Treasure Hunt Results
On Saturday midday, May 9, 2026, the Treasure Hunt draw in Pennsylvania marked a notable return: 11 13 21 23 30 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 142,506 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Winning numbers for 1 draw on May 9, 2026 in Pennsylvania.
Draw times: Day.
Our take on the Treasure Hunt results
May 9, 2026Treasure Hunt report — Saturday midday, May 9, 2026: 11 13 21 23 30 shows a notable pattern
On Saturday midday, May 9, 2026, the Treasure Hunt draw in Pennsylvania marked a notable return: 11 13 21 23 30 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 142,506 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Overview
On Saturday midday, May 9, 2026, the Treasure Hunt draw in Pennsylvania marked a notable return: 11 13 21 23 30 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 142,506 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Combo Profile
As a number pattern, 11 13 21 23 30 uses 5 distinct numbers and a wide spread from 11 to 30.
Why Droughts Matter
Prolonged absences function as context, not a forecast - they mark how variance accumulates over long samples. They clarify how far outcomes drift from baseline cadence.
Data Notes
This analysis uses the draw results recorded for Saturday midday, May 9, 2026 and compares them against the observed historical cadence for the game. This is descriptive, based on frequency tracking - not predictive modeling.
From Stepzero
At Stepzero, the priority is accuracy and context. This report is intended as a historical record entry, not a forecast.
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.
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 long-horizon tracking, this appearance adds a fresh entry to the record to the historical dataset. The record gains clarity as entries accumulate.