Probability Lab · Module

Chaos Wall

Live heatmap of randomness

Every column the simulator generates contributes to this picture. Watch frequency converge toward uniform across 50 main numbers and 12 euro numbers in real time. After enough samples, the wall flattens — that's the Law of Large Numbers turning chaos into predictability.

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Total simulations

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Numbers drawn (main)

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Numbers drawn (euro)

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Max deviation

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Frequency heatmaps

Color intensity ∝ relative frequency vs expected uniform mean

Each cell represents one number. Saturation shows how often that number has been drawn relative to the uniform expectation. Hover for exact counts and Z-score.

Main numbers · 1 to 50—

cold

hot

Euro numbers · 1 to 12—

cold

hot

Hot & cold rankings

Top deviations from uniform · main pool only

Numbers with the largest positive Z-score (drawn more than expected) and the largest negative Z-score (drawn less than expected). These deviations are the random fluctuations of a fair process — they prove nothing about the future.

▲ Hot — drawn MORE than expected

▼ Cold — drawn LESS than expected

Pair-affinity matrix

Which numbers tend to share a column with which

Pick a target number. The grid below shows the relative frequency of every other number co-appearing in a column with the target. In a fair draw, every co-appearance frequency converges to 4/49 ≈ 8.16%.

Target number: Sampled locally over 50 k Pure RNG columns

What you're seeing. Backend simulator generates random Eurojackpot draws and counts every drawn number into a global Postgres table. The frequency array is read every 5 s through GET /api/eurojackpot/frequencies. With billions of columns evaluated, the 1-to-50 grid is now flat to within roughly ±1% — exactly what theory predicts for a Bernoulli sampler at this sample size.