Tri-State Gimme 5 Results
On Monday night, March 30, 2026, the Tri-State Gimme 5 draw in New Hampshire brought 13 17 24 33 38 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 30, 2026 in New Hampshire.
Draw times: Evening.
Our take on the Tri-State Gimme 5 results
March 30, 2026Tri-State Gimme 5 report — Monday night, March 30, 2026: 13 17 24 33 38 shows a notable pattern
On Monday night, March 30, 2026, the Tri-State Gimme 5 draw in New Hampshire brought 13 17 24 33 38 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 Monday night, March 30, 2026, the Tri-State Gimme 5 draw in New Hampshire brought 13 17 24 33 38 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
Beyond the drought, the numbers show a clean structure: 5 distinct numbers with no repeats, spanning 13 to 38 (wide spread).
Why Droughts Matter
Extended gaps are context markers, not a signal - they mark how variance accumulates over long samples. They make variance visible across extended windows.
Data Notes
This analysis uses the draw results recorded for Monday night, March 30, 2026 and compares them against the observed historical cadence for the game. This is descriptive, based on frequency tracking - not predictive modeling.
From Stepzero
Simply put: this reporting is built to maintain continuity across the record as a calm, evidence-first reference. The aim is context, not a call to action.
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
Across the long-horizon record, this draw extends the historical ledger by one more data point. Long-horizon stability comes from accumulation.