Multi-Match Results
On Monday night, May 13, 2024, the Multi-Match draw in Maryland produced a notable return: 04 17 20 21 26 35 after days of absence. Against an expected cadence of 1 in 6,096,454 draws, the gap registers as a clear deviation in timing that merits documentation in the historical record.
Winning numbers for 1 draw on May 13, 2024 in Maryland.
Draw times: Evening.
Our take on the Multi-Match results
May 13, 2024Multi-Match report — Monday night, May 13, 2024: 04 17 20 21 26 35 shows a notable pattern
On Monday night, May 13, 2024, the Multi-Match draw in Maryland produced a notable return: 04 17 20 21 26 35 after days of absence. Against an expected cadence of 1 in 6,096,454 draws, the gap registers as a clear deviation in timing that merits documentation in the historical record.
Overview
On Monday night, May 13, 2024, the Multi-Match draw in Maryland produced a notable return: 04 17 20 21 26 35 after days of absence. Against an expected cadence of 1 in 6,096,454 draws, the gap registers as a clear deviation in timing that merits documentation in the historical record.
Combo Profile
The numbers in 04 17 20 21 26 35 cover a wide range (4 to 35) with no repeats.
Why Droughts Matter
Deep gaps are context, not directional - they document what has already happened. Their value is in long-horizon tracking.
Data Notes
This analysis uses the draw results recorded for Monday night, May 13, 2024 and compares them against the observed historical cadence for the game. This is descriptive, based on frequency tracking - not predictive modeling.
From Stepzero
The takeaway: these reports are built to keep the long-horizon record steady as a calm, evidence-first reference. The aim is context, not a call to action.
Additional Context
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. 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
With its return, 04 17 20 21 26 35 contributes another meaningful data point to the historical dataset. Each draw - whether routine or statistically unusual - refines the long-term view of how large random systems behave over time.