red Seven and KO are about the same. Red Seven is slightly weaker in the double deck, where Hi-Lo and KO are about the same. Red Seven is stronger in both the six deck and eight deck, where Hi-Lo and KO are slightly weaker. If you actually look at all of the data in the WGBJS reports, you find that all three of these counts continually go back and forth, depending on the number of decks, penetration, and betting spreads. But none of them are a match for Advanced Omega II.

I would also point out that even these independently run sims—done with no attempt to bias the data towards any system—are inadvertently set up with conditions that are more favorable to the unbalanced counts, at least in comparison with the Hi-Lo. This is because John Auston used the "Illustrious 18" shoe strategy indices for all systems other than Advanced Omega II. These indices—which are the ideal indices for shoe games—are not the best 18 indices for one and two deck games. Since all but two of these indices call for Hi-Lo strategy changes at neutral to slightly positive counts (0 to +5), this is precisely the range of counts where both KO and Red Seven will perform best.

A Hi-Lo player who is using more indices for the common playing variations that occur both at negative counts and at higher positive counts would actually expect a performance level closer to the Advanced Omega II system (which Auston simmed with a full set of indices) in the one and two deck games. Red Seven and KO simply do not have a playing accuracy level comparable to Hi-Lo outside the limited Illustrious 18 range.

Also, Red Seven’s dominating performance in the six deck and eight deck games, where it solidly trounces both Hi-Lo and KO, is due to the way the system is designed, where it performs with maximum strength when the advantage has risen by about 1%. Because it is strongest at this point, this is where it will be placing most high bets.

In these shoe games with only 75% penetration, higher advantages only rarely occur. So, Red Seven is optimized to play in precisely these types of games.

I can assure you, however, that it is playing with more risk than Hi-Lo, so in reality it would require a larger bankroll to play Red Seven to its optimum performance in these games. Auston’s six deck WGBJS data shows Red Seven’s average bet to be 1.63 units, while KO and Hi-Lo are making average bets of only 1.42 and 1.47 units respectively.

If we were to force average bets of 1.63 units on Hi-Lo and KO, they would perform even worse in comparison with Red Seven. KO’s playing and betting accuracy do not optimize until a 2% raise in the player advantage has occurred, and Hi-Lo suffers from not counting the sevens, which raises the Red Seven’s betting correlation (BC) and playing efficiency (PE) enough at its pivot point to justify the more frequent higher bets.

John Auston’s risk-adjusted analysis, which we have yet to look at in this article, solves the problem of equalizing the risk factors for the various systems tested, but it is still unfairly skewed toward the unbalanced systems in hand-held games, because it again uses only the Illustrious 18 range of indices, with which the unbalanced systems perform best.

One other problem with the Red Seven data is that in all of these sims—WGBJS and risk-adjusted—the advanced version of the Red Seven system cannot be simulated as published in the 1998 edition of Blackbelt in Blackjack [editor’s note: the Advanced Red Seven can now be simulated using CVDATA, which was not available at the time this article was written].

In shoe games, for example, I provide index numbers for the Advanced Red Seven that are to be used only in the second half of the shoe. The player is advised to use only the six “simple Red 7” indices in the first half of the shoe, then switch to the advanced indices for the second half. The ability to “step up” to the much more powerful Advanced Red Seven when ready is something not available to players with the KO Count.

Also, although both the simple and advanced Red Seven indices are employed by running count, the Advanced Red Seven advises the use of the “true edge” method of estimating advantage for bet sizing, which is actually a simplified method of adjusting to the true count. In other words, the Advanced Red Seven player would be making strategy plays by running count, but some of these plays would not be made until after the 50% level of penetration was reached, while bet sizing is done by true count.

The performance of Red Seven in these simulations will be hurt by not employing these techniques. The sims will simply not show accurate data for the Advanced Red Seven’s true power.

