Mega Millions Results
On Tuesday night, May 6, 2025, the Mega Millions draw in Massachusetts marked a notable return: 16 17 43 46 58 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 May 6, 2025 in Massachusetts.
Draw times: Evening.
Our take on the Mega Millions results
May 6, 2025Mega Millions report — Tuesday night, May 6, 2025: 16 17 43 46 58 shows a notable pattern
On Tuesday night, May 6, 2025, the Mega Millions draw in Massachusetts marked a notable return: 16 17 43 46 58 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 Tuesday night, May 6, 2025, the Mega Millions draw in Massachusetts marked a notable return: 16 17 43 46 58 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
The numbers in 16 17 43 46 58 cover a wide range (16 to 58) with no repeats.
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 report summarizes observed outcomes for Tuesday night, May 6, 2025 and interprets them within the long-run distribution record. It does not imply a forecast or recommendation.
From Stepzero
At its core: this reporting is shaped to keep the record consistent over time as a record, not a recommendation. It is meant to inform, not forecast.
Additional Context
Distribution analysis depends on consistent documentation. Each draw updates the record, allowing analysts to test whether deviations persist, reverse, or revert to 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
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.