Match 6 Results
On Sunday night, May 31, 2026, the Match 6 draw in Pennsylvania brought 05 27 32 38 42 46 back after days away. Given an expected cadence of 1 in 13,983,816 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 31, 2026 in Pennsylvania.
Draw times: Evening.
Our take on the Match 6 results
May 31, 2026Match 6 report — Sunday night, May 31, 2026: 05 27 32 38 42 46 shows a notable pattern
On Sunday night, May 31, 2026, the Match 6 draw in Pennsylvania brought 05 27 32 38 42 46 back after days away. Given an expected cadence of 1 in 13,983,816 draws, this interval places the result well beyond typical spacing and makes it a meaningful entry for long-term distribution tracking.
Overview
On Sunday night, May 31, 2026, the Match 6 draw in Pennsylvania brought 05 27 32 38 42 46 back after days away. Given an expected cadence of 1 in 13,983,816 draws, this interval places the result well beyond typical spacing and makes it a meaningful entry for long-term distribution tracking.
Combo Profile
The numbers in 05 27 32 38 42 46 cover a wide range (5 to 46) with no repeats.
Why Droughts Matter
Extended gaps are best read as context, not a forecast - they record variance across time. They make variance visible across extended windows.
Data Notes
Results are evaluated against historical frequency baselines where available. The goal is documentation and context rather than prediction.
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 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.
Distribution analysis depends on consistent documentation. Each draw updates the record, allowing analysts to test whether deviations persist, reverse, or revert to expected ranges.
Adding to the Long-Term Record
This result adds a measurable entry to the long-term record. Over time, those entries are what sharpen distribution analysis and reveal whether the system is tracking its expected cadence.