Also, the SBA software used for these simulations was incapable of counting sevens by color. John Auston adjusted for this deficiency by essentially counting all of the 7s as +1/2, instead of just the red sevens as +1. In both the 1983 and 1998 editions of Blackbelt in Blackjack, I wrote: “One may even count all sevens as +1/2, or simply count every other seven as +1” in order to maintain the same imbalance as is provided by counting just the red sevens—and over the years not a few players have told me that this is what they do at the tables." This method of unbalancing the count has a slightly better betting and playing efficiency than the traditional Red Seven, however. In prior sim comparisons published in Blackjack Forum, I always used Imming’s RWC software (no longer commercially available), which does allow counting by suit.

Also, Auston chose to simulate the Red Seven for his risk-adjusted analysis with the indices he derived from SBA, and which I published in the Red Seven Count edition of his “World’s Greatest Blackjack Simulation” report. So, both the betting and playing strategies used in these risk-adjusted analyses are different from those you will find in the 1998 (and 2005) edition of Blackbelt in Blackjack, which I believe to be superior. Just bear in mind that these comparisons are specifically for the 1997 version of the simple Red Seven as published in Auston’s WGBJ Sim.

More Problems With Comparing Card Counting Systems

Finally, the risk-adjusted method of analysis will give an unbalanced system an ability to bet far more accurately in all games than would be possible in the real world. A Hi-Lo or Omega II player who is using a true count system will truly know when his advantage is approximately +1/2%, +1%, +1.5%, +2%, etc., and he will be able to size his bets accordingly. This would also be true of an Advanced Red Seven player who is using the true edge method of bet sizing.

For a KO player or a simple Red Seven player who is purely going by the running count, an accurate bet can only be made at the pivot. If a Red Seven or KO running count is +6 above the pivot, the actual player advantage will be quite different in a six deck game if only two decks have been dealt than if 4.5 decks have been dealt.

This is why most professional blackjack players steer clear of the unbalanced counts—and why I added the true edge methodology to the 1998 Advanced Red Seven. If you are betting multiple black chips for every ½% rise in your advantage, or calling in a big player who will be doing this, you do not want to be constantly overbetting and underbetting your optimal Kelly bet—or most likely, a fractional Kelly bet you’ve chosen to minimize your risk.

So, as you look at this risk-adjusted comparison data, bear in mind all of these factors. The validity of the data extends as far as the assumptions used in the sims for playing and betting. Ultimately, Red Seven and KO perform very well compared to Hi-Lo, and I still believe these simplified unbalanced systems should be used by most players for practical reasons, in particular the cost of errors associated with inaccurate true count adjustments.

Again, I want to emphasize that Hi-Lo is being severely penalized in the hand-held games in the charts below by using the Illustrious 18. If you use the Hi-Lo in one or two deck games and you are using the Illustrious 18 indices, then you could probably do almost as well with Red Seven or KO—better, if your true count adjustments aren’t perfect. Most single deck players I know use many more indices than this in single deck, especially some negative indices that are more important than some of the Illustrious 18 in these games, and unbalanced counts are incapable of using more indices with accuracy.

Risk-Adjusted, One Deck, H17, DAS, 75% Dealt

Spread Hi-Lo KO Red Seven
1-2 72.83 66.50 72.43
1-3 133.13 125.00 131.95
1-4 176.55 168.39 174.72

Risk-Adjusted, Two Deck, S17, DAS, 75% Dealt

Spread Hi-Lo KO Red Seven
1-4 58.79 55.80 58.50
1-6 84.64 81.78 83.78
1-8 101.14 98.39 99.75

Risk-Adjusted, Six Deck, S17, DAS, LS, 75% Dealt

Spread Hi-Lo KO Red Seven
1-8 24.71 22.74 26.39
1-10 29.43 27.36 30.99
1-12 32.91 30.85 34.53

Risk-Adjusted, Six Deck, S17, DAS, LS, 87.5% Dealt

Spread Hi-Lo KO Red Seven
1-8 40.45 38.40 40.72
1-10 47.26 45.36 47.00
1-12 52.04 50.50 51.89

Risk-Adjusted, Six Deck, S17, DAS, LS, 92% Dealt

Spread Hi-Lo KO Red Seven
1-8 73.74 71.54 68.24
1-10 85.26 82.83 77.57
1-12 93.62 91.07 84.46

In the six deck comparisons above, we have a perfect illustration of the power of the pivot. Note that with 4.5 decks dealt (75%), Red Seven is the strongest performer. With five decks dealt, Hi-Lo and Red Seven pretty much equalize, both slightly outperforming KO. But look what happens when we go to 5.5 decks dealt out (91.67% penetration). Red Seven is now the weakest performer, as KO is capitalizing on its strength when many more opportunities arise for playing and betting with a 2% advantage.

