Match 6 Results
On Thursday night, November 13, 2025, the Match 6 draw in Pennsylvania brought 02 12 16 22 25 27 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 November 13, 2025 in Pennsylvania.
Draw times: Evening.
Our take on the Match 6 results
November 13, 2025Match 6 report — Thursday night, November 13, 2025: 02 12 16 22 25 27 shows a notable pattern
On Thursday night, November 13, 2025, the Match 6 draw in Pennsylvania brought 02 12 16 22 25 27 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 Thursday night, November 13, 2025, the Match 6 draw in Pennsylvania brought 02 12 16 22 25 27 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
As a number pattern, 02 12 16 22 25 27 uses 6 distinct numbers and a wide spread from 2 to 27.
Why Droughts Matter
Deep gaps are best treated as context, not a cue - they highlight the tail behavior of the system. Their value is in long-horizon tracking.
Data Notes
This analysis uses the draw results recorded for Thursday night, November 13, 2025 and compares them against the observed historical cadence for the game. This is descriptive, based on frequency tracking - not predictive modeling.
From Stepzero
The takeaway: this reporting is built to keep the long-horizon record steady as a stable reference point. The focus is long-horizon context.
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
Across the long-horizon record, this result adds another archive entry to the record. Long-horizon stability comes from accumulation.