Tri-State Gimme 5 Results
On Tuesday night, March 17, 2026, the Tri-State Gimme 5 draw in Vermont marked a notable return: 06 09 14 20 39 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 17, 2026 in Vermont.
Draw times: Evening.
Our take on the Tri-State Gimme 5 results
March 17, 2026Tri-State Gimme 5 report — Tuesday night, March 17, 2026: 06 09 14 20 39 shows a notable pattern
On Tuesday night, March 17, 2026, the Tri-State Gimme 5 draw in Vermont marked a notable return: 06 09 14 20 39 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 Tuesday night, March 17, 2026, the Tri-State Gimme 5 draw in Vermont marked a notable return: 06 09 14 20 39 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
As a number pattern, 06 09 14 20 39 uses 5 distinct numbers and a wide spread from 6 to 39.
Why Droughts Matter
Large gaps function as context, not a forecast - they document what has already happened. They clarify how far outcomes drift from baseline cadence.
Data Notes
Specifically: this analysis summarizes the draw results for Tuesday night, March 17, 2026 with benchmarking against long-run cadence. It is intended for context, not forecasting.
From Stepzero
The core idea: this series is designed to sustain continuity in the archive as a reference point for continuity. The intent is clarity, not prediction.
Additional Context
Long-horizon measurement matters most when viewed across extended windows. As samples expand, the distribution becomes clearer and anomalies settle into their expected ranges. 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
In long-horizon tracking, this result extends the historical ledger to the record. The long-run picture sharpens as entries accrue.