Multi-Match Results
On Thursday night, March 26, 2026, the Multi-Match draw in Maryland marked a notable return: 01 16 23 24 26 29 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 March 26, 2026 in Maryland.
Draw times: Evening.
Our take on the Multi-Match results
March 26, 2026Multi-Match report — Thursday night, March 26, 2026: 01 16 23 24 26 29 shows a notable pattern
On Thursday night, March 26, 2026, the Multi-Match draw in Maryland marked a notable return: 01 16 23 24 26 29 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 Thursday night, March 26, 2026, the Multi-Match draw in Maryland marked a notable return: 01 16 23 24 26 29 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, 01 16 23 24 26 29 uses 6 distinct numbers and a wide spread from 1 to 29.
Why Droughts Matter
A long drought is descriptive rather than predictive. It records variance across time and helps analysts evaluate whether outcomes are tracking within expected frequency bands or drifting into the tails of the distribution.
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
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
The return of 01 16 23 24 26 29 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.