yes Payton Pritchard: 1+,yes Payton Pritchard: 2+,yes Boston,yes Neemias Queta: 10+,yes Payton Pritchard: 10+,yes Over 224.5 points scored
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 17:57 UTC | View → |
| Kalshi | — | — | — | 19:09 UTC | View → |
| Kalshi | — | — | — | 21:42 UTC | View → |
| Kalshi | — | — | — | 20:26 UTC | View → |
| Kalshi | — | — | — | 17:06 UTC | View → |
| Kalshi | — | — | — | 23:18 UTC | View → |
| Kalshi | — | — | — | 18:50 UTC | View → |
| Kalshi | — | — | — | 18:06 UTC | View → |
| Kalshi | — | — | — | 19:05 UTC | View → |
| Kalshi | — | — | — | 18:06 UTC | View → |
| Kalshi | — | — | — | 18:05 UTC | View → |
| Kalshi | — | — | — | 19:49 UTC | View → |
| Kalshi | — | — | — | 22:57 UTC | View → |
| Kalshi | — | — | — | 22:27 UTC | View → |
| Kalshi | — | — | — | 19:07 UTC | View → |
| Kalshi | — | — | — | 23:20 UTC | View → |
| Kalshi | — | — | — | 22:54 UTC | View → |
| Kalshi | — | — | — | 23:30 UTC | View → |
| Kalshi | — | — | — | 17:16 UTC | View → |
| Kalshi | — | — | — | 18:26 UTC | View → |
| Kalshi | — | — | — | 21:40 UTC | View → |
| Kalshi | — | — | — | 17:03 UTC | View → |
| Kalshi | — | — | — | 17:20 UTC | View → |
| Kalshi | — | — | — | 21:57 UTC | View → |
| Kalshi | — | — | — | 17:31 UTC | View → |
| Kalshi | — | — | — | 19:05 UTC | View → |
| Kalshi | — | — | — | 19:29 UTC | View → |
| Kalshi | — | — | — | 19:09 UTC | View → |
| Kalshi | — | — | — | 19:24 UTC | View → |
| Kalshi | — | — | — | 21:42 UTC | View → |
| Kalshi | — | — | — | 18:37 UTC | View → |
| Kalshi | — | — | — | 20:27 UTC | View → |
| Kalshi | — | — | — | 18:06 UTC | View → |
| Kalshi | — | — | — | 22:40 UTC | View → |
| Kalshi | — | — | — | 17:03 UTC | View → |
| Kalshi | — | — | — | 22:10 UTC | View → |
| Kalshi | — | — | — | 21:12 UTC | View → |
| Kalshi | — | — | — | 20:15 UTC | View → |
| Kalshi | — | — | — | 21:17 UTC | View → |
| Kalshi | — | — | — | 20:38 UTC | View → |
| Kalshi | — | — | — | 18:11 UTC | View → |
| Kalshi | — | — | — | 23:53 UTC | View → |
| Kalshi | — | — | — | 20:06 UTC | View → |
| Kalshi | — | — | — | 22:23 UTC | View → |
| Kalshi | — | — | — | 18:41 UTC | View → |
| Kalshi | — | — | — | 19:55 UTC | View → |
| Kalshi | — | — | — | 20:34 UTC | View → |
| Kalshi | — | — | — | 17:31 UTC | View → |
| Kalshi | — | — | — | 17:07 UTC | View → |
| Kalshi | — | — | — | 23:31 UTC | View → |
| Kalshi | — | — | — | 18:27 UTC | View → |
| Kalshi | — | — | — | 21:24 UTC | View → |
| Kalshi | — | — | — | 21:37 UTC | View → |
| Kalshi | — | — | — | 19:19 UTC | View → |
| Kalshi | — | — | — | 20:23 UTC | View → |
| Kalshi | — | — | — | 21:27 UTC | View → |
| Kalshi | — | — | — | 23:29 UTC | View → |
| Kalshi | — | — | — | 20:32 UTC | View → |
| Kalshi | — | — | — | 20:32 UTC | View → |
| Kalshi | — | — | — | 20:35 UTC | View → |
| Kalshi | — | — | — | 17:29 UTC | View → |
| Kalshi | — | — | — | 21:38 UTC | View → |
| Kalshi | — | — | — | 23:11 UTC | View → |
| Kalshi | — | — | — | 17:09 UTC | View → |
| Kalshi | — | — | — | 22:47 UTC | View → |
| Kalshi | — | — | — | 17:29 UTC | View → |
| Kalshi | — | — | — | 21:11 UTC | View → |
What do these odds mean?
Cross-platform data for yes Payton Pritchard: 1+,yes Payton Pritchard: 2+,yes Boston,yes Neemias Queta: 10+,yes Payton Pritchard: 10+,yes Over 224.5 points scored is still being collected.
How to read cross-platform spreads
When two platforms price the same event meaningfully differently, it usually means one of three things: liquidity is thin on one side, fee structures are pushing a spread, or traders on one platform have information the other lacks. Spreads larger than 5 percentage points on events with over $50K in volume often resolve toward the higher-volume platform's price.
About this data
Beeks.ai aggregates prediction market data from Polymarket, Kalshi, and Manifold. Updates run every minute. Consensus probability is a volume-weighted average across all matched markets. Historical snapshots are stored for calibration analysis.
News & context
Your edge calculator
Enter your own probability estimate to see expected value and recommended position size using the Kelly Criterion.