Comparison of the effectiveness of roulette betting strategies using Monte Carlo simulation
Article Sidebar
Issue Vol. 40 (2026)
-
Analysis of the capabilities of predictive artificial intelligence models in corporate risk management
Kacper Ziemski188-192
-
Usability and availability of selected e-commerce services
Marcin Kozicki, Maria Skublewska-Paszkowska193-200
-
Comparison of C++ and Python performance based on selected algorithms
Szymon Bogucki, Kacper Burda201-205
-
Security analysis of selected web applications using vulnerability scanners
Mariusz Choroś, Marta Dziuba-Kozieł206-212
-
Comparison of Java and .NET reflection mechanisms for dynamic module loading: a performance benchmark study
Michał Mazur, Sebastian Maruszak, Marek Miłosz213-217
-
Comparative analysis of network vulnerability detection tools
Mateusz Zdunek218-225
-
Evaluation of mobile applications for personal finance management using the MARS scale
Łukasz Nikiel, Artsiom Patskevich, Marek Miłosz226-231
-
Comparison of the effectiveness of roulette betting strategies using Monte Carlo simulation
Marek Sarnecki232-238
-
Analysis of optimization capabilities of selected database management systems
Paweł Tarkiewicz, Małgorzata Plechawska-Wójcik239-246
-
Comparative analysis of Espresso and Appium frameworks for automated UI testing of Android mobile applications
Jakub Derkacz247-254
-
Comparative analysis of the applicability of artificial intelligence models for code generation
Patryk Warchoł, Małgorzata Plechawska-Wójcik255-262
-
Comparison of the effectiveness of selected tools for detecting texts generated by artificial intelligence
Marcin Brodacki, Małgorzata Plechawska-Wójcik263-269
-
Comparative analysis of selected containerization tools in terms of MCP
Paweł Jan Tłusty, Maciej Pańczyk270-276
-
SpikeCliff effect: empirical analysis of deterministic timing discontinuities in sponge-based XOF functions
Łukasz Wójcik, Stanisław Lota277-282
-
Comparison of AI agents for creating SQL queries
Julia Sierpień, Maria Skublewska-Paszkowska283-288
-
Comparative analysis of the performance of PostgreSQL and Neo4j databases in the context of genealogical queries
Michał Muzyka, Mateusz Niedźwiedź, Marek Miłosz289-296
-
Evaluation of the effectiveness of static and dynamic methods in malware analysis
Dominik Tracz, Daniel Sawicki, Konrad Gromaszek297-303
-
Comparison of classical machine learning methods in the task of obesity level classification
Paweł Biesaga, Paweł Powroźnik304-312
Main Article Content
Authors
Abstract
This study presents the performance of seven betting strategies in European roulette via Monte Carlo simulations across 48 scenarios. Performance metrics included mean and median net profit, win and ruin probability, maximum drawdown, VaR and CVaR. Results confirmed that all strategies have negative expected value. Progressive strategies had higher short-term win rates but incurred much heavier loss tails. Fractional Kelly and Fixed-fractional showed moderate profiles with no long-term advantage. The fixed-stake strategy proved to be the safest, effectively limiting the risk of ruin. The study can be used to modify new betting strategies and serve as a warning to casino users.
Keywords:
Sustainable Development Goal (SDG)
- Industry, Innovation, Technology and Infrastructure
References
[1] Gambling - Worldwide | Statista Market Forecast, Statista, https://www.statista.com/outlook/amo/gambling/worldwide, [09.05.2026].
[2] 7 Most Successful Roulette Strategies A Finish Guide, Grand Hotel Son Net, https://sonnet.es/blog/2025/01/17/7-most-successful-roulette-strategies-a-finish-guide/, [17.05.2026].
[3] Rules of Roulette | Instructions for the Casino Classic, https://www.mastersofgames.com/rules/roulette-rules.htm, [17.05.2026].
[4] J. S. Croucher, A Comparison of Strategies for Playing Even Money Bets in Roulette, Teaching Statistics 27(1) (2005) 20–23, https://doi.org/10.1111/j.1467-9639.2005.00193.x.
[5] P. Pflaumer, A Statistical Analysis of the Roulette Martingale System: Examples, Formulas and Simulations with R, International Conference on Gambling and Risk Taking, 2019, https://oasis.library.unlv.edu/gaming_institute/2019/May29/8/, [14.03.2026].
[6] S.-K. (Amang) Kim, Kelly Criterion Extension: Advanced Gambling Strategy, Mathematics 12(11) (2024) 1725, https://doi.org/10.3390/math12111725
[7] B. P. Jacot and P. V. Mochkovitch, Kelly criterion and fractional Kelly strategy for non-mutually exclusive bets, Journal of Quantitative Analysis in Sports 19(1) (2023) 37–42, https://doi.org/10.1515/jqas-2020-0122.
[8] D. Duffie and J. Pan, An Overview of Value at Risk, The Journal of Derivatives 4(3) (1997) 7–49, https://doi.org/10.3905/jod.1997.407971.
[9] R. T. Rockafellar and S. Uryasev, Optimization of conditional value-at-risk, The Journal of Risk 2(3) (2000) 21–41, https://doi.org/10.21314/JOR.2000.038.
[10] D. P. Kroese, T. Taimre, and Z. I. Botev, Handbook of Monte Carlo Methods, 1st ed., Wiley, 2011, https://doi.org/10.1002/9781118014967.
[11] P. Wlaź, Symulacje Monte Carlo. Teoria i zastosowania, Politechnika Lubelska, 2025, https://doi.org/10.35784/9788379476404.
[12] Y. Cheng, Monte Carlo-Based VaR Estimation and Backtesting Under Basel III, Risks 13(8) (2025) 146, https://doi.org/10.3390/risks13080146.
[13] J. Wang, S. Wang, M. Lv, and H. Jiang, Forecasting VaR and ES by using deep quantile regression, GANs-based scenario generation, and heterogeneous market hypothesis, Financial Innovation 10(1) (2024) 36, https://doi.org/10.1186/s40854-023-00564-5.
[14] C. M. Borror, Practical Nonparametric Statistics, 3rd Ed., Journal of Quality Technology 33(2) (2001) 260–260, https://doi.org/10.1080/00224065.2001.11980074.
[15] A. Stanisz, Przystępny kurs statystyki z zastosowaniem Statistica PL na przykładach z medycyny. T. 1: Statystyki podstawowe, Wyd. 3 zm. i popr, StatSoft Polska, Kraków, 2006.
Article Details
Abstract views: 1

