Lotto! Results
On Tuesday, September 23, 2025, the Lotto! draw in Connecticut produced a notable return: 01 03 05 06 14 22 after days of absence. Against an expected cadence of 1 in 7,059,052 draws, the gap registers as a clear deviation in timing that merits documentation in the historical record.
Winning numbers for 1 draw on September 23, 2025 in Connecticut.
Draw times: T.
Our take on the Lotto! results
September 23, 2025Lotto! report — Tuesday, September 23, 2025: 01 03 05 06 14 22 shows a notable pattern
On Tuesday, September 23, 2025, the Lotto! draw in Connecticut produced a notable return: 01 03 05 06 14 22 after days of absence. Against an expected cadence of 1 in 7,059,052 draws, the gap registers as a clear deviation in timing that merits documentation in the historical record.
Overview
On Tuesday, September 23, 2025, the Lotto! draw in Connecticut produced a notable return: 01 03 05 06 14 22 after days of absence. Against an expected cadence of 1 in 7,059,052 draws, the gap registers as a clear deviation in timing that merits documentation in the historical record.
Combo Profile
As a number pattern, 01 03 05 06 14 22 uses 6 distinct numbers and a wide spread from 1 to 22.
Why Droughts Matter
Large gaps are best treated as context, not a forecast - they record variance across time. They offer context for distribution stability over time.
Data Notes
This analysis uses the draw results recorded for Tuesday, September 23, 2025 and compares them against the observed historical cadence for the game. This is descriptive, based on frequency tracking - not predictive modeling.
From Stepzero
Stepzero focuses on documenting distribution behavior over large samples. Each report is a snapshot of observed outcomes, designed to support disciplined, long-term analysis.
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
The return of 01 03 05 06 14 22 expands the archive by one more data point. It is the accumulation of these entries, not a single draw, that defines the reliability of long-horizon analysis.