Mega Millions Results
On Tuesday night, April 1, 2025, the Mega Millions draw in Connecticut marked a notable return: 11 12 21 29 49 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 April 1, 2025 in Connecticut.
Draw times: Evening.
Our take on the Mega Millions results
April 1, 2025Mega Millions report — Tuesday night, April 1, 2025: 11 12 21 29 49 shows a notable pattern
On Tuesday night, April 1, 2025, the Mega Millions draw in Connecticut marked a notable return: 11 12 21 29 49 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, April 1, 2025, the Mega Millions draw in Connecticut marked a notable return: 11 12 21 29 49 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
As a number pattern, 11 12 21 29 49 uses 5 distinct numbers and a wide spread from 11 to 49.
Why Droughts Matter
Extended absences function as context, not prescriptive - they show where spacing departs from typical cadence. They make variance visible across extended windows.
Data Notes
This analysis uses the draw results recorded for Tuesday night, April 1, 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 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
Record-keeping at scale becomes the foundation for analysis. Each outcome, whether typical or unusual, contributes to the stability and clarity of the long-run picture. 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.