Mega Millions Results
On Friday night, February 6, 2026, 13 21 25 52 62 returned following a -day absence for Massachusetts. Against an expected cadence of 1 in 12,103,014 draws, the gap stands out as a long-horizon outlier.
Winning numbers for 1 draw on February 6, 2026 in Massachusetts.
Draw times: Evening.
Our take on the Mega Millions results
February 6, 2026Mega Millions report — Friday night, February 6, 2026: 13 21 25 52 62 shows a notable pattern
On Friday night, February 6, 2026, 13 21 25 52 62 returned following a -day absence for Massachusetts. Against an expected cadence of 1 in 12,103,014 draws, the gap stands out as a long-horizon outlier.
Overview
On Friday night, February 6, 2026, 13 21 25 52 62 returned following a -day absence for Massachusetts. Against an expected cadence of 1 in 12,103,014 draws, the gap stands out as a long-horizon outlier.
Combo Profile
As a number pattern, 13 21 25 52 62 uses 5 distinct numbers and a wide spread from 13 to 62.
Why Droughts Matter
Long gaps function as context, not a cue - they record variance across time. They make variance visible across extended windows.
Data Notes
This report summarizes observed outcomes for Friday night, February 6, 2026 and interprets them within the long-run distribution record. It does not imply a forecast or recommendation.
From Stepzero
At Stepzero, the priority is accuracy and context. This report is intended as a historical record entry, not a forecast.
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 measurement matters most when viewed across extended windows. As samples expand, the distribution becomes clearer and anomalies settle into their expected ranges. 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
From a long-horizon view, this entry adds one more entry to the record. Stability comes from the growing record, not any one draw.