yes Minnesota,yes New York M,yes New York Y,no Detroit wins by over 1.5 runs,yes Los Angeles D wins by over 1.5 runs,yes Josh Hokit,yes Esteban Ribovics,yes Randy Brown,yes Azamat Murzakanov,yes Francisco Prado,yes Lupita Godinez,yes Nate Landwehr
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 08:17 UTC | View → |
| Kalshi | — | — | — | 06:34 UTC | View → |
| Kalshi | — | — | — | 07:04 UTC | View → |
| Kalshi | — | — | — | 23:39 UTC | View → |
| Kalshi | — | — | — | 19:25 UTC | View → |
| Kalshi | — | — | — | 12:31 UTC | View → |
| Kalshi | — | — | — | 08:46 