Megabucks Results
On Monday night, January 6, 2025, the Megabucks draw in Massachusetts brought 02 13 27 30 33 39 back after days away. Given an expected cadence of 1 in 7,059,052 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 6, 2025 in Massachusetts.
Draw times: Evening.
Our take on the Megabucks results
January 6, 2025Megabucks report — Monday night, January 6, 2025: 02 13 27 30 33 39 shows a notable pattern
On Monday night, January 6, 2025, the Megabucks draw in Massachusetts brought 02 13 27 30 33 39 back after days away. Given an expected cadence of 1 in 7,059,052 draws, this interval places the result well beyond typical spacing and makes it a meaningful entry for long-term distribution tracking.
Overview
On Monday night, January 6, 2025, the Megabucks draw in Massachusetts brought 02 13 27 30 33 39 back after days away. Given an expected cadence of 1 in 7,059,052 draws, this interval places the result well beyond typical spacing and makes it a meaningful entry for long-term distribution tracking.
Combo Profile
From a pattern view, 02 13 27 30 33 39 shows 6 distinct numbers while showing no repeats. The numbers span 2 to 39, a wide spread.
Why Droughts Matter
A long drought is descriptive rather than predictive. It records variance across time and helps analysts evaluate whether outcomes are tracking within expected frequency bands or drifting into the tails of the distribution.
Data Notes
This analysis uses the draw results recorded for Monday night, January 6, 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 series is designed to keep the long-horizon record steady as context for disciplined analysis. It is meant to inform, not forecast.
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
From a long-horizon view, this return adds a new point to the dataset to the cumulative record. The accumulation, not any single draw, builds reliability.