How To Bet In Blackjack While Counting Cards – Under normal circumstances, the house favors blackjack. Of course, this only applies if you consider card counting, which can help a player win long-term wins in the casino. Counting cards is relatively simple. The idea is that certain cards, if left in the deck, are more valuable to the player, while other cards are more valuable to the dealer. If we can assign an exact value to each card and keep track of the total number of cards we have already seen, we can effectively predict whether the rest of the deck will be in our favor and bet accordingly. Therefore, this post focuses on a simple way to create values that are efficient for card counting.
Before dealing with the simulation task of defining a counting system, it is useful to review some standard counting systems. Each of these systems has three basic features that primarily relate to ease of use:
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Perhaps the most popular balanced card counting system ever used is the ‘Hi-Lo’. The value of each card is as follows:
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In this setup, all high cards from 10 to A are valuable to the player; When these cards appear, we take a step in the negative direction. On the other hand, all low cards (2 to 6) are valuable to the dealer, so we take a step in the positive direction when they leave the game. If the total is high, the player’s chance of winning is higher. In these situations, the player should bet more on subsequent hands and stick to the minimum bet if the number is negative, as the odds are against them. It is important to note that most people do not realize that a smart betting system is the most important tactic in card counting; Most people think that players gain an advantage simply by changing their game strategy based on the count. Fortunately, this is an advantage on the counter-attack, as memorizing the correct game strategy under normal circumstances is quite daunting in itself. Remembering when to deviate from this game makes it even more complicated.
Because we focus solely on betting efficiency, we stick to the basic strategy of Blackjack. However, other players will likely focus on other considerations when choosing their preferred counting system. Therefore, a wide range has developed over time; Here are some examples of other card counting systems.
Looking at the previous system table, the question may arise as to how a solid counting system can be developed. Is this a trial and error process, or is there a computational approach that can determine meaningful card values? Note that one of the features mentioned in the last section, namely the centering of integers, is largely present in the aforementioned systems. What if we relaxed this restriction and allowed all real numbers? What if the value range is unlimited? Of course, removing these restrictions would probably make the system unusable for humans, but it would allow us to determine the ideal weight of each card, or in other words the
In the past, I have dealt with this problem by initializing the simulations with one of the counting systems already known above and introducing slight perturbations in their values. After simulating enough hands, I was able to compare the new counting system to the old one, and if the new system had better properties, it would prevail as the “best” counting system. This random search continues until the prizes are reduced and the current number is announced as the winner. However, since this approach primarily involved brute force, it was very ineffective; For each counting scenario, tens of millions of hands had to be counted to ensure statistical significance compared to the other counts. I also had to accept that simulating thousands of possible counting systems led to multiple testing problems. Therefore, it was possible to introduce a counting system that was no better than the previous one.
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When I revisited this problem, I discovered a completely different approach that could produce valid results. Instead of forcing a specific system to simulate and calculate ROI, how about simply simulating blackjack hands while logging card history? After each hand, I could also note the amount won or lost; the output would look something like this:
Counting each value should be intuitive and effectively reflect how many times we saw this card before the current hand started. The range of these values depends on the number of decks in the blackjack shoe, which is usually six. Therefore, during our simulations, the number of times we saw a card is between 0 and 24. The results most often take the following values:
Technically, however, integers can be greater than two if hands are split and multiple double-flops occur. Therefore, although it is rare, there is no need to worry if numbers greater than three appear here.
If you look at this data set, you may have come to the same aha moment I recently stumbled upon. Can we apply a simple linear regression to this data set and get the appropriate weights for each map? Our formula for reconstructing the count can be:
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Before we go ahead, it is important to mention some nuances that make this solution less than ideal:
In order for our regression to work, we first need to generate data that is as close as possible to a regular game of blackjack. That’s why you need a simulator. Although there were online blackjack programs written in various languages, most of them lacked the depth I needed, such as dealing cards and tracking hand data. So I put together a quick simulator that you can find HERE on GitHub.
To summarize the code: it goes through a shoe and logs the numbers before and after the hand. The decisions made by the system follow the hard-coded strategy of the standard game. In this scenario, we don’t require the system to learn to play more efficiently as the number changes – the goal is the expected expectation of the next hand.
Note that there are many variations of blackjack rules, but I used these for my simulation program:
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In most cases, these are standard rules, although replacing certain rules with others requires minimal code modification.
Once we’ve created game state and outcomes for millions of hands, it’s time to modify our model. As mentioned earlier, this part is not too complicated because the counting has a linear structure; However, one thing should be noted. Although there are thirteen different cards, there are only ten different values because the 10, J, Q, and K are of the same value. We could apply regression to all thirteen features, but the four maps above have equivalent coefficients. That’s why I decided to combine the number of cards to save some degrees of freedom. From then on, fitting the model follows the standard procedure described below:
The initial bias (intercept) can be translated as the expected profit for betting 1 in a hand where the shoe is fresh or the number is neutral. Since the house is favored, it should not be too surprising that the part is negative. This concept of bias gives us the best understanding of how “unfair” a given set of blackjack rules is. In this simulation, we assume that we lose 0.0226 in the starting hand; As we see more cards, this expectation changes depending on the coefficient weight of each card. Taking into account the above coefficients, it is good for the dealer if the previous hands showed more aces and tens, while all other cards are better for the player. Therefore, the total value of the card must exceed the initial bias in order for the game to evolve in the player’s favor. If this happens, the player will make the maximum bet suggested by their bankroll strategy. From the point of view of the scope of the post, we can assume that in cases where the expectation is less than 0, the player is the minimum, and if it is greater than 0, the player is the maximum. It is important to remember that in traditional counting systems the counting starts from zero, while here we started counting from a negative value. Most systems allow you to start from scratch, using a predetermined range to assess whether the hand is favorable to the player. In some systems, this value is greater than one.
It may seem like we’ve already solved the problem, but there is another caveat that traditional counter systems handle and solve. We tracked the count in progress, but it’s not the same
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This gives a better indication of whether the player will win the next hand or not. The true count is effectively a normalized version of the running count that tries to understand the strength of the count based on the current depth.