yes Charlotte,yes Dallas,yes San Antonio,yes Philadelphia,yes Orlando,yes Oklahoma City,yes Portland,yes Los Angeles L,yes Toronto wins by over 22.5 points,yes Los Angeles C wins by over 6.5 points
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 08:19 UTC | View → |
| Kalshi | — | — | — | 10:51 UTC | View → |
| Kalshi | — | — | — | 08:19 UTC | View → |
| Kalshi | — | — | — | 21:13 UTC | View → |
| Kalshi | — | — | — | 21:15 UTC | View → |
| Kalshi | — | — | — | 12:30 UTC | View → |
| Kalshi | — | — | — | 08:19 UTC | View → |
| Kalshi | — | — | — | 12:30 UTC | View → |
| Kalshi | — | — | — | 08:19 UTC | View → |
| Kalshi | — | — | — | 08:19 UTC | View → |
| Kalshi | — | — | — | 10:52 UTC | View → |
| Kalshi | — | — | — | 20:37 UTC | View → |
| Kalshi | — | — | — | 11:41 UTC | View → |
| Kalshi | — | — | — | 20:38 UTC | View → |
| Kalshi | — | — | — | 11:41 UTC | View → |
| Kalshi | — | — | — | 10:34 UTC | View → |
| Kalshi | — | — | — | 10:33 UTC | View → |
| Kalshi | — | — | — | 08:19 UTC | View → |
| Kalshi | — | — | — | 08:49 UTC | View → |
| Kalshi | — | — | — | 10:34 UTC | View → |
| Kalshi | — | — | — | 07:55 UTC | View → |
| Kalshi | — | — | — | 10:46 UTC | View → |
| Kalshi | — | — | — | 07:32 UTC | View → |
| Kalshi | — | — | — | 10:52 UTC | View → |
| Kalshi | — | — | — | 07:54 UTC | View → |
| Kalshi | — | — | — | 08:13 UTC | View → |
| Kalshi | — | — | — | 10:53 UTC | View → |
| Kalshi | — | — | — | 08:03 UTC | View → |
| Kalshi | — | — | — | 07:51 UTC | View → |
| Kalshi | — | — | — | 09:09 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 08:54 UTC | View → |
| Kalshi | — | — | — | 08:01 UTC | View → |
| Kalshi | — | — | — | 07:54 UTC | View → |
| Kalshi | — | — | — | 08:03 UTC | View → |
| Kalshi | — | — | — | 10:16 UTC | View → |
| Kalshi | — | — | — | 09:04 UTC | View → |
| Kalshi | — | — | — | 08:19 UTC | View → |
| Kalshi | — | — | — | 07:51 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 10:14 UTC | View → |
| Kalshi | — | — | — | 10:53 UTC | View → |
| Kalshi | — | — | — | 07:51 UTC | View → |
| Kalshi | — | — | — | 07:44 UTC | View → |
| Kalshi | — | — | — | 09:55 UTC | View → |
| Kalshi | — | — | — | 10:45 UTC | View → |
| Kalshi | — | — | — | 09:43 UTC | View → |
| Kalshi | — | — | — | 07:32 UTC | View → |
| Kalshi | — | — | — | 10:43 UTC | View → |
| Kalshi | — | — | — | 09:42 UTC | View → |
| Kalshi | — | — | — | 07:51 UTC | View → |
| Kalshi | — | — | — | 07:58 UTC | View → |
| Kalshi | — | — | — | 08:31 UTC | View → |
| Kalshi | — | — | — | 08:31 UTC | View → |
| Kalshi | — | — | — | 09:42 UTC | View → |
| Kalshi | — | — | — | 10:16 UTC | View → |
| Kalshi | — | — | — | 07:54 UTC | View → |
| Kalshi | — | — | — | 07:51 UTC | View → |
| Kalshi | — | — | — | 20:57 UTC | View → |
| Kalshi | — | — | — | 09:45 UTC | View → |
| Kalshi | — | — | — | 08:19 UTC | View → |
| Kalshi | — | — | — | 20:53 UTC | View → |
| Kalshi | — | — | — | 07:51 UTC | View → |
| Kalshi | — | — | — | 08:58 UTC | View → |
| Kalshi | — | — | — | 08:56 UTC | View → |
| Kalshi | — | — | — | 08:56 UTC | View → |
| Kalshi | — | — | — | 08:03 UTC | View → |
What do these odds mean?
Cross-platform data for yes Charlotte,yes Dallas,yes San Antonio,yes Philadelphia,yes Orlando,yes Oklahoma City,yes Portland,yes Los Angeles L,yes Toronto wins by over 22.5 points,yes Los Angeles C wins by over 6.5 points 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.