The Red Seven (that is, the simple running count version tested here) simply performs better in shoes at most common levels of penetration, whereas KO performs better in the rare games with an extremely deep level.

Also, in some games under specific conditions, KO does outperform Hi-Lo in a risk-adjusted sim. For example, look at this six deck game with a less favorable set of rules:

Risk-Adjusted, Six Deck, H17, DAS, 75% Dealt

Spread Hi-Lo KO Red Seven
1-8 6.60 6.66 6.30
1-10 9.20 9.95 9.83
1-12 11.90 12.54 12.10

Risk-Adjusted, Six Deck, H17, DAS, 87.5% Dealt

Spread Hi-Lo KO Red Seven
1-8 15.03 15.10 15.13
1-10 19.69 19.82 19.50
1-12 23.43 23.74 22.94

KO outperforms both Hi-Lo and Red Seven in these H17 games. Again, the explanation for these types of seemingly aberrant results is that at some levels of penetration, and with certain rule sets, the sevens (which Hi-Lo ignores) are important enough on some of the Illustrious 18 strategy decisions as to give these unbalanced counts, which count sevens, a slight edge. Also, the betting schemes are such that the optimal high bets happen to occur very near the unbalanced counts’ pivots, and counting the sevens also gives them a slightly higher betting correlation right at this crucial point.

Unless we are adjusting an unbalanced count to a true count, we cannot cite its betting correlation (BC) or playing efficiency (PE). For example, the Red Seven has a betting correlation of about 97% at the pivot, just slightly greater than the Hi-Lo. But the Hi-Lo has better than 96% betting correlation throughout the full range of counts that occur. It is simply incorrect to attempt to compare a balanced system with an unbalanced system based on BC and PE if you are using the unbalanced system as a running count system.

Now let’s look at some 8-deck back-counting risk-adjusted comparisons (below). We’ll look at the risk-adjusted results with six decks (75%), 6.5 decks (82%), and 7 decks (88%). Here again, we see that KO outperforms Red Seven at the very deepest level of penetration.

Risk-Adjusted, 8-Deck, Back Count S17, DAS, LS, 75% Dealt

Spread Hi-Lo KO Red Seven
1-8 30.14 28.79 31.34
1-10 37.82 35.94 38.40
1-12 40.49 37.14 39.94

Risk-Adjusted, 8-Deck, Back Count S17, DAS, LS, 82% Dealt

Spread Hi-Lo KO Red Seven
1-8 40.82 38.78 40.98
1-10 52.09 49.59 51.52
1-12 55.56 51.59 52.64

Risk-Adjusted, 8-Deck, Back Count S17, DAS, LS, 88% Dealt

Spread Hi-Lo KO Red Seven
1-8 57.78 60.00 56.36
1-10 74.20 73.19 70.94
1-12 78.51 74.75 72.29

Note that in most comparisons, regardless of the number of decks, Hi-Lo slightly outperforms both KO and Red Seven. I think in the real world, a good Hi-Lo player would outperform the unbalanced running count players more than these sim results indicate. In the hand-held games the Hi-Lo player would simply be able to use more strategy changes, and in the shoe games the Hi-Lo player would be betting more accurately according to his advantage throughout the full range of counts that occur.

Overall, as we would expect, the risk-adjusted comparisons do show Hi-Lo (accurately used) to be the stronger counting system.

