Match 4 Results
On Monday night, June 16, 2025, the Match 4 draw in Washington marked a notable return: 01 05 10 11 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 10,626 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Winning numbers for 1 draw on June 16, 2025 in Washington.
Draw times: Evening.
Our take on the Match 4 results
June 16, 2025Match 4 report — Monday night, June 16, 2025: 01 05 10 11 shows a notable pattern
On Monday night, June 16, 2025, the Match 4 draw in Washington marked a notable return: 01 05 10 11 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 10,626 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Overview
On Monday night, June 16, 2025, the Match 4 draw in Washington marked a notable return: 01 05 10 11 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 10,626 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Combo Profile
Structurally, this sequence settles on 4 distinct numbers while showing no repeats. The spread runs 1 to 11 (wide).
Why Droughts Matter
Long droughts are best read as context, not a signal - they show where spacing departs from typical cadence. They offer context for distribution stability over time.
Data Notes
Worth noting: this analysis summarizes the recorded draws for Monday night, June 16, 2025 and evaluates them against long-run frequency baselines. It is intended for context, not forecasting.
From Stepzero
The takeaway: these reports are intended to sustain continuity in the archive for analysts and long-run tracking. The aim is a trustworthy record.
Additional Context
Distribution analysis depends on consistent documentation. Each draw updates the record, allowing analysts to test whether deviations persist, reverse, or revert to 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
With its return, 01 05 10 11 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.