yes Cincinnati,yes New York M,yes Atlanta,yes Milwaukee,yes St. Louis
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 12:36 UTC | View → |
| Kalshi | — | — | — | 19:14 UTC | View → |
| Kalshi | — | — | — | 14:39 UTC | View → |
| Kalshi | — | — | — | 21:56 UTC | View → |
| Kalshi | — | — | — | 15:25 UTC | View → |
| Kalshi | — | — | — | 16:48 UTC | View → |
| Kalshi | — | — | — | 07:08 UTC | View → |
| Kalshi | — | — | — | 14:39 UTC | View → |
| Kalshi | — | — | — | 15:11 UTC | View → |
| Kalshi | — | — | — | 12:36 UTC | View → |
| Kalshi | — | — | — | 02:15 UTC | View → |
| Kalshi | — | — | — | 08:54 UTC | View → |
| Kalshi | — | — | — | 02:14 UTC | View → |
| Kalshi | — | — | — | 08:46 UTC | View → |
| Kalshi | — | — | — | 08:46 UTC | View → |
| Kalshi | — | — | — | 14:19 UTC | View → |
| Kalshi | — | — | — | 02:12 UTC | View → |
| Kalshi | — | — | — | 21:56 UTC | View → |
| Kalshi | — | — | — | 02:14 UTC | View → |
| Kalshi | — | — | — | 02:14 UTC | View → |
| Kalshi | — | — | — | 19:05 UTC | View → |
| Kalshi | — | — | — | 02:15 UTC | View → |
| Kalshi | — | — | — | 08:33 UTC | View → |
| Kalshi | — | — | — | 09:59 UTC | View → |
| Kalshi | — | — | — | 15:50 UTC | View → |
| Kalshi | — | — | — | 09:37 UTC | View → |
| Kalshi | — | — | — | 15:50 UTC | View → |
| Kalshi | — | — | — | 21:09 UTC | View → |
| Kalshi | — | — | — | 18:33 UTC | View → |
| Kalshi | — | — | — | 19:19 UTC | View → |
| Kalshi | — | — | — | 05:10 UTC | View → |
| Kalshi | — | — | — | 18:17 UTC | View → |
| Kalshi | — | — | — | 08:27 UTC | View → |
| Kalshi | — | — | — | 09:44 UTC | View → |
| Kalshi | — | — | — | 08:22 UTC | View → |
| Kalshi | — | — | — | 18:16 UTC | View → |
| Kalshi | — | — | — | 08:41 UTC | View → |
| Kalshi | — | — | — | 20:18 UTC | View → |
| Kalshi | — | — | — | 09:49 UTC | View → |
| Kalshi | — | — | — | 14:57 UTC | View → |
| Kalshi | — | — | — | 22:14 UTC | View → |
| Kalshi | — | — | — | 08:27 UTC | View → |
| Kalshi | — | — | — | 21:04 UTC | View → |
| Kalshi | — | — | — | 17:46 UTC | View → |
| Kalshi | — | — | — | 16:50 UTC | View → |
| Kalshi | — | — | — | 20:21 UTC | View → |
| Kalshi | — | — | — | 15:02 UTC | View → |
| Kalshi | — | — | — | 09:42 UTC | View → |
| Kalshi | — | — | — | 09:45 UTC | View → |
| Kalshi | — | — | — | 21:38 UTC | View → |
| Kalshi | — | — | — | 08:27 UTC | View → |
| Kalshi | — | — | — | 08:27 UTC | View → |
| Kalshi | — | — | — | 11:53 UTC | View → |
| Kalshi | — | — | — | 22:08 UTC | View → |
| Kalshi | — | — | — | 09:46 UTC | View → |
| Kalshi | — | — | — | 11:33 UTC | View → |
| Kalshi | — | — | — | 22:03 UTC | View → |
| Kalshi | — | — | — | 14:49 UTC | View → |
| Kalshi | — | — | — | 08:46 UTC | View → |
| Kalshi | — | — | — | 09:42 UTC | View → |
| Kalshi | — | — | — | 21:44 UTC | View → |
| Kalshi | — | — | — | 19:22 UTC | View → |
| Kalshi | — | — | — | 19:37 UTC | View → |
| Kalshi | — | — | — | 20:51 UTC | View → |
| Kalshi | — | — | — | 17:38 UTC | View → |
| Kalshi | — | — | — | 19:19 UTC | View → |
| Kalshi | — | — | — | 08:25 UTC | View → |
| Kalshi | — | — | — | 08:25 UTC | View → |
| Kalshi | — | — | — | 17:50 UTC | View → |
| Kalshi | — | — | — | 09:36 UTC | View → |
| Kalshi | — | — | — | 23:27 UTC | View → |
| Kalshi | — | — | — | 19:00 UTC | View → |
What do these odds mean?
Cross-platform data for yes Cincinnati,yes New York M,yes Atlanta,yes Milwaukee,yes St. Louis 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.