For those of you who do not have John Auston’s “World’s Greatest Blackjack Simulation—Red Seven Edition,” I am reproducing below all of the running count indices that John used in that report as well as in his risk-adjusted analyses. Note that these indices assume that you begin your count at 0. The indices were specifically derived for the Red Seven Count that counts all sevens as +1/2 instead of counting just the red sevens as +1. This would not change any of the indices.

Some players who use the Red Seven in this way have told me that the easiest way to count by halves is to simply count every other seven as +1. I believe I actually first heard of this technique being used by players who did it with Wong’s Halves Count. I may even have read about the technique in one of Wong’s newsletters many years ago, or possibly in one of the earlier versions of Wong’s Professional Blackjack.

I was unable to find the reference in print but the technique has been used by some Red Seven and Halves players for many years. I do not believe my suggestion that Red Seven players might count in this way in the 1983 Blackbelt in Blackjack was the first reference to this technique in print. Those who use it swear it is the easiest and most accurate way to count with these systems.

I once ran some simulations using Imming’s RWC software to compare the difference between counting the red sevens as +1 and all sevens as +1/2, but the results were statistically insignificant. I suspect that I did not run a sufficient number of hands. (Computers were notably slower back then.) The simulations Auston used in his WGBG repots, his risk-adjusted analyses, and his truly amazing Blackjack Risk Manager software, are all based on sims of 400 million hands each, quite enough to obtain statistically significant data for practical comparisons.

Below, you will find all of the Red Seven risk-adjusted data that John Auston produced for this study. Some Red Seven players may regret that he did not run risk adjusted sims on the full range of rule sets that he did for Hi-Lo and KO.

John chose five different rule sets for his Red Seven single-deck analyses, and four different rule sets for each of the two deck, six deck and eight deck analyses. I don’t think a more exhaustive risk-adjusted analysis of this version of the Red Seven is called for at the present, as I believe that most Red Seven players probably use one of the versions (simple or advanced) from Blackbelt in Blackjack, rather than the version I published in the WGBJ sim report.

The Red 7 Simulation Indices
  8-Deck 6-Deck 2-Deck 1-Deck
Index/IRC 0 0 0 0
Insurance +20 +15 +5 +2
16 vs 9 +25 +21 +7 +4
16 vs 10 +9 +6 +2 +1
15 vs 10 +22 +17 +6 +3
13 vs 2 +1 +1 +1 +1
13 vs 3 -5 -4 -1 0
12 vs 2 +20 +16 +6 +4
12 vs 3 +15 +11 +5 +3
12 vs 4 +9 +6 +2 +2
12 vs 5 -2 +1 0 +1
12 vs 6 0/-10* 0/-7 +1/-2 +1/-1
11 vs Ace +13/+7 +10/+4 +2/-1 0/-1
10 vs 10 +20 +16 +5 +3
10 vs Ace +20/+18 +16/+13 +5/+4 +2
9 vs 2 +11 +9 +3 +2
9 vs 7 +21 +16 +6 +3
10,10 vs 5 +24 +19 +7 +4
10,10 vs 6 +23 +18 +7 +4
 
Surrender
 
15 vs 9 +17 +14 +5 +3
15 vs 10 +8 +5 +2 +1
15 vs Ace +15/+1 +11/0 +3/0 +1/0
14 vs 10 +18 +14 +5 +3
 
1-Deck Special
 
8 vs 5 N/A N/A N/A +3
8 vs 6 N/A N/A N/A +3
* Where two indices are shown, the first is for S17, the second for H17

Red 7 1-Deck ($Won/100)
(exact indices, sim counted all 7s as .5, to simulate counting only red)
  S17 S17DAS H17 H17DAS H17D10
26 (1.15)
1-2 17.28 27.66 6.30 13.75 0.54
1-3 38.93 52.17 22.70 34.32 4.36
1-4 55.08 69.35 36.59 49.86 13.53
31 (1.20)
1-2 41.89 56.37 23.87 36.61 5.38
1-3 77.33 94.65 54.47 71.01 23.56
1-4 103.50 122.07 78.70 96.26 42.62
35 (1.20)
1-2 50.47 65.10 29.90 44.21 7.41
1-3 93.76 112.49 68.72 88.01 31.37
1-4 126.53 145.73 99.16 121.10 55.52
39 (1.25)
1-2 81.98 99.37 55.24 72.43 22.40
1-3 143.75 164.25 111.00 131.95 63.09
1-4 187.64 207.98 152.38 174.72 97.81

