Mega Millions Results
On Friday night, November 11, 2022, the Mega Millions draw in Wisconsin marked a notable return: 01 05 17 37 70 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 11, 2022 in Wisconsin.
Draw times: Evening.
Our take on the Mega Millions results
November 11, 2022Mega Millions report — Friday night, November 11, 2022: 01 05 17 37 70 shows a notable pattern
On Friday night, November 11, 2022, the Mega Millions draw in Wisconsin marked a notable return: 01 05 17 37 70 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 11, 2022, the Mega Millions draw in Wisconsin marked a notable return: 01 05 17 37 70 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, 01 05 17 37 70 uses 5 distinct numbers and a wide spread from 1 to 70.
Why Droughts Matter
Prolonged absences function as context, not a cue - they document what has already happened. They offer context for distribution stability over time.
Data Notes
This analysis uses the draw results recorded for Friday night, November 11, 2022 and compares them against the observed historical cadence for the game. This is descriptive, based on frequency tracking - not predictive modeling.
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
Context improves with scale. As more draws accumulate, isolated anomalies either normalize into baseline rates or reveal persistent deviations that warrant closer monitoring. 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.