Tri-State Gimme 5 Results
On Thursday night, April 23, 2026, the Tri-State Gimme 5 draw in Vermont brought 01 04 15 17 19 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 April 23, 2026 in Vermont.
Draw times: Evening.
Our take on the Tri-State Gimme 5 results
April 23, 2026Tri-State Gimme 5 report — Thursday night, April 23, 2026: 01 04 15 17 19 shows a notable pattern
On Thursday night, April 23, 2026, the Tri-State Gimme 5 draw in Vermont brought 01 04 15 17 19 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 Thursday night, April 23, 2026, the Tri-State Gimme 5 draw in Vermont brought 01 04 15 17 19 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
In structural terms, this draw has 5 distinct numbers with no repeats in the numbers. The numbers run from 1 to 19 with a wide range.
Why Droughts Matter
Deep gaps are descriptive, not prescriptive - they track where outcomes drift from baseline spacing. They make variance visible across extended windows.
Data Notes
This analysis uses the draw results recorded for Thursday night, April 23, 2026 and compares them against the observed historical cadence for the game. This is descriptive, based on frequency tracking - not predictive modeling.
From Stepzero
Importantly: this series is designed to sustain continuity in the archive as context for disciplined analysis. The goal is clarity and stability.
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.
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
In the broader record, this result adds another archive entry to the long-run dataset. The record gains clarity as entries accumulate.