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I met Dan Pronovost, the developer of Speed Count, in cyberspace. Dan is a mathematical genius and developer of blackjack training software programs. Nothing will substitute for getting 6 decks, hand shuffling, and playing out 6 hands at a time (where only your 1 hand is your hand counting and changing your bet, etc, etc). Physical cards, in my opinion, are the best and most proficient way of training for counting. The player to the immediate left of the dealer button in the small blind receives the first card and then the dealer pitches cards around the table in a clockwise motion from player to player until each has received two starting cards. Once we’ve generated the game state and outcome for millions of hands, it is time to fit our model. As mentioned earlier, since counting has a linear structure, this part isn’t too complex; however, there is one thing to note. While there are thirteen different cards, only ten different values exist as 10, J, Q, and K are all worth the same. We could fit the regression on all thirteen of these features, but the four cards mentioned above end up with equivalent coefficients. Therefore, I’ve chosen to combine the counts of these cards into one to save a few degrees of freedom. From there, fitting the model follows the standard procedure as detailed below: