Multi-Match Results
On Monday night, May 20, 2024, the Multi-Match draw in Maryland marked a notable return: 03 06 16 22 25 34 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 6,096,454 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Winning numbers for 1 draw on May 20, 2024 in Maryland.
Draw times: Evening.
Our take on the Multi-Match results
May 20, 2024Multi-Match report — Monday night, May 20, 2024: 03 06 16 22 25 34 shows a notable pattern
On Monday night, May 20, 2024, the Multi-Match draw in Maryland marked a notable return: 03 06 16 22 25 34 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 6,096,454 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Overview
On Monday night, May 20, 2024, the Multi-Match draw in Maryland marked a notable return: 03 06 16 22 25 34 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 6,096,454 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Combo Profile
As a number pattern, 03 06 16 22 25 34 uses 6 distinct numbers and a wide spread from 3 to 34.
Why Droughts Matter
Deep gaps are context, not forward-looking - they show where spacing departs from typical cadence. They clarify how far outcomes drift from 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 produces these reports to provide a calm, evidence-first record of how draw patterns unfold over time. The aim is clarity and continuity - a reference point for long-horizon tracking rather than a call to action.
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. 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, 03 06 16 22 25 34 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.