Cash5 Results
On Wednesday night, May 20, 2026, the Cash5 draw in Connecticut brought 09 15 17 22 24 back after days away. Given an expected cadence of 1 in 324,632 draws, this interval places the result well beyond typical spacing and makes it a meaningful entry for long-term distribution tracking.
Winning numbers for 1 draw on May 20, 2026 in Connecticut.
Draw times: Evening.
Our take on the Cash5 results
May 20, 2026Cash5 report — Wednesday night, May 20, 2026: 09 15 17 22 24 shows a notable pattern
On Wednesday night, May 20, 2026, the Cash5 draw in Connecticut brought 09 15 17 22 24 back after days away. Given an expected cadence of 1 in 324,632 draws, this interval places the result well beyond typical spacing and makes it a meaningful entry for long-term distribution tracking.
Overview
On Wednesday night, May 20, 2026, the Cash5 draw in Connecticut brought 09 15 17 22 24 back after days away. Given an expected cadence of 1 in 324,632 draws, this interval places the result well beyond typical spacing and makes it a meaningful entry for long-term distribution tracking.
Combo Profile
From a number-profile view, the pattern uses 5 distinct numbers with no repeats in the pattern. The numbers run from 9 to 24 with a wide range.
Why Droughts Matter
Extended absences are best read as context, not predictive - they highlight the tail behavior of the system. They help analysts track drift against expected cadence.
Data Notes
In detail: this report documents the draw results for Wednesday night, May 20, 2026 and compares them to historical cadence. This is descriptive, not predictive.
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
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 measurement matters most when viewed across extended windows. As samples expand, the distribution becomes clearer and anomalies settle into their expected ranges.
Adding to the Long-Term Record
The return of 09 15 17 22 24 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.