yes Toronto,yes Kansas City,yes San Diego
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 11:48 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 17:04 UTC | View → |
| Kalshi | — | — | — | 09:09 UTC | View → |
| Kalshi | — | — | — | 09:08 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 23:03 UTC | View → |
| Kalshi | — | — | — | 23:03 UTC | View → |
| Kalshi | — | — | — | 17:40 UTC | View → |
| Kalshi | — | — | — | 07:54 UTC | View → |
| Kalshi | — | — | — | 00:28 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 09:07 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 09:10 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 09:10 UTC | View → |
| Kalshi | — | — | — | 09:08 UTC | View → |
| Kalshi | — | — | — | 09:09 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 00:46 UTC | View → |
| Kalshi | — | — | — | 19:01 UTC | View → |
| Kalshi | — | — | — | 09:10 UTC | View → |
| Kalshi | — | — | — | 11:48 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 08:47 UTC | View → |
| Kalshi | — | — | — | 15:12 UTC | View → |
| Kalshi | — | — | — | 15:10 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 14:44 UTC | View → |
| Kalshi | — | — | — | 14:44 UTC | View → |
| Kalshi | — | — | — | 09:11 UTC | View → |
| Kalshi | — | — | — | 09:07 UTC | View → |
| Kalshi | — | — | — | 14:36 UTC | View → |
| Kalshi | — | — | — | 01:05 UTC | View → |
| Kalshi | — | — | — | 16:17 UTC | View → |
| Kalshi | — | — | — | 22:09 UTC | View → |
| Kalshi | — | — | — | 08:52 UTC | View → |
| Kalshi | — | — | — | 14:37 UTC | View → |
| Kalshi | — | — | — | 09:55 UTC | View → |
| Kalshi | — | — | — | 01:02 UTC | View → |
| Kalshi | — | — | — | 08:08 UTC | View → |
| Kalshi | — | — | — | 21:35 UTC | View → |
| Kalshi | — | — | — | 10:16 UTC | View → |
| Kalshi | — | — | — | 22:20 UTC | View → |
| Kalshi | — | — | — | 09:00 UTC | View → |
| Kalshi | — | — | — | 20:25 UTC | View → |
| Kalshi | — | — | — | 22:50 UTC | View → |
| Kalshi | — | — | — | 08:53 UTC | View → |
| Kalshi | — | — | — | 08:53 UTC | View → |
| Kalshi | — | — | — | 17:20 UTC | View → |
| Kalshi | — | — | — | 20:23 UTC | View → |
| Kalshi | — | — | — | 19:26 UTC | View → |
| Kalshi | — | — | — | 23:52 UTC | View → |
| Kalshi | — | — | — | 23:01 UTC | View → |
| Kalshi | — | — | — | 00:58 UTC | View → |
| Kalshi | — | — | — | 10:36 UTC | View → |
| Kalshi | — | — | — | 09:42 UTC | View → |
| Kalshi | — | — | — | 10:46 UTC | View → |
| Kalshi | — | — | — | 17:18 UTC | View → |
| Kalshi | — | — | — | 08:08 UTC | View → |
| Kalshi | — | — | — | 14:56 UTC | View → |
| Kalshi | — | — | — | 23:04 UTC | View → |
| Kalshi | — | — | — | 11:14 UTC | View → |
| Kalshi | — | — | — | 14:57 UTC | View → |
| Kalshi | — | — | — | 08:53 UTC | View → |
| Kalshi | — | — | — | 07:56 UTC | View → |
| Kalshi | — | — | — | 10:20 UTC | View → |
| Kalshi | — | — | — | 18:51 UTC | View → |
| Kalshi | — | — | — | 12:44 UTC | View → |
| Kalshi | — | — | — | 00:56 UTC | View → |
| Kalshi | — | — | — | 22:01 UTC | View → |
| Kalshi | — | — | — | 00:55 UTC | View → |
What do these odds mean?
Cross-platform data for yes Toronto,yes Kansas City,yes San Diego 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.