Deception in Roulette: A Look at Random Number Manipulation

Roulette has been a cornerstone in the study of randomness and statistics since its invention, influencing not only physical casinos but also online platforms. I have created a unique dataset that simulates a roulette wheel, not only to explore the random generation of numbers but also to illustrate how certain techniques can be easily employed by online casinos for fraudulent activities.

Dynamic Factors at Play:

-Temporal and Climatic Variables: Each spin is precisely recorded, integrating sports results and weather conditions that influence fraud techniques.

-Dynamic Fraud Techniques: I have created 53 different fraud techniques, including 5 advanced hybrid techniques that combine various manipulation methods. I select and change fraud techniques daily, adjusting them according to the 'peak hours' of casino traffic to reflect realistic manipulation methods.

-Influence of Historical Results: I use spin histories to determine 'hot' (more frequent) and 'cold' (less frequent) numbers, which are key to deciding the fraud techniques at any given moment.

-Distributions and Biases: The distributions of resulting numbers are adjusted based on these analyses, showing how historical information can be used to manipulate future results.

-Majority of Legitimate Spins: Almost 95% of the spins in this dataset are completely legitimate, without any manipulation, reflecting the normal operation of a roulette wheel.

-Fraud Concentrated During Peak Hours, Weeks, Months, and Days: The remaining 5% corresponds to fraudulent spins, strategically distributed during peak hours, weeks, months, and days, covering a period of one year. This proportion highlights the importance of thoroughly auditing these high-activity periods.

I would love to see more studies on this database, so I encourage everyone who reads this post to share the insights you discover.

Here is the list of strategies used in the dataset (some of them are not as intuitive as they might seem by their names):

0 == No Fraud

1. 'number_bias'
2. 'predictable_sequences'
3. 'color_omission'
4. 'low_range_bias'
5. 'sequence_repetition'
6. 'cyclic_alteration'
7. 'day_night_bias'
8. 'altered_zero_frequency'
9. 'random_alterations'
10. 'temporal_bias'
11. 'day_hour_bias'
12. 'day_of_week_bias'
13. 'day_of_month_bias'
14. 'bimodal_distribution'
15. 'fibonacci_bias'
16. 'parity_alteration'
17. 'prime_sequence'
18. 'double_sinusoidal_distribution'
19. 'normal_distribution'
20. 'time_series_patterns'
21. 'adaptive_variation'
22. 'wear_simulation'
23. 'advanced_hybrid_1'
24. 'advanced_hybrid_2'
25. 'advanced_hybrid_3'
26. 'advanced_hybrid_4'
27. 'advanced_hybrid_5'
28. 'previous_result_sum_bias'
29. 'special_dates_bias'
30. 'weighted_global_events_distribution'
31. 'previous_winning_combinations_bias'
32. 'sentiment_analysis_alteration'
33. 'weighted_day_of_month_bias'
34. 'weather_patterns_bias'
35. 'weighted_hour_of_day_distribution'
36. 'sports_events_bias'
37. 'lunar_cycles_modulation'
38. 'high_range_bias'
39. 'inverse_prime_sequence'
40. 'alternate_parity_bias'
41. 'zero_series_frequency'
42. 'game_history_bias'
43. 'gaussian_noise_modulation'
44. 'time_weighted_distribution_bias'
45. 'last_digit_bias'
46. 'cumulative_temporal_bias'
47. 'hidden_previous_results_patterns'
48. 'weighted_hot_cold_oscillation'
49. 'adaptive_hot_cold_sequence'
50. 'cold_number_mirage'
51. 'hot_number_evasion'
52. 'false_cold'
53. 'hot_deviation'

Attached is an example of analysis for a specific hour using a specific strategy, in this case, "double_sinusoidal_distribution":