Mega Millions Results
On Tuesday night, November 11, 2025, the Mega Millions draw in Vermont brought 10 13 40 42 46 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 November 11, 2025 in Vermont.
Draw times: Evening.
Our take on the Mega Millions results
November 11, 2025Mega Millions report — Tuesday night, November 11, 2025: 10 13 40 42 46 shows a notable pattern
On Tuesday night, November 11, 2025, the Mega Millions draw in Vermont brought 10 13 40 42 46 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, November 11, 2025, the Mega Millions draw in Vermont brought 10 13 40 42 46 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
In terms of number structure, the outcome contains 5 distinct numbers with no repeats in the numbers. The range from 10 to 46 is a wide spread.
Why Droughts Matter
Extended absences like this provide context, not direction. They show how randomness behaves across large samples and help analysts quantify how often the system deviates from its baseline cadence.
Data Notes
To clarify: this report records the results logged for Tuesday night, November 11, 2025 and benchmarks them against historical frequency baselines. This is documentation, not a forecast.
From Stepzero
Stepzero produces these reports to provide a calm, evidence-first record of how draw patterns unfold over time. The aim is clarity and continuity - a reference point for long-horizon tracking rather than a call to action.
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 appearance extends the historical ledger to the historical dataset. The long-run picture sharpens as entries accrue.