Tri-State Gimme 5 Results
On Friday night, March 20, 2026, the Tri-State Gimme 5 draw in Vermont marked a notable return: 13 14 28 31 35 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 20, 2026 in Vermont.
Draw times: Evening.
Our take on the Tri-State Gimme 5 results
March 20, 2026Tri-State Gimme 5 report — Friday night, March 20, 2026: 13 14 28 31 35 shows a notable pattern
On Friday night, March 20, 2026, the Tri-State Gimme 5 draw in Vermont marked a notable return: 13 14 28 31 35 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 Friday night, March 20, 2026, the Tri-State Gimme 5 draw in Vermont marked a notable return: 13 14 28 31 35 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, 13 14 28 31 35 uses 5 distinct numbers and a wide spread from 13 to 35.
Why Droughts Matter
Long gaps remain descriptive, not forward-looking - they show how distribution tails behave. They clarify how far outcomes drift from baseline cadence.
Data Notes
This report summarizes observed outcomes for Friday night, March 20, 2026 and interprets them within the long-run distribution record. It does not imply a forecast or recommendation.
From Stepzero
At its core: this series is meant to maintain continuity across the record as a reference point for continuity. The aim is a trustworthy record.
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
Across the long-term record, this appearance adds one more entry to the record. The accumulation, not any single draw, builds reliability.