yes Detroit,yes Miami,yes Orlando,yes Philadelphia,yes New York
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 01:04 UTC | View → |
| Kalshi | — | — | — | 14:48 UTC | View → |
| Kalshi | — | — | — | 08:32 UTC | View → |
| Kalshi | — | — | — | 07:28 UTC | View → |
| Kalshi | — | — | — | 01:06 UTC | View → |
| Kalshi | — | — | — | 18:00 UTC | View → |
| Kalshi | — | — | — | 13:19 UTC | View → |
| Kalshi | — | — | — | 20:37 UTC | View → |
| Kalshi | — | — | — | 19:17 UTC | View → |
| Kalshi | — | — | — | 09:15 UTC | View → |
| Kalshi | — | — | — | 09:15 UTC | View → |
| Kalshi | — | — | — | 04:47 UTC | View → |
| Kalshi | — | — | — | 13:40 UTC | View → |
| Kalshi | — | — | — | 18:16 UTC | View → |
| Kalshi | — | — | — | 18:16 UTC | View → |
| Kalshi | — | — | — | 13:52 UTC | View → |
| Kalshi | — | — | — | 09:15 UTC | View → |
| Kalshi | — | — | — | 09:15 UTC | View → |
| Kalshi | — | — | — | 12:13 UTC | View → |
| Kalshi | — | — | — | 09:13 UTC | View → |
| Kalshi | — | — | — | 05:33 UTC | View → |
| Kalshi | — | — | — | 09:57 UTC | View → |
| Kalshi | — | — | — | 18:18 UTC | View → |
| Kalshi | — | — | — | 17:57 UTC | View → |
| Kalshi | — | — | — | 09:15 UTC | View → |
| Kalshi | — | — | — | 16:41 UTC | View → |
| Kalshi | — | — | — | 14:48 UTC | View → |
| Kalshi | — | — | — | 06:05 UTC | View → |
| Kalshi | — | — | — | 05:46 UTC | View → |
| Kalshi | — | — | — | 19:31 UTC | View → |
| Kalshi | — | — | — | 22:56 UTC | View → |
| Kalshi | — | — | — | 01:02 UTC | View → |
| Kalshi | — | — | — | 23:59 UTC | View → |
| Kalshi | — | — | — | 04:28 UTC | View → |
| Kalshi | — | — | — | 23:30 UTC | View → |
| Kalshi | — | — | — | 19:46 UTC | View → |
| Kalshi | — | — | — | 07:21 UTC | View → |
| Kalshi | — | — | — | 12:49 UTC | View → |
| Kalshi | — | — | — | 18:09 UTC | View → |
| Kalshi | — | — | — | 05:45 UTC | View → |
| Kalshi | — | — | — | 07:16 UTC | View → |
| Kalshi | — | — | — | 14:53 UTC | View → |
| Kalshi | — | — | — | 05:46 UTC | View → |
| Kalshi | — | — | — | 00:20 UTC | View → |
| Kalshi | — | — | — | 14:43 UTC | View → |
| Kalshi | — | — | — | 13:57 UTC | View → |
| Kalshi | — | — | — | 23:39 UTC | View → |
| Kalshi | — | — | — | 23:45 UTC | View → |
| Kalshi | — | — | — | 22:07 UTC | View → |
| Kalshi | — | — | — | 21:55 UTC | View → |
| Kalshi | — | — | — | 21:53 UTC | View → |
| Kalshi | — | — | — | 13:57 UTC | View → |
| Kalshi | — | — | — | 22:25 UTC | View → |
| Kalshi | — | — | — | 04:30 UTC | View → |
| Kalshi | — | — | — | 18:36 UTC | View → |
| Kalshi | — | — | — | 07:05 UTC | View → |
| Kalshi | — | — | — | 08:26 UTC | View → |
| Kalshi | — | — | — | 08:51 UTC | View → |
| Kalshi | — | — | — | 08:52 UTC | View → |
| Kalshi | — | — | — | 08:53 UTC | View → |
| Kalshi | — | — | — | 00:24 UTC | View → |
| Kalshi | — | — | — | 00:06 UTC | View → |
| Kalshi | — | — | — | 07:21 UTC | View → |
| Kalshi | — | — | — | 19:41 UTC | View → |
| Kalshi | — | — | — | 07:20 UTC | View → |
| Kalshi | — | — | — | 07:20 UTC | View → |
| Kalshi | — | — | — | 07:05 UTC | View → |
| Kalshi | — | — | — | 09:07 UTC | View → |
| Kalshi | — | — | — | 14:24 UTC | View → |
| Kalshi | — | — | — | 11:25 UTC | View → |
| Kalshi | — | — | — | 05:48 UTC | View → |
| Kalshi | — | — | — | 08:47 UTC | View → |
| Kalshi | — | — | — | 18:14 UTC | View → |
What do these odds mean?
Cross-platform data for yes Detroit,yes Miami,yes Orlando,yes Philadelphia,yes New York 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.