Mathematical Design Exercise • No Prediction • No Historical Data
Objective: Exploring C(v, k, t) covering array designs to maximize target coverage probability (e.g., 3+) via Monte Carlo verification.
Key Distinction: "Full Coverage" indicates that all numbers from the pool are utilized, not all possible combinations.
Disclaimer: Coverage rates represent hit probability, not financial profit.
⚠️ Not gambling advice • Main numbers only • Bonus balls excluded.
Mathematical Design Exercise • No Prediction • No Historical Data
Objective: Exploring C(v, k, t) covering array designs to maximize target coverage probability (e.g., 3+) via Monte Carlo verification.
Key Distinction: "Full Coverage" indicates that all numbers from the pool are utilized, not all possible combinations.
Disclaimer: Coverage rates represent hit probability, not financial profit.
⚠️ Not gambling advice • Main numbers only • Bonus balls excluded
12/80 • 74 Columns
97.8%
Hit Rate (6+)
Hits Distribution (100,000 Monte Carlo draws)
Every draw hits at least 5 numbers. 97.8% hit at least 6.
10/70 • 80 Columns
93.1%
Hit Rate (5+)
Hits Distribution (100,000 Monte Carlo draws)
Every draw hits at least 5 numbers. 93.1% hit at least 5.
Unlike standard lottery systems (where the target is 3+), Keno games require higher match counts (6+ or 5+) due to the larger number of picks per column. The algorithm optimizes columns accordingly.
Key insight: Keno systems achieve very high coverage (93-98%) with relatively few columns (74-80), eliminating low-hit draws entirely.
Test the Algorithm with 10 Numbers
Select Game: Choose a lottery (Mega Millions, Powerball, etc.) to auto-set the number pool.
Pick 10 Numbers: Select your 10-number subset (Manual or Random).
Generate: The algorithm synthesizes 4 optimized columns from your selection.
Verify: Run a Monte Carlo simulation (up to 1,000,000 draws) to see the real-time coverage breakdown.
