Note: This module is an educational simulation. Its purpose is to observe how a dynamic
selection model behaves under explicit rules and limits.
1) Data input
Results (0–36) are entered manually. The history is stored and used by the model.
2) Analysis window
The model analyzes up to the latest 50 values and applies a recency weighting.
3) “Top 6” selection
Six numbers with the highest score (weighted frequency) are selected and refreshed according to the experiment rules.
4) Experiment limits
The run stops when thresholds are reached (e.g., iteration count or long negative streaks) to avoid misleading readings.
Visible metrics:
- Consecutive misses: counts outcomes that do not match the current selection.
- Spins in this run: iterations recorded in the current run (with a minimum of 50 to activate calculations).
- History: the latest entered values (editable in case of input corrections).
Important: the information shown here should not be interpreted as prediction or as guidance for real-world use.