Tri-State Gimme 5 Results
On Wednesday night, March 11, 2026, the Tri-State Gimme 5 draw in Vermont marked a notable return: 07 09 12 16 37 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 575,757 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Winning numbers for 1 draw on March 11, 2026 in Vermont.
Draw times: Evening.
Our take on the Tri-State Gimme 5 results
March 11, 2026Tri-State Gimme 5 report — Wednesday night, March 11, 2026: 07 09 12 16 37 shows a notable pattern
On Wednesday night, March 11, 2026, the Tri-State Gimme 5 draw in Vermont marked a notable return: 07 09 12 16 37 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 575,757 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Overview
On Wednesday night, March 11, 2026, the Tri-State Gimme 5 draw in Vermont marked a notable return: 07 09 12 16 37 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 575,757 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Combo Profile
From a number profile angle, this result settles on 5 distinct numbers with no repeats in the pattern. The range from 7 to 37 is a wide spread.
Why Droughts Matter
Prolonged absences are best treated as context, not directional - they record variance across time. They make variance visible across extended windows.
Data Notes
In detail: this analysis summarizes outcomes logged on Wednesday night, March 11, 2026 with comparison to long-run frequency baselines. The focus is documentation over prediction.
From Stepzero
Simply put: this reporting is built to keep the record consistent over time as a calm, evidence-first reference. The intent is clarity, not prediction.
Additional Context
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
The return of 07 09 12 16 37 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.