Multi-Match Results
On Thursday night, February 13, 2025, the Multi-Match draw in Maryland marked a notable return: 04 11 16 29 33 40 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 February 13, 2025 in Maryland.
Draw times: Evening.
Our take on the Multi-Match results
February 13, 2025Multi-Match report — Thursday night, February 13, 2025: 04 11 16 29 33 40 shows a notable pattern
On Thursday night, February 13, 2025, the Multi-Match draw in Maryland marked a notable return: 04 11 16 29 33 40 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, February 13, 2025, the Multi-Match draw in Maryland marked a notable return: 04 11 16 29 33 40 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, 04 11 16 29 33 40 uses 6 distinct numbers and a wide spread from 4 to 40.
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
The method: this report records outcomes documented for Thursday night, February 13, 2025 and evaluates them against long-run frequency baselines. It is context-focused, not predictive.
From Stepzero
At its core: these reports are intended to preserve a stable long-horizon record as a reference point for continuity. It is meant to inform, not forecast.
Additional Context
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. Distribution analysis depends on consistent documentation. Each draw updates the record, allowing analysts to test whether deviations persist, reverse, or revert to expected ranges.
Adding to the Long-Term Record
In the broader record, this return adds another data point to the cumulative record. Stability comes from the growing record, not any one draw.