Tri-State Gimme 5 Results
On Wednesday night, February 11, 2026, the Tri-State Gimme 5 draw in New Hampshire marked a notable return: 16 21 22 24 27 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 February 11, 2026 in New Hampshire.
Draw times: Evening.
Our take on the Tri-State Gimme 5 results
February 11, 2026Tri-State Gimme 5 report — Wednesday night, February 11, 2026: 16 21 22 24 27 shows a notable pattern
On Wednesday night, February 11, 2026, the Tri-State Gimme 5 draw in New Hampshire marked a notable return: 16 21 22 24 27 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 Wednesday night, February 11, 2026, the Tri-State Gimme 5 draw in New Hampshire marked a notable return: 16 21 22 24 27 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
The numbers in 16 21 22 24 27 cover a wide range (16 to 27) with no repeats.
Why Droughts Matter
Prolonged absences remain descriptive, not predictive - they document what has already happened. They provide a clean read on long-run variance.
Data Notes
The approach: this report captures outcomes documented for Wednesday night, February 11, 2026 and anchors them against historical cadence. This is descriptive, not predictive.
From Stepzero
To be clear: this series is meant to maintain continuity across the record as a calm, evidence-first reference. The goal is clarity and stability.
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.
Context improves with scale. As more draws accumulate, isolated anomalies either normalize into baseline rates or reveal persistent deviations that warrant closer monitoring.
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
From a long-horizon view, this appearance adds another data point to the archive. Long-horizon stability comes from accumulation.