Tri-State Gimme 5 Results
On Friday night, November 28, 2025, the Tri-State Gimme 5 draw in Vermont produced a notable return: 06 23 25 29 31 after days of absence. Against an expected cadence of 1 in 575,757 draws, the gap registers as a clear deviation in timing that merits documentation in the historical record.
Winning numbers for 1 draw on November 28, 2025 in Vermont.
Draw times: Evening.
Our take on the Tri-State Gimme 5 results
November 28, 2025Tri-State Gimme 5 report — Friday night, November 28, 2025: 06 23 25 29 31 shows a notable pattern
On Friday night, November 28, 2025, the Tri-State Gimme 5 draw in Vermont produced a notable return: 06 23 25 29 31 after days of absence. Against an expected cadence of 1 in 575,757 draws, the gap registers as a clear deviation in timing that merits documentation in the historical record.
Overview
On Friday night, November 28, 2025, the Tri-State Gimme 5 draw in Vermont produced a notable return: 06 23 25 29 31 after days of absence. Against an expected cadence of 1 in 575,757 draws, the gap registers as a clear deviation in timing that merits documentation in the historical record.
Combo Profile
As a number pattern, 06 23 25 29 31 uses 5 distinct numbers and a wide spread from 6 to 31.
Why Droughts Matter
Long gaps remain descriptive, not a cue - they show where spacing departs from typical cadence. They provide a clean read on long-run variance.
Data Notes
Results are evaluated against historical frequency baselines where available. The goal is documentation and context rather than 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
Context improves with scale. As more draws accumulate, isolated anomalies either normalize into baseline rates or reveal persistent deviations that warrant closer monitoring.
Distribution analysis depends on consistent documentation. Each draw updates the record, allowing analysts to test whether deviations persist, reverse, or revert to 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
In the broader record, this appearance adds one more entry to the record. Stability comes from the growing record, not any one draw.