Multi-Match Results
On Monday night, June 1, 2026 in Maryland, 01 04 12 16 18 21 showed up after a -day wait in the Maryland record. Against an expected cadence of 1 in 6,096,454 draws, the gap stands out as a long-horizon outlier.
Winning numbers for 1 draw on June 1, 2026 in Maryland.
Draw times: Evening.
Our take on the Multi-Match results
June 1, 2026Multi-Match report — Monday night, June 1, 2026: 01 04 12 16 18 21 shows a notable pattern
On Monday night, June 1, 2026 in Maryland, 01 04 12 16 18 21 showed up after a -day wait in the Maryland record. Against an expected cadence of 1 in 6,096,454 draws, the gap stands out as a long-horizon outlier.
Overview
On Monday night, June 1, 2026 in Maryland, 01 04 12 16 18 21 showed up after a -day wait in the Maryland record. Against an expected cadence of 1 in 6,096,454 draws, the gap stands out as a long-horizon outlier.
Combo Profile
Beyond the drought, the numbers show a clean structure: 6 distinct numbers with no repeats, spanning 1 to 21 (wide spread).
Why Droughts Matter
Extended gaps function as context, not a signal - they show how distribution tails behave. Their value is in long-horizon tracking.
Data Notes
This analysis uses the draw results recorded for Monday night, June 1, 2026 and compares them against the observed historical cadence for the game. This is descriptive, based on frequency tracking - not predictive modeling.
From Stepzero
At Stepzero, the priority is accuracy and context. This report is intended as a historical record entry, not a forecast.
Additional Context
Record-keeping at scale becomes the foundation for analysis. Each outcome, whether typical or unusual, contributes to the stability and clarity of the long-run picture.
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.
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, 01 04 12 16 18 21 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.