Tri-State Gimme 5 Results
On Thursday night, February 19, 2026, the Tri-State Gimme 5 draw in New Hampshire marked a notable return: 13 16 27 29 31 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 19, 2026 in New Hampshire.
Draw times: Evening.
Our take on the Tri-State Gimme 5 results
February 19, 2026Tri-State Gimme 5 report — Thursday night, February 19, 2026: 13 16 27 29 31 shows a notable pattern
On Thursday night, February 19, 2026, the Tri-State Gimme 5 draw in New Hampshire marked a notable return: 13 16 27 29 31 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 Thursday night, February 19, 2026, the Tri-State Gimme 5 draw in New Hampshire marked a notable return: 13 16 27 29 31 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 16 27 29 31 uses 5 distinct numbers and a wide spread from 13 to 31.
Why Droughts Matter
Prolonged absences are best treated as context, not predictive - they highlight the tail behavior of the system. They clarify how far outcomes drift from baseline cadence.
Data Notes
The approach: this report documents observed outcomes for Thursday night, February 19, 2026 and compares them to historical cadence. It is context-focused, not predictive.
From Stepzero
At its core: this series is designed to maintain continuity across the record as a reliable record for analysts. The priority is accuracy and continuity.
Additional Context
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
The return of 13 16 27 29 31 expands the archive by one more data point. It is the accumulation of these entries, not a single draw, that defines the reliability of long-horizon analysis.