All or Nothing Results
On Saturday midday, May 30, 2026, during the All or Nothing draw in Wisconsin, 02 04 05 07 08 11 12 15 17 18 22 showed up again after a -day wait in the Wisconsin draw record. Given an expected cadence of 1 in 705,432 draws, the interval lands deep in the long-gap tail.
Winning numbers for 2 draws on May 30, 2026 in Wisconsin.
Draw times: D, Evening.
Our take on the All or Nothing results
May 30, 2026All or Nothing report — Saturday midday, May 30, 2026: 02 04 05 07 08 11 12 15 17 18 22 shows a notable pattern
On Saturday midday, May 30, 2026, during the All or Nothing draw in Wisconsin, 02 04 05 07 08 11 12 15 17 18 22 showed up again after a -day wait in the Wisconsin draw record. Given an expected cadence of 1 in 705,432 draws, the interval lands deep in the long-gap tail.
Overview
On Saturday midday, May 30, 2026, during the All or Nothing draw in Wisconsin, 02 04 05 07 08 11 12 15 17 18 22 showed up again after a -day wait in the Wisconsin draw record. Given an expected cadence of 1 in 705,432 draws, the interval lands deep in the long-gap tail.
Combo Profile
From a number-profile view, 02 04 05 07 08 11 12 15 17 18 22 uses 11 distinct numbers with no repeats in the pattern. The numbers span 2 to 22, a wide spread.
Why Droughts Matter
Prolonged absences are best read as context, not a forecast - they record variance across time. Their value is in long-horizon tracking.
Data Notes
To clarify: this report captures the results logged for Saturday midday, May 30, 2026 and anchors them against historical cadence. The intent is documentation, not forecasting.
From Stepzero
Simply put: this reporting is designed to keep the record consistent over time as a calm, evidence-first reference. The intent is clarity, not prediction.
Additional Context
Stability comes from the accumulation of entries. One draw alone does not define the pattern, but the record grows more reliable with each addition to the dataset. Stability comes from the accumulation of entries. One draw alone does not define the pattern, but the record grows more reliable with each addition to the dataset.
Adding to the Long-Term Record
Across the long-term record, this draw adds another archive entry to the record. Reliability is a function of the growing record.