Can You Count Cards At Online Blackjack?
This post is in NO way an attempt to promote blackjack or the act of gambling.
Any time you blackjack helper card at a casino, the odds are stacked against you — and over time you WILL lose money.
I have updated both the code below and.
One of the classic applications of probability and statistics is the study of games of chance gambling.
Games of chance card games, dice strip blackjack flash, etc.
Today, we will study blackjack by writing up a blackjack simulator in Python, simulating a bunch of games, and then studying how go here player did.
Aces can be worth either 1 or 11; every other card is worth its face amount face cards are worth 10.
If hitting results in the player busting total going over 21then his or her bet is lost.
No money is exchanged in the event of a tie.
If you would like to Time to start coding!
Coding Up our Simulator It probably would have been a good idea to use object oriented programming.
But at this point, I am not yet used to writing code in that way.
I will probably revise my code at some point in the future to be object oriented; but that is a project for another day.
First, we need a function that creates a new deck of cards for us to play with.
It is slightly more complicated than simple summation because aces can be worth either 1 or 11, depending on whichever is most advantageous to its blackjack helper card />So our function first tallies up the value of each non-ace card in the hand I represent all face cards with the number 10 as they are all functionally the same in blackjack.
Then it counts up the number of aces.
Finally, it determines how much each ace should be worth depending on the value of the rest of your cards.
Corrections: In my previous version of the code below, blackjack helper card was an error.
At that point it moves onto the next stack.
Each element in this array corresponds to one player at the blackjack table.
In order to deal the cards, I take advantage of the pop function with an input of 0 — this returns the first element of a list while simultaneously removing it from that list perfect for dealing cards from a stack.
Notice that in the previous code block, I defined blackjack as a set that includes an ace and a 10.
The players do their own blackjack checks — if they have one, they win in some casinos blackjack pays 1.
For the players without blackjack, they now have the option to hit, stay, etc.
For this simulation, my objective was to capture blackjack helper card kinds of player decisions — smart ones, lucky ones, and stupid ones.
So I based the decision for the player on a coin flip if random.
I am not trying to figure out the optimal strategy right this instance.
Rather, I want to use this simulator to generate training data, with which I can eventually train a to play blackjack optimally in a future post.
The dealer must hit until either he or she busts or has a hand that sums blackjack helper card at least 17.
So the while loop deals cards to the dealer until 17 is reached, then we check if our dealer busted.
I ran the simulator for 50,000 decks.
But then after 6, the probability declines dramatically.
This partially explains why the probabilities from 2 to 6 are on average higher than those from 7 to ace.
And if he must hit, there is a decent chance that he will bust.
This explains why the probability increases from 2 to 6.
So if the dealer is showing a 6, assuming we are not card counting the most likely preliminary total for our dealer is 16.
Since 16 is less than 17, he must hit.
And there are many cards that could cause him to bust — anything worth 6 or more.
The same logic holds true if the dealer is showing a 5, just that there is one fewer card that would cause him to bust has to be 7 or more now.
In this case, odds are that the other hidden card is worth 10.
Then the players who have 16 or less will feel compelled to hit.
But if they do hit, then they face a high probability of busting due to all the 10s.
It also makes sense why initial player hand values of 4 and 5 have the blackjack helper card lowest probabilities.
In an upcoming post, I will use the training data that I generated above to train a neural network to play blackjack.
That way I can examine what the machine chooses as the optimal strategy.
So even if he or she is at 20, there is still a 50% chance of hitting.
So instead of a coin flip, our new decision rule is to keep hitting only if our total hand value is 11 or less.
While the old trends are still there, no matter what card the dealer shows, our probability of not losing money has increased.
We have improved our probability of winning for all initial hand values besides 12 through 16.
Those hand values are relatively unaffected because by choosing to stay in order to eliminate the risk of bustingwe make it easier for the dealer to beat our blackjack helper card because the dealer can only stop hitting when he or she reaches a hand value of 17 or more.
But for all other hand values, it looks like our strategy of avoiding busts is pretty helpful.
I hope you enjoyed reading, and stay tuned for the next post where we see if a neural network can beat our naive strategy.
Cheers and remember — NEVER bet what you cannot afford to lose!
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