Mega Millions Results
On Friday night, January 9, 2026, the Mega Millions draw in Maryland brought 12 30 36 42 47 back after days away. Given an expected cadence of 1 in 12,103,014 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 January 9, 2026 in Maryland.
Draw times: Evening.
Our take on the Mega Millions results
January 9, 2026Mega Millions report — Friday night, January 9, 2026: 12 30 36 42 47 shows a notable pattern
On Friday night, January 9, 2026, the Mega Millions draw in Maryland brought 12 30 36 42 47 back after days away. Given an expected cadence of 1 in 12,103,014 draws, this interval places the result well beyond typical spacing and makes it a meaningful entry for long-term distribution tracking.
Overview
On Friday night, January 9, 2026, the Mega Millions draw in Maryland brought 12 30 36 42 47 back after days away. Given an expected cadence of 1 in 12,103,014 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, 12 30 36 42 47 uses 5 distinct numbers and a wide spread from 12 to 47.
Why Droughts Matter
Long droughts are best read as context, not predictive - they document what has already happened. Their value is in long-horizon tracking.
Data Notes
This analysis uses the draw results recorded for Friday night, January 9, 2026 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
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. Record-keeping at scale becomes the foundation for analysis. Each outcome, whether typical or unusual, contributes to the stability and clarity of the long-run picture.
Adding to the Long-Term Record
From a long-horizon view, this return adds one more entry by one more data point. Reliability is a function of the growing record.