Tri-State Gimme 5 Results
On Friday night, January 23, 2026, the Tri-State Gimme 5 draw in Vermont marked a notable return: 04 05 13 26 32 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 January 23, 2026 in Vermont.
Draw times: Evening.
Our take on the Tri-State Gimme 5 results
January 23, 2026Tri-State Gimme 5 report — Friday night, January 23, 2026: 04 05 13 26 32 shows a notable pattern
On Friday night, January 23, 2026, the Tri-State Gimme 5 draw in Vermont marked a notable return: 04 05 13 26 32 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, January 23, 2026, the Tri-State Gimme 5 draw in Vermont marked a notable return: 04 05 13 26 32 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
Structurally, this sequence has 5 distinct numbers with no repeats in the pattern. The range sits at 4 to 32, a wide spread.
Why Droughts Matter
Long droughts are best treated as context, not predictive - they mark how variance accumulates over long samples. They help quantify how often outcomes move into the tails.
Data Notes
This analysis uses the draw results recorded for Friday night, January 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: these reports are intended to keep the long-horizon record steady 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
Over the broader record, this entry adds another data point to the historical dataset. Stability comes from the growing record, not any one draw.