Red 7 2-Deck ($Won/100)
(exact indices, sim counted all 7s as .5, to simulate counting only red)
  S17DAS S17DASLS H17DAS H17DASLS
52 (0.95)
1-4 18.89 28.68 6.59 15.86
1-6 29.69 42.69 17.33 28.63
1-8 36.48 51.84 23.51 37.43
62 (0.95)
1-4 30.74 46.06 18.15 29.61
1-6 45.45 66.48 32.13 48.54
1-8 55.07 79.40 41.55 60.99
70 (1.00)
1-4 43.74 63.66 26.80 43.71
1-6 63.38 90.70 44.75 68.54
1-8 76.12 107.55 57.09 85.50
78 (1.00)
1-4 58.50 84.43 39.99 61.79
1-6 83.78 119.19 64.06 95.05
1-8 99.75 140.67 80.13 116.71

Red 7 6-Deck ($Won/100)
(exact indices, sim counted all 7s as .5, to simulate counting only red)
  S17DAS S17DASLS H17DAS H17DASLS
4/6 (0.90)
1-8 8.70 17.26 2.41 8.32
1-10 10.83 20.67 4.54 11.32
1-12 12.46 23.14 6.62 13.85
4.5/6 (0.95)
1-8 14.43 26.39 6.30 15.62
1-10 17.80 30.99 9.83 19.96
1-12 20.26 34.53 12.10 23.20
5/6 (0.95)
1-8 25.19 40.72 15.13 27.42
1-10 29.81 47.00 19.50 33.61
1-12 33.49 51.89 22.94 38.35
5.5/6 (0.95)
1-8 44.03 68.24 29.64 51.26
1-10 50.94 77.57 36.33 60.70
1-12 56.24 84.46 41.34 67.69

Red 7 6-Deck Back Count ($Won/100)
Integer to rightof $ amount is Running Count of 1st bet (IRC=0)
(exact indices, sim counted all 7s as .5, to simulate counting only red)
  S17DAS S17DASLS H17DAS H17DASLS
4/6 (0.90)
1-1 21.32 (17) 32.50 (14) 18.29 (17) 26.15 (17)
1-2 25.20 (14) 38.00 (14) 20.86 (14) 30.63 (14)
1-4 26.59 (14) 40.47 (11) 22.43 (14) 32.58 (14)
1-8 27.57 (11) 41.60 (11) 23.17 (14) 33.79 (14)
1-12 27.77 (11) 41.74 (11) 23.31 (14) 34.42 (11)
4.5/6 (0.95)
1-1 31.34 (17) 47.17 (17) 26.03 (17) 39.65 (17)
1-2 35.97 (14) 53.93 (14) 30.34 (17) 45.07 (17)
1-4 38.07 (14) 57.38 (14) 32.43 (14) 48.72 (14)
1-8 38.91 (11) 58.85 (11) 33.73 (14) 49.75 (14)
1-12 39.69 (11) 59.80 (11) 33.52 (14) 50.30 (14)
5/6 (0.95)
1-1 46.55 (17) 68.09 (17) 40.32 (17) 60.00 (17)
1-2 54.08 (14) 76.25 (14) 46.05 (17) 67.21 (17)
1-4 58.12 (14) 82.10 (14) 49.61 (14) 71.57 (14)
1-8 58.79 (14) 83.85 (14) 51.14 (14) 73.84 (14)
1-12 59.50 (14) 85.39 (11) 51.08 (14) 73.69 (14)
5.5/6 (0.95)
1-1 73.47 (17) 105.13 (17) 65.39 (17) 97.56 (17)
1-2 84.15 (14) 117.91 (14) 71.43 (17) 106.00 (17)
1-4 90.14 (14) 126.42 (14) 77.30 (14) 113.40 (14)
1-8 90.66 (14) 126.28 (14) 78.47 (14) 115.95 (14)
1-12 90.88 (14) 127.47 (14) 78.44 (14) 115.83 (14)

Red 7 8-Deck ($Won/100)
(exact indices, sim counted all 7s as .5, to simulate counting only red)
  S17DAS S17DASLS H17DAS H17DASLS
5.5/8 (0.90)
1-8 4.03 9.80 0.22 3.19
1-10 6.17 12.06 1.63 5.47
1-12 7.56 14.28 2.93 7.47
6/8 (0.95)
1-8 7.30 14.78 1.79 7.24
1-10 9.41 17.92 3.93 9.92
1-12 11.68 20.46 5.72 12.61
6.5/8 (0.95)
1-8 11.71 21.27 4.54 12.51
1-10 15.23 25.52 7.67 16.92
1-12 17.70 28.72 9.88 20.04
7/8 (0.95)
1-8 18.85 32.32 10.09 21.61
1-10 23.18 38.19 14.60 27.11
1-12 26.46 42.44 17.82 31.28

Red 7 8-Deck Back Count ($Won/100)
Integer to rightof $ amount is Running Count of 1st bet (IRC=0)
(exact indices, sim counted all 7s as .5, to simulate counting only red)
  S17DAS S17DASLS H17DAS H17DASLS
5.5/8 (0.90)
1-1 14.89 (17) 23.29 (17) 12.50 (17) 18.52 (17)
1-2 17.52 (14) 26.85 (14) 14.29 (17) 20.95 (17)
1-4 18.75 (14) 28.49 (11) 14.95 (14) 22.75 (14)
1-8 19.19 (11) 29.57 (11) 15.63 (14) 24.02 (14)
1-12 19.53 (11) 29.97 (11) 15.56 (14) 23.82 (14)
6/8 (0.95)
1-1 21.43 (17) 31.34 (17) 16.84 (17) 25.68 (17)
1-2 23.85 (17) 34.78 (14) 18.85 (17) 29.47 (17)
1-4 25.63 (14) 38.40 (14) 20.29 (14) 32.28 (14)
1-8 26.54 (14) 39.25 (11) 20.72 (14) 33.03 (14)
1-12 26.60 (11) 39.94 (11) 21.39 (14) 33.10 (14)
6.5/8 (0.95)
1-1 28.95 (17) 40.98 (17) 23.53 (17) 36.36 (17)
1-2 32.99 (17) 47.44 (17) 27.10 (17) 41.67 (17)
1-4 35.37 (14) 51.52 (14) 28.96 (14) 44.36 (14)
1-8 36.80 (14) 52.41 (14) 29.93 (14) 46.23 (14)
1-12 36.45 (14) 52.64 (11) 29.97 (14) 46.46 (14)
7/8 (0.95)
1-1 40.30 (17) 56.36 (17) 34.55 (20) 50.00 (17)
1-2 46.51 (17) 65.72 (17) 40.22 (17) 59.46 (17)
1-4 48.30 (14) 70.94 (14) 42.14 (17) 61.91 (14)
1-8 49.76 (14) 72.29 (14) 43.55 (14) 63.85 (14)
1-12 49.83 (14) 72.29 (14) 43.71 (14) 64.20 (14)

For complete instructions on the Red 7 count, see The East Red 7 Count

In summary, the "best" blackjack card counting system for you, whether the Red Seven, Advanced Red Seven, KO, Hi-Lo, Zen, or some other count, will depend partly on your current abilities as a card counter and partly on the games you actually play in. I hope this article will help you better understand some of the issues involved in blackjack system simulations and comparisons. ♠


For complete information on the Red Seven, Advanced Red Seven, Hi-Lo Lite and Zen card counting systems, see Arnold Snyder's Blackbelt in Blackjack. Blackbelt also contains introductions to shuffle tracking, hole-card play, team play, and other advanced professional gambling techniques.

For complete information on the KO card counting system, see Knock-Out Blackjack by Olaf Vancura and Ken Fuchs.

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