yes Los Angeles L,yes San Antonio,yes Miami,yes Houston,yes Boston,yes Orlando,yes Philadelphia
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 11:45 UTC | View → |
| Kalshi | — | — | — | 13:24 UTC | View → |
| Kalshi | — | — | — | 06:21 UTC | View → |
| Kalshi | — | — | — | 18:24 UTC | View → |
| Kalshi | — | — | — | 14:34 UTC | View → |
| Kalshi | — | — | — | 19:26 UTC | View → |
| Kalshi | — | — | — | 04:34 UTC | View → |
| Kalshi | — | — | — | 07:44 UTC | View → |
| Kalshi | — | — | — | 10:51 UTC | View → |
| Kalshi | — | — | — | 10:51 UTC | View → |
| Kalshi | — | — | — | 16:35 UTC | View → |
| Kalshi | — | — | — | 22:54 UTC | View → |
| Kalshi | — | — | — | 11:46 UTC | View → |
| Kalshi | — | — | — | 08:31 UTC | View → |
| Kalshi | — | — | — | 07:43 UTC | View → |
| Kalshi | — | — | — | 08:28 UTC | View → |
| Kalshi | — | — | — | 06:28 UTC | View → |
| Kalshi | — | — | — | 08:00 UTC | View → |
| Kalshi | — | — | — | 08:00 UTC | View → |
| Kalshi | — | — | — | 08:00 UTC | View → |
| Kalshi | — | — | — | 06:28 UTC | View → |
| Kalshi | — | — | — | 17:31 UTC | View → |
| Kalshi | — | — | — | 08:00 UTC | View → |
| Kalshi | — | — | — | 11:46 UTC | View → |
| Kalshi | — | — | — | 15:15 UTC | View → |
| Kalshi | — | — | — | 23:23 UTC | View → |
| Kalshi | — | — | — | 11:46 UTC | View → |
| Kalshi | — | — | — | 15:21 UTC | View → |
| Kalshi | — | — | — | 20:49 UTC | View → |
| Kalshi | — | — | — | 11:46 UTC | View → |
| Kalshi | — | — | — | 00:18 UTC | View → |
| Kalshi | — | — | — | 11:47 UTC | View → |
| Kalshi | — | — | — | 10:48 UTC | View → |
| Kalshi | — | — | — | 14:30 UTC | View → |
| Kalshi | — | — | — | 23:14 UTC | View → |
| Kalshi | — | — | — | 06:11 UTC | View → |
| Kalshi | — | — | — | 04:27 UTC | View → |
| Kalshi | — | — | — | 07:12 UTC | View → |
| Kalshi | — | — | — | 07:38 UTC | View → |
| Kalshi | — | — | — | 07:38 UTC | View → |
| Kalshi | — | — | — | 17:09 UTC | View → |
| Kalshi | — | — | — | 17:00 UTC | View → |
| Kalshi | — | — | — | 08:11 UTC | View → |
| Kalshi | — | — | — | 23:45 UTC | View → |
| Kalshi | — | — | — | 07:41 UTC | View → |
| Kalshi | — | — | — | 23:54 UTC | View → |
| Kalshi | — | — | — | 05:46 UTC | View → |
| Kalshi | — | — | — | 15:56 UTC | View → |
| Kalshi | — | — | — | 17:40 UTC | View → |
| Kalshi | — | — | — | 11:35 UTC | View → |
| Kalshi | — | — | — | 08:11 UTC | View → |
| Kalshi | — | — | — | 23:34 UTC | View → |
| Kalshi | — | — | — | 06:57 UTC | View → |
| Kalshi | — | — | — | 20:35 UTC | View → |
| Kalshi | — | — | — | 14:44 UTC | View → |
| Kalshi | — | — | — | 01:12 UTC | View → |
| Kalshi | — | — | — | 11:34 UTC | View → |
| Kalshi | — | — | — | 13:03 UTC | View → |
| Kalshi | — | — | — | 06:35 UTC | View → |
| Kalshi | — | — | — | 20:04 UTC | View → |
| Kalshi | — | — | — | 09:29 UTC | View → |
| Kalshi | — | — | — | 09:02 UTC | View → |
| Kalshi | — | — | — | 09:23 UTC | View → |
| Kalshi | — | — | — | 21:09 UTC | View → |
| Kalshi | — | — | — | 22:05 UTC | View → |
| Kalshi | — | — | — | 11:34 UTC | View → |
| Kalshi | — | — | — | 09:41 UTC | View → |
| Kalshi | — | — | — | 07:45 UTC | View → |
| Kalshi | — | — | — | 00:18 UTC | View → |
| Kalshi | — | — | — | 09:42 UTC | View → |
| Kalshi | — | — | — | 00:22 UTC | View → |
| Kalshi | — | — | — | 13:29 UTC | View → |
| Kalshi | — | — | — | 13:30 UTC | View → |
| Kalshi | — | — | — | 20:42 UTC | View → |
| Kalshi | — | — | — | 14:20 UTC | View → |
| Kalshi | — | — | — | 21:35 UTC | View → |
| Kalshi | — | — | — | 15:56 UTC | View → |
| Kalshi | — | — | — | 00:23 UTC | View → |
| Kalshi | — | — | — | 20:55 UTC | View → |
| Kalshi | — | — | — | 12:15 UTC | View → |
What do these odds mean?
Cross-platform data for yes Los Angeles L,yes San Antonio,yes Miami,yes Houston,yes Boston,yes Orlando,yes Philadelphia 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.