Tri-State Gimme 5 Results
On Thursday night, March 12, 2026, the Tri-State Gimme 5 draw in New Hampshire brought 03 14 16 18 26 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 12, 2026 in New Hampshire.
Draw times: Evening.
Our take on the Tri-State Gimme 5 results
March 12, 2026Tri-State Gimme 5 report — Thursday night, March 12, 2026: 03 14 16 18 26 shows a notable pattern
On Thursday night, March 12, 2026, the Tri-State Gimme 5 draw in New Hampshire brought 03 14 16 18 26 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, March 12, 2026, the Tri-State Gimme 5 draw in New Hampshire brought 03 14 16 18 26 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
As a number pattern, 03 14 16 18 26 uses 5 distinct numbers and a wide spread from 3 to 26.
Why Droughts Matter
Large gaps function as context, not directional - they record variance across time. They make variance visible across extended windows.
Data Notes
Specifically: this analysis records observed outcomes for Thursday night, March 12, 2026 and compares them to historical cadence. The focus is documentation over prediction.
From Stepzero
At Stepzero, the priority is accuracy and context. This report is intended as a historical record entry, not a forecast.
Additional Context
Record-keeping at scale becomes the foundation for analysis. Each outcome, whether typical or unusual, contributes to the stability and clarity of the long-run picture.
Long-horizon measurement matters most when viewed across extended windows. As samples expand, the distribution becomes clearer and anomalies settle into their expected ranges.
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
With its return, 03 14 16 18 26 contributes another meaningful data point to the historical dataset. Each draw - whether routine or statistically unusual - refines the long-term view of how large random systems behave over time.