Mega Millions Results
On Tuesday night, May 6, 2025, the Mega Millions draw in West Virginia brought 16 17 43 46 58 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 May 6, 2025 in West Virginia.
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 West Virginia brought 16 17 43 46 58 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 Tuesday night, May 6, 2025, the Mega Millions draw in West Virginia brought 16 17 43 46 58 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 shape, the outcome uses 5 distinct numbers with no repeats noted. Its range is 16 to 58 with 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
Results are evaluated against historical frequency baselines where available. The goal is documentation and context rather than prediction.
From Stepzero
At Stepzero, the priority is accuracy and context. This report is intended as a historical record entry, not a 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
Across the long-term record, today's outcome adds one more entry to the long-run dataset. Reliability is a function of the growing record.