Match 6 Results
On Sunday night, May 17, 2026, the Match 6 draw in Pennsylvania marked a notable return: 07 22 27 38 40 45 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 13,983,816 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Winning numbers for 1 draw on May 17, 2026 in Pennsylvania.
Draw times: Evening.
Our take on the Match 6 results
May 17, 2026Match 6 report — Sunday night, May 17, 2026: 07 22 27 38 40 45 shows a notable pattern
On Sunday night, May 17, 2026, the Match 6 draw in Pennsylvania marked a notable return: 07 22 27 38 40 45 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 13,983,816 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Overview
On Sunday night, May 17, 2026, the Match 6 draw in Pennsylvania marked a notable return: 07 22 27 38 40 45 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 13,983,816 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Combo Profile
As a number pattern, 07 22 27 38 40 45 uses 6 distinct numbers and a wide spread from 7 to 45.
Why Droughts Matter
Large gaps are context, not forward-looking - they track where outcomes drift from baseline spacing. They help quantify how often outcomes move into the tails.
Data Notes
This report summarizes observed outcomes for Sunday night, May 17, 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
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 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
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.