yes San Antonio,yes Houston,yes Oklahoma City,yes Chicago,yes Toronto
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 10:33 UTC | View → |
| Kalshi | — | — | — | 14:58 UTC | View → |
| Kalshi | — | — | — | 20:32 UTC | View → |
| Kalshi | — | — | — | 10:33 UTC | View → |
| Kalshi | — | — | — | 13:14 UTC | View → |
| Kalshi | — | — | — | 10:16 UTC | View → |
| Kalshi | — | — | — | 00:25 UTC | View → |
| Kalshi | — | — | — | 15:55 UTC | View → |
| Kalshi | — | — | — | 18:31 UTC | View → |
| Kalshi | — | — | — | 09:57 UTC | View → |
| Kalshi | — | — | — | 15:15 UTC | View → |
| Kalshi | — | — | — | 21:39 UTC | View → |
| Kalshi | — | — | — | 14:16 UTC | View → |
| Kalshi | — | — | — | 09:53 UTC | View → |
| Kalshi | — | — | — | 14:58 UTC | View → |
| Kalshi | — | — | — | 23:03 UTC | View → |
| Kalshi | — | — | — | 23:10 UTC | View → |
| Kalshi | — | — | — | 10:33 UTC | View → |
| Kalshi | — | — | — | 07:00 UTC | View → |
| Kalshi | — | — | — | 22:02 UTC | View → |
| Kalshi | — | — | — | 09:31 UTC | View → |
| Kalshi | — | — | — | 13:57 UTC | View → |
| Kalshi | — | — | — | 15:36 UTC | View → |
| Kalshi | — | — | — | 11:30 UTC | View → |
| Kalshi | — | — | — | 11:48 UTC | View → |
| Kalshi | — | — | — | 00:25 UTC | View → |
| Kalshi | — | — | — | 14:16 UTC | View → |
| Kalshi | — | — | — | 15:07 UTC | View → |
| Kalshi | — | — | — | 15:07 UTC | View → |
| Kalshi | — | — | — | 16:04 UTC | View → |
| Kalshi | — | — | — | 11:14 UTC | View → |
| Kalshi | — | — | — | 20:06 UTC | View → |
| Kalshi | — | — | — | 08:55 UTC | View → |
| Kalshi | — | — | — | 10:58 UTC | View → |
| Kalshi | — | — | — | 17:24 UTC | View → |
| Kalshi | — | — | — | 08:35 UTC | View → |
| Kalshi | — | — | — | 07:27 UTC | View → |
| Kalshi | — | — | — | 10:08 UTC | View → |
| Kalshi | — | — | — | 08:39 UTC | View → |
| Kalshi | — | — | — | 00:08 UTC | View → |
| Kalshi | — | — | — | 23:42 UTC | View → |
| Kalshi | — | — | — | 11:04 UTC | View → |
| Kalshi | — | — | — | 21:37 UTC | View → |
| Kalshi | — | — | — | 11:15 UTC | View → |
| Kalshi | — | — | — | 18:08 UTC | View → |
| Kalshi | — | — | — | 20:30 UTC | View → |
| Kalshi | — | — | — | 17:02 UTC | View → |
| Kalshi | — | — | — | 23:03 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 07:55 UTC | View → |
| Kalshi | — | — | — | 16:30 UTC | View → |
| Kalshi | — | — | — | 13:48 UTC | View → |
| Kalshi | — | — | — | 13:48 UTC | View → |
| Kalshi | — | — | — | 16:09 UTC | View → |
| Kalshi | — | — | — | 11:14 UTC | View → |
| Kalshi | — | — | — | 10:29 UTC | View → |
| Kalshi | — | — | — | 08:51 UTC | View → |
| Kalshi | — | — | — | 18:34 UTC | View → |
| Kalshi | — | — | — | 13:46 UTC | View → |
| Kalshi | — | — | — | 09:04 UTC | View → |
| Kalshi | — | — | — | 09:04 UTC | View → |
| Kalshi | — | — | — | 19:26 UTC | View → |
| Kalshi | — | — | — | 21:00 UTC | View → |
| Kalshi | — | — | — | 08:30 UTC | View → |
| Kalshi | — | — | — | 08:38 UTC | View → |
| Kalshi | — | — | — | 13:47 UTC | View → |
| Kalshi | — | — | — | 20:02 UTC | View → |
| Kalshi | — | — | — | 07:27 UTC | View → |
| Kalshi | — | — | — | 16:06 UTC | View → |
| Kalshi | — | — | — | 08:30 UTC | View → |
| Kalshi | — | — | — | 12:57 UTC | View → |
| Kalshi | — | — | — | 15:01 UTC | View → |
| Kalshi | — | — | — | 17:03 UTC | View → |
What do these odds mean?
Cross-platform data for yes San Antonio,yes Houston,yes Oklahoma City,yes Chicago,yes Toronto 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.