Tri-State Gimme 5 Results
On Monday night, March 9, 2026 in New Hampshire, 10 13 24 31 33 came back after days out of the results in the New Hampshire record. By the expected cadence of 1 in 575,757 draws, the interval is a long-gap event.
Winning numbers for 1 draw on March 9, 2026 in New Hampshire.
Draw times: Evening.
Our take on the Tri-State Gimme 5 results
March 9, 2026Tri-State Gimme 5 report — Monday night, March 9, 2026: 10 13 24 31 33 shows a notable pattern
On Monday night, March 9, 2026 in New Hampshire, 10 13 24 31 33 came back after days out of the results in the New Hampshire record. By the expected cadence of 1 in 575,757 draws, the interval is a long-gap event.
Overview
On Monday night, March 9, 2026 in New Hampshire, 10 13 24 31 33 came back after days out of the results in the New Hampshire record. By the expected cadence of 1 in 575,757 draws, the interval is a long-gap event.
Combo Profile
The numbers in 10 13 24 31 33 cover a wide range (10 to 33) with no repeats.
Why Droughts Matter
Extended absences are context, not predictive - they show where spacing departs from typical cadence. They clarify how far outcomes drift from baseline cadence.
Data Notes
To clarify: this analysis documents the results logged for Monday night, March 9, 2026 and evaluates them against long-run frequency baselines. The goal is context, not prediction.
From Stepzero
Stepzero produces these reports to provide a calm, evidence-first record of how draw patterns unfold over time. The aim is clarity and continuity - a reference point for long-horizon tracking rather than a call to action.
Additional Context
Long-horizon measurement matters most when viewed across extended windows. As samples expand, the distribution becomes clearer and anomalies settle into their expected ranges.
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
In the broader record, 10 13 24 31 33 adds one more entry to the long-run dataset. The record gains clarity as entries accumulate.