EuroJackpot PyLab

Coding the lottery. Keeping it human.

EuroNumbers Prediction Stats

This page looks at the separate “EuroNumbers engine” from the book. For each draw, the model produces a set of 6 candidate EuroNumbers. The real game then draws 2 EuroNumbers out of 12. We count how many of the 2 landed inside the 6 predicted numbers (hits = 0, 1, or 2).

To see if the model does better than random, we compare the historical hit frequencies with the theoretical baseline where the 6 numbers were chosen uniformly at random.

Summary vs random play

Based on 20 resolved predictions.

Hits Model (empirical) Random baseline
0 hits 10.0% 22.7%
1 hit 45.0% 54.5%
2 hits 45.0% 22.7%

Probability of getting at least one EuroNumber correct:
Model: 90.0%, Random: 77.3% (lift ≈ 1.16 vs random)

Average number of EuroNumbers hit per prediction:
Model: 1.350, Random: 1.000

When we only look at perfect EuroNumbers (2/2 inside the 6), the empirical probability is 45.0% compared to 22.7% for random play (lift ≈ 1.98).

Prediction log

Below you can see the EuroNumbers prediction history for the last 10 draws.

# Draw index Prediction Date Predicted set (6 nums) Actual EuroNumbers Hits
1 927 2026-02-06 04 12 05 06 08 09 04 05 2
2 926 2026-02-03 04 12 11 05 06 09 01 11 1
3 925 2026-01-30 04 01 12 05 11 02 01 02 2
4 924 2026-01-27 10 07 04 01 12 11 03 07 1
5 923 2026-01-23 10 07 04 01 08 12 08 09 1
6 922 2026-01-20 07 04 01 09 08 06 06 09 2
7 921 2026-01-16 03 07 04 01 09 02 02 03 2
8 920 2026-01-13 03 11 05 07 04 01 05 11 2
9 919 2026-01-09 03 11 05 07 04 01 06 12 0
10 918 2026-01-06 03 11 05 07 04 06 06 12 1

All of this is descriptive, not a promise. The point is to see whether the EuroNumbers model leans even a little away from pure randomness, and to keep track of that story over time.