yes San Diego,yes New York Y,yes Milwaukee,yes Boston
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 04:43 UTC | View → |
| Kalshi | — | — | — | 10:03 UTC | View → |
| Kalshi | — | — | — | 11:32 UTC | View → |
| Kalshi | — | — | — | 14:48 UTC | View → |
| Kalshi | — | — | — | 05:32 UTC | View → |
| Kalshi | — | — | — | 11:20 UTC | View → |
| Kalshi | — | — | — | 10:27 UTC | View → |
| Kalshi | — | — | — | 14:34 UTC | View → |
| Kalshi | — | — | — | 06:01 UTC | View → |
| Kalshi | — | — | — | 11:20 UTC | View → |
| Kalshi | — | — | — | 09:39 UTC | View → |
| Kalshi | — | — | — | 10:27 UTC | View → |
| Kalshi | — | — | — | 10:03 UTC | View → |
| Kalshi | — | — | — | 10:27 UTC | View → |
| Kalshi | — | — | — | 09:13 UTC | View → |
| Kalshi | — | — | — | 05:42 UTC | View → |
| Kalshi | — | — | — | 19:45 UTC | View → |
| Kalshi | — | — | — | 10:08 UTC | View → |
| Kalshi | — | — | — | 10:06 UTC | View → |
| Kalshi | — | — | — | 10:27 UTC | View → |
| Kalshi | — | — | — | 15:20 UTC | View → |
| Kalshi | — | — | — | 22:02 UTC | View → |
| Kalshi | — | — | — | 04:26 UTC | View → |
| Kalshi | — | — | — | 11:20 UTC | View → |
| Kalshi | — | — | — | 05:32 UTC | View → |
| Kalshi | — | — | — | 12:27 UTC | View → |
| Kalshi | — | — | — | 02:23 UTC | View → |
| Kalshi | — | — | — | 11:20 UTC | View → |
| Kalshi | — | — | — | 10:03 UTC | View → |
| Kalshi | — | — | — | 13:14 UTC | View → |
| Kalshi | — | — | — | 09:39 UTC | View → |
| Kalshi | — | — | — | 10:28 UTC | View → |
| Kalshi | — | — | — | 12:27 UTC | View → |
| Kalshi | — | — | — | 10:07 UTC | View → |
| Kalshi | — | — | — | 04:28 UTC | View → |
| Kalshi | — | — | — | 17:40 UTC | View → |
| Kalshi | — | — | — | 21:47 UTC | View → |
| Kalshi | — | — | — | 09:35 UTC | View → |
| Kalshi | — | — | — | 13:13 UTC | View → |
| Kalshi | — | — | — | 23:56 UTC | View → |
| Kalshi | — | — | — | 21:32 UTC | View → |
| Kalshi | — | — | — | 10:59 UTC | View → |
| Kalshi | — | — | — | 14:25 UTC | View → |
| Kalshi | — | — | — | 14:25 UTC | View → |
| Kalshi | — | — | — | 05:03 UTC | View → |
| Kalshi | — | — | — | 05:03 UTC | View → |
| Kalshi | — | — | — | 20:31 UTC | View → |
| Kalshi | — | — | — | 05:10 UTC | View → |
| Kalshi | — | — | — | 22:27 UTC | View → |
| Kalshi | — | — | — | 14:19 UTC | View → |
| Kalshi | — | — | — | 09:59 UTC | View → |
| Kalshi | — | — | — | 20:45 UTC | View → |
| Kalshi | — | — | — | 10:59 UTC | View → |
| Kalshi | — | — | — | 13:24 UTC | View → |
| Kalshi | — | — | — | 14:22 UTC | View → |
| Kalshi | — | — | — | 21:36 UTC | View → |
| Kalshi | — | — | — | 00:25 UTC | View → |
| Kalshi | — | — | — | 22:58 UTC | View → |
| Kalshi | — | — | — | 08:24 UTC | View → |
| Kalshi | — | — | — | 12:54 UTC | View → |
| Kalshi | — | — | — | 15:07 UTC | View → |
| Kalshi | — | — | — | 19:41 UTC | View → |
| Kalshi | — | — | — | 00:25 UTC | View → |
| Kalshi | — | — | — | 05:03 UTC | View → |
| Kalshi | — | — | — | 19:41 UTC | View → |
| Kalshi | — | — | — | 13:00 UTC | View → |
| Kalshi | — | — | — | 13:00 UTC | View → |
| Kalshi | — | — | — | 05:02 UTC | View → |
| Kalshi | — | — | — | 23:52 UTC | View → |
| Kalshi | — | — | — | 08:17 UTC | View → |
| Kalshi | — | — | — | 05:31 UTC | View → |
| Kalshi | — | — | — | 09:29 UTC | View → |
| Kalshi | — | — | — | 20:39 UTC | View → |
| Kalshi | — | — | — | 10:59 UTC | View → |
| Kalshi | — | — | — | 10:59 UTC | View → |
| Kalshi | — | — | — | 12:53 UTC | View → |
| Kalshi | — | — | — | 12:25 UTC | View → |
| Kalshi | — | — | — | 20:44 UTC | View → |
| Kalshi | — | — | — | 11:01 UTC | View → |
| Kalshi | — | — | — | 12:50 UTC | View → |
| Kalshi | — | — | — | 13:01 UTC | View → |
| Kalshi | — | — | — | 12:58 UTC | View → |
What do these odds mean?
Cross-platform data for yes San Diego,yes New York Y,yes Milwaukee,yes Boston 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.