Tri-State Megabucks Results
On Saturday night, February 28, 2026, for Vermont's Tri-State Megabucks draw, 14 16 20 22 37 resurfaced after a -day wait in the Vermont record. Given an expected cadence of 1 in 749,398 draws, the interval lands deep in the long-gap tail.
Winning numbers for 1 draw on February 28, 2026 in Vermont.
Draw times: Evening.
Our take on the Tri-State Megabucks results
February 28, 2026Tri-State Megabucks report — Saturday night, February 28, 2026: 14 16 20 22 37 shows a notable pattern
On Saturday night, February 28, 2026, for Vermont's Tri-State Megabucks draw, 14 16 20 22 37 resurfaced after a -day wait in the Vermont record. Given an expected cadence of 1 in 749,398 draws, the interval lands deep in the long-gap tail.
Overview
On Saturday night, February 28, 2026, for Vermont's Tri-State Megabucks draw, 14 16 20 22 37 resurfaced after a -day wait in the Vermont record. Given an expected cadence of 1 in 749,398 draws, the interval lands deep in the long-gap tail.
Combo Profile
As a number pattern, 14 16 20 22 37 uses 5 distinct numbers and a wide spread from 14 to 37.
Why Droughts Matter
Extended gaps are best treated as context, not forward-looking - they record variance across time. Their value is in long-horizon tracking.
Data Notes
This report summarizes observed outcomes for Saturday night, February 28, 2026 and interprets them within the long-run distribution record. It does not imply a forecast or recommendation.
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 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. 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 result adds a new point to the dataset by one more data point. Reliability is a function of the growing record.