Multi-Match Results
On Thursday night, January 16, 2025, the Multi-Match draw in Maryland marked a notable return: 09 13 27 32 40 43 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 January 16, 2025 in Maryland.
Draw times: Evening.
Our take on the Multi-Match results
January 16, 2025Multi-Match report — Thursday night, January 16, 2025: 09 13 27 32 40 43 shows a notable pattern
On Thursday night, January 16, 2025, the Multi-Match draw in Maryland marked a notable return: 09 13 27 32 40 43 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, January 16, 2025, the Multi-Match draw in Maryland marked a notable return: 09 13 27 32 40 43 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
The numbers in 09 13 27 32 40 43 cover a wide range (9 to 43) with no repeats.
Why Droughts Matter
Long gaps are context markers, not forward-looking - they mark how variance accumulates over long samples. They provide a clean read on long-run variance.
Data Notes
This analysis uses the draw results recorded for Thursday night, January 16, 2025 and compares them against the observed historical cadence for the game. This is descriptive, based on frequency tracking - not predictive modeling.
From Stepzero
The core idea: this series is meant to keep the record consistent over time as context for disciplined analysis. The priority is accuracy and continuity.
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. 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
From a long-horizon view, this entry adds a fresh entry to the record to the historical dataset. The accumulation, not any single draw, builds reliability.