Match 6 Results
On Friday night, January 9, 2026, the Match 6 draw in Pennsylvania marked a notable return: 01 05 13 18 28 30 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 January 9, 2026 in Pennsylvania.
Draw times: Evening.
Our take on the Match 6 results
January 9, 2026Match 6 report — Friday night, January 9, 2026: 01 05 13 18 28 30 shows a notable pattern
On Friday night, January 9, 2026, the Match 6 draw in Pennsylvania marked a notable return: 01 05 13 18 28 30 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 Friday night, January 9, 2026, the Match 6 draw in Pennsylvania marked a notable return: 01 05 13 18 28 30 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
From a number profile angle, the pattern contains 6 distinct numbers with no repeats noted. The numbers run from 1 to 30 with a wide range.
Why Droughts Matter
Extended gaps are descriptive, not predictive - they track where outcomes drift from baseline spacing. Their value is in long-horizon tracking.
Data Notes
The approach: this report summarizes outcomes logged on Friday night, January 9, 2026 with comparison to long-run frequency baselines. The goal is context, not prediction.
From Stepzero
To be clear: this reporting is designed to document distribution behavior over time for analysts and long-run tracking. The aim is a trustworthy record.
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
The return of 01 05 13 18 28 30 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.