Real Valued Card Counting Strategies for the Game of Blackjack
Card counting is a family of casino card game advantage gambling strategies, in which a player keeps a mental tally of the cards played in order to calculate whether the next hand is likely to be in the favor of the player or the dealer. A card counting system…
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Conference paper
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First Online: 20 November 2020
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pp 63–73
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Card counting is a family of casino card game advantage gambling strategies, in which a player keeps a mental tally of the cards played in order to calculate whether the next hand is likely to be in the favor of the player or the dealer. A card counting system assigns point values (weights) to the cards. Summing the point values of the already played cards gives a concise numerical estimate of how advantageous the remaining cards are for the player. In theory, any assignment of weights is permissible. Historically, card counting systems used integers and rarely the 1/2 and 3/2 fractions, as computation with these are easier and more tractable for the human memory.
In this paper we investigate how much advantage would a system using real valued weights provide. Using a blackjack simulator and a simple genetic algorithm, we evolved weights vectors for ace-neutral and ace-reckoned balanced strategies with a fitness function that indicates how much a given strategy empirically under or outperforms a simple card counting system. After convergence, we evaluated the systems in the three efficiency categories used to characterize card counting strategies: playing efficiency, betting and insurance correlation. The obtained systems outperform classical integer count techniques, offering a better balance of the efficiency metrics. Finally, by applying rounding and scaling, we transformed some real valued strategies to integer point counts and found that most of the systems’ extra edge is preserved. However, because of the large weight values, it is unlikely that these systems can be played quickly and accurately even by professional card counters.
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Authors and Affiliations
- Computational Intelligence Research Group, Sapientia Hungarian University of Transylvania, Târgu-Mureş, Romania
Mózes Vidámi, László Szilágyi & David Iclanzan 65. Physiological Controls Research Center, Obuda University, Budapest, Hungary
László Szilágyi 66. Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
László Szilágyi Authors68. Mózes VidámiView author publicationsYou can also search for this author in PubMed Google Scholar 69. László SzilágyiView author publicationsYou can also search for this author in PubMed Google Scholar 70. David IclanzanView author publicationsYou can also search for this author in PubMed Google Scholar
Corresponding author
Correspondence to David Iclanzan .
- Computational Intelligence Research Group, Sapientia Hungarian University of Transylvania, Târgu-Mureş, Romania
Mózes Vidámi, László Szilágyi & David Iclanzan 74. Physiological Controls Research Center, Obuda University, Budapest, Hungary
László Szilágyi 75. Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
László Szilágyi Authors77. Mózes VidámiView author publicationsYou can also search for this author in PubMed Google Scholar 78. László SzilágyiView author publicationsYou can also search for this author in PubMed Google Scholar 79. David IclanzanView author publicationsYou can also search for this author in PubMed Google Scholar
Corresponding author
Correspondence to David Iclanzan .
Editors and Affiliations
- Department of AI, Ping An Life, Shenzhen, China
Haiqin Yang 84. Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand
Kitsuchart Pasupa 85. City University of Hong Kong, Kowloon, China
Andrew Chi-Sing Leung 86. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong
James T. Kwok 87. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
Jonathan H. Chan 88. The Chinese University of Hong Kong, New Territories, Hong Kong
Irwin King 89. Department of AI, Ping An Life, Shenzhen, China
Haiqin Yang 90. Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand
Kitsuchart Pasupa 91. City University of Hong Kong, Kowloon, China
Andrew Chi-Sing Leung 92. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong
James T. Kwok 93. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
Jonathan H. Chan 94. The Chinese University of Hong Kong, New Territories, Hong Kong
Irwin King Reprints and permissions
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Vidámi, M., Szilágyi, L., Iclanzan, D. (2020). Real Valued Card Counting Strategies for the Game of Blackjack. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12533. Springer, Cham. https://doi.org/10.1007/978-3-030-63833-7_6
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Vidámi, M., Szilágyi, L., Iclanzan, D. (2020). Real Valued Card Counting Strategies for the Game of Blackjack. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12533. Springer, Cham. https://doi.org/10.1007/978-3-030-63833-7_6
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Published: 20 November 2020
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Publisher Name: Springer, Cham
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