All or Nothing Results
04 05 09 11 14 15 16 17 20 21 22 reappeared in the All or Nothing draw on Thursday midday, May 21, 2026 after days, a long-gap outcome that warrants documentation in the historical record even when cadence benchmarks are unavailable.
Winning numbers for 2 draws on May 21, 2026 in Wisconsin.
Draw times: D, Evening.
Our take on the All or Nothing results
May 21, 2026All or Nothing report — Thursday midday, May 21, 2026: 04 05 09 11 14 15 16 17 20 21 22 shows a notable pattern
04 05 09 11 14 15 16 17 20 21 22 reappeared in the All or Nothing draw on Thursday midday, May 21, 2026 after days, a long-gap outcome that warrants documentation in the historical record even when cadence benchmarks are unavailable.
Overview
04 05 09 11 14 15 16 17 20 21 22 reappeared in the All or Nothing draw on Thursday midday, May 21, 2026 after days, a long-gap outcome that warrants documentation in the historical record even when cadence benchmarks are unavailable.
Combo Profile
As a number pattern, 04 05 09 11 14 15 16 17 20 21 22 uses 11 distinct numbers and a wide spread from 4 to 22.
Why Droughts Matter
Extended absences like this provide context, not direction. They show how randomness behaves across large samples and help analysts quantify how often the system deviates from its baseline cadence.
Data Notes
Results are evaluated against historical frequency baselines where available. The goal is documentation and context rather than prediction.
From Stepzero
Stepzero focuses on documenting distribution behavior over large samples. Each report is a snapshot of observed outcomes, designed to support disciplined, long-term analysis.
Additional Context
Context improves with scale. As more draws accumulate, isolated anomalies either normalize into baseline rates or reveal persistent deviations that warrant closer monitoring. Long-horizon tracking is the only reliable way to separate short-term noise from persistent drift. By logging each outcome against its expected cadence, the system builds a distribution profile that becomes more stable as the sample grows.
Adding to the Long-Term Record
With its return, 04 05 09 11 14 15 16 17 20 21 22 contributes another meaningful data point to the historical dataset. Each draw - whether routine or statistically unusual - refines the long-term view of how large random systems behave over time.