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Make Medium yours. It appears that whatever strategy that runs first in the simulation will result in wildly different values in the beginning. Roman Orac in Towards Data Science. This can be done simply by plotting the results for each strategy in a line graph format using Matplotlib. You signed in with another tab or window. A Medium publication sharing concepts, ideas, and codes. It is also one of the easiest games to learn. Make sure that this pickle file is in the same folder as the Blackjack code in order for it to run properly. Next, in order to count cards during the game, we created a Pandas DataFrame of the different card counting strategies and their specific values for every card:. It should be noted that we do not endorse gambling as a method of making money. However, we must temper our expectations for each strategy. Discover Medium. However, just know that these simulations were done with a basic hit or stay decision and some of these strategies do actually factor in bet size as well as other factors. Towards Data Science A Medium publication sharing concepts, ideas, and codes. All in all, good luck to those who decide to use these strategies! Then, you add them all together and this total value becomes the count. It eventually compares the hand of the dealer to the hand of the player to determine who won. We recorded the last 10 rounds of each game in order to capture the effectiveness of each strategy because those rounds will have a decent count from which a decision could be made. Towards Data Science Follow. We shuffled the order of the strategies to be simulated to further increase the randomness. So our observation was correct in assuming that whatever strategy ran first results in erratically different values. Simulating 1, Games of Blackjack Now that we have a Blackjack game coded out and ready to go, we can finally begin simulating Blackjack games with different card counting strategies 1, times each. Running this code will give us the following graph:. Chanin Nantasenamat in Towards Data Science. We begin the simulation by entering our function for playing Blackjack games into a loop that will iterate through a list of strategies and then play 1, games with each strategy. By simulating the various different strategies, we can narrow down the preferred method of card counting that will benefit us in the best way possible. Well, one option we could do is to implement each strategy ourselves, go and play Blackjack several times, then record the results. Using Card Counting in Blackjack Card counting may appear difficult at first, but it is actually quite simple. More From Medium. Data Classes in Python. However, the weight of those outcomes is entirely up to you because you can risk to lose a lot of money in just one hand. Also, please know that these simulations and results were made with relatively simple Python methods and tools. With that handled, we can move on to simulating the games. You signed out in another tab or…. For example, one strategy may count the Ace as -1 and another strategy will count it as 0. Then, we added card counting functions to keep track of the count while the game is playing:. Card counters use this count to determine whether to hit or stay depending on whether the count is high or low. Julia Nikulski in Towards Data Science. But the overall results for every strategy are very similar. This option is what we will be demonstrating in order to determine the most effective card counting strategy. There are many more differences between the strategies than just the card values but we cannot cover them all. Each function serves an important purpose for running the game. First, in order to simulate a game of Blackjack, we would need to code out a Blackjack game with all the necessary rules and regulations that come with it. So what if we wanted to implement a card counting strategy into our Blackjack games? Chris in Towards Data Science. Beating the Dealer with Programming. See responses 7. These functions keep track of the count throughout every round of Blackjack in the entire game. We reiterate that each strategy is reported to give the player a 0. Marco Santos Follow. Now that we have a Blackjack game coded out and ready to go, we can finally begin simulating Blackjack games with different card counting strategies 1, times each. Another option can be to code out a game of Blackjack, simulate the game, then record the results after a desired number of simulations. Also, since we are measuring wins versus losses, we can eliminate the need for betting. You can either beat the dealer, lose to the dealer, or tie with the dealer. Implementing either one of these strategies seems to be enough to give you an advantage over the casino or house. This function follows the rules of every Blackjack dealer. To simulate a Blackjack game with our code, we will have to alter our previous Blackjack code to compensate for the lack of player input. It is important to note that these card counting strategies at most, give a 0. In order to tilt the odds against the casino or the house, Blackjack players have developed numerous card counting strategies to decrease their chances of losing. Finally, we have simulated over 1, games of Blackjack for each different strategy we have listed in the Pandas DataFrame. Written by Marco Santos Follow. There are two versions of this game:. It deals until the dealer reaches a value between 17 to 21 or until the dealer busts. For the game of Blackjack, that is all it takes. Closing Thoughts As it appears in the graphs before, each strategy can provide a decent edge over the dealer. Become a member.

B lackjack is one of the simplest games to play at a casino. About Help Legal.

The goal is simple; just try to get as close to the value of 21 as best as you can without going over. This function may require some tweaking to better improve results later on. However, betting size in a normal Blackjack game is important to know. In this code to create a DataFrame of card counting values, we exported a pickle of the DataFrame for use later on. Sign in. How to process a DataFrame with billions of rows in seconds. The outcomes of Blackjack are very straightforward. That option can be extremely time consuming and inaccurate in actually showing the probability and effectiveness of the strategy. To keep things simple, we would leave out options such as splitting , doubling down , surrendering , etc. But, card counting can provide you with an edge and it really does not matter which strategy you choose to stick with. Which one should we choose? Many different strategies for card counting exist and these strategies implement different values for each card. It appears that most of the card counting strategies have similar results but they all seem to beat out having no strategy at all. No one wants to lose and losing in Blackjack can hurt depending on your bet size. This also seems to provide a decent percentage of success as long as the strategy is followed with no deviations. Card counting may appear difficult at first, but it is actually quite simple. Now, we just need to graph the results from our simulations. Also, we included draws with our wins because a draw in Blackjack results in no money lost when playing against just the dealer. As it appears in the graphs before, each strategy can provide a decent edge over the dealer. Now that we have all of our functions ready, we can run Blackjack. Emmett Boudreau in Towards Data Science. Feel free to test out the Blackjack game in your own terminal or IDE.