All or Nothing Results
01 03 04 07 08 09 10 13 16 21 22 reappeared in the All or Nothing draw on Thursday midday, April 23, 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 April 23, 2026 in Wisconsin.
Draw times: D, Evening.
Our take on the All or Nothing results
April 23, 2026All or Nothing report — Thursday midday, April 23, 2026: 01 03 04 07 08 09 10 13 16 21 22 shows a notable pattern
01 03 04 07 08 09 10 13 16 21 22 reappeared in the All or Nothing draw on Thursday midday, April 23, 2026 after days, a long-gap outcome that warrants documentation in the historical record even when cadence benchmarks are unavailable.
Overview
01 03 04 07 08 09 10 13 16 21 22 reappeared in the All or Nothing draw on Thursday midday, April 23, 2026 after days, a long-gap outcome that warrants documentation in the historical record even when cadence benchmarks are unavailable.
Combo Profile
Beyond the drought, the numbers show a clean structure: 11 distinct numbers with no repeats, spanning 1 to 22 (wide spread).
Why Droughts Matter
Large gaps function as context, not directional - they show how distribution tails behave. They offer context for distribution stability over time.
Data Notes
The method: this report summarizes results recorded for Thursday midday, April 23, 2026 and benchmarks them against historical frequency baselines. It is intended for context, not forecasting.
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
Distribution analysis depends on consistent documentation. Each draw updates the record, allowing analysts to test whether deviations persist, reverse, or revert to expected ranges.
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 measurement matters most when viewed across extended windows. As samples expand, the distribution becomes clearer and anomalies settle into their expected ranges.
Adding to the Long-Term Record
The return of 01 03 04 07 08 09 10 13 16 21 22 expands the archive by one more data point. It is the accumulation of these entries, not a single draw, that defines the reliability of long-horizon analysis.