Tri-State Gimme 5 Results
On Monday night, March 16, 2026, the Tri-State Gimme 5 draw in Vermont brought 01 08 17 30 35 back after days away. Given an expected cadence of 1 in 575,757 draws, this interval places the result well beyond typical spacing and makes it a meaningful entry for long-term distribution tracking.
Winning numbers for 1 draw on March 16, 2026 in Vermont.
Draw times: Evening.
Our take on the Tri-State Gimme 5 results
March 16, 2026Tri-State Gimme 5 report — Monday night, March 16, 2026: 01 08 17 30 35 shows a notable pattern
On Monday night, March 16, 2026, the Tri-State Gimme 5 draw in Vermont brought 01 08 17 30 35 back after days away. Given an expected cadence of 1 in 575,757 draws, this interval places the result well beyond typical spacing and makes it a meaningful entry for long-term distribution tracking.
Overview
On Monday night, March 16, 2026, the Tri-State Gimme 5 draw in Vermont brought 01 08 17 30 35 back after days away. Given an expected cadence of 1 in 575,757 draws, this interval places the result well beyond typical spacing and makes it a meaningful entry for long-term distribution tracking.
Combo Profile
Beyond the drought, the numbers show a clean structure: 5 distinct numbers with no repeats, spanning 1 to 35 (wide spread).
Why Droughts Matter
Extended gaps are best read as context, not prescriptive - they show where spacing departs from typical cadence. They offer context for distribution stability over time.
Data Notes
The approach: this analysis documents the recorded draws for Monday night, March 16, 2026 and anchors them against historical cadence. The intent is documentation, not forecasting.
From Stepzero
The takeaway: this reporting is designed to document distribution behavior over time as context for disciplined analysis. 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. 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-horizon record, this result adds a new point to the dataset to the long-horizon record. Reliability is a function of the growing record.