Mega Millions Results
On Tuesday night, November 25, 2025, the Mega Millions draw in Connecticut brought 11 15 31 32 59 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 25, 2025 in Connecticut.
Draw times: Evening.
Our take on the Mega Millions results
November 25, 2025Mega Millions report — Tuesday night, November 25, 2025: 11 15 31 32 59 shows a notable pattern
On Tuesday night, November 25, 2025, the Mega Millions draw in Connecticut brought 11 15 31 32 59 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 25, 2025, the Mega Millions draw in Connecticut brought 11 15 31 32 59 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
The numbers in 11 15 31 32 59 cover a wide range (11 to 59) 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 analysis uses the draw results recorded for Tuesday night, November 25, 2025 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
Stability comes from the accumulation of entries. One draw alone does not define the pattern, but the record grows more reliable with each addition to the dataset. 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, this entry adds a fresh entry to the record to the archive. The accumulation, not any single draw, builds reliability.