Mega Millions Results
On Friday night, November 28, 2025, the Mega Millions draw in Arizona marked a notable return: 06 07 13 39 48 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 12,103,014 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Winning numbers for 1 draw on November 28, 2025 in Arizona.
Draw times: Evening.
Our take on the Mega Millions results
November 28, 2025Mega Millions report — Friday night, November 28, 2025: 06 07 13 39 48 shows a notable pattern
On Friday night, November 28, 2025, the Mega Millions draw in Arizona marked a notable return: 06 07 13 39 48 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 12,103,014 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Overview
On Friday night, November 28, 2025, the Mega Millions draw in Arizona marked a notable return: 06 07 13 39 48 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 12,103,014 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Combo Profile
As a number pattern, 06 07 13 39 48 uses 5 distinct numbers and a wide spread from 6 to 48.
Why Droughts Matter
Deep gaps are context, not predictive - they record variance across time. They help analysts track drift against expected cadence.
Data Notes
This analysis uses the draw results recorded for Friday night, November 28, 2025 and compares them against the observed historical cadence for the game. This is descriptive, based on frequency tracking - not predictive modeling.
From Stepzero
Simply put: this series is meant to preserve a stable long-horizon record as a reliable record for analysts. The goal is clarity and stability.
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
Over the long run, this appearance adds one more entry to the archive. Reliability is a function of the growing record.