Tri-State Pick 4 Results
On Thursday midday, January 22, 2026, the Tri-State Pick 4 draw in Vermont produced a notable return: 5112 after days of absence. Against an expected cadence of 1 in 10,000 draws, the gap registers as a clear deviation in timing that merits documentation in the historical record.
Winning numbers for 2 draws on January 22, 2026 in Vermont.
Draw times: Evening, Midday.
Our take on the Tri-State Pick 4 results
January 22, 2026Tri-State Pick 4 report — Thursday midday, January 22, 2026: 5112 shows a notable pattern
On Thursday midday, January 22, 2026, the Tri-State Pick 4 draw in Vermont produced a notable return: 5112 after days of absence. Against an expected cadence of 1 in 10,000 draws, the gap registers as a clear deviation in timing that merits documentation in the historical record.
Overview
On Thursday midday, January 22, 2026, the Tri-State Pick 4 draw in Vermont produced a notable return: 5112 after days of absence. Against an expected cadence of 1 in 10,000 draws, the gap registers as a clear deviation in timing that merits documentation in the historical record.
Combo Profile
Beyond the drought, the digits show a clean structure: 3 distinct digits with a repeated digit, spanning 1 to 5 (moderate spread).
Why Droughts Matter
Droughts do not indicate what will happen next - they simply document what has already occurred. Their value lies in measuring distribution over long horizons and identifying when a combination performs far above or below its expected appearance rate.
Data Notes
To clarify: this analysis records the results logged for Thursday midday, January 22, 2026 and evaluates them against long-run frequency baselines. This is documentation, not a forecast.
From Stepzero
Simply put: this series is designed to maintain continuity across the record as a stable reference point. The intent is clarity, not prediction.
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. 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
From a long-horizon view, this appearance adds a new point to the dataset to the historical dataset. Stability comes from the growing record, not any one draw.