Mega Millions Results
On Tuesday night, April 30, 2024, the Mega Millions draw in Wisconsin marked a notable return: 10 18 27 37 61 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 30, 2024 in Wisconsin.
Draw times: Evening.
Our take on the Mega Millions results
April 30, 2024Mega Millions report — Tuesday night, April 30, 2024: 10 18 27 37 61 shows a notable pattern
On Tuesday night, April 30, 2024, the Mega Millions draw in Wisconsin marked a notable return: 10 18 27 37 61 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 30, 2024, the Mega Millions draw in Wisconsin marked a notable return: 10 18 27 37 61 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 shape, the outcome shows 5 distinct numbers with no repeats in the pattern. The numbers run from 10 to 61 with a wide range.
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 documents outcomes documented for Tuesday night, April 30, 2024 and anchors them against historical cadence. The focus is documentation over prediction.
From Stepzero
To be clear: this series is meant to preserve a stable long-horizon record as a reliable record for analysts. The intent is clarity, not prediction.
Additional Context
Context improves with scale. As more draws accumulate, isolated anomalies either normalize into baseline rates or reveal persistent deviations that warrant closer monitoring. 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
In the broader record, this entry adds another archive entry by one more data point. The accumulation, not any single draw, builds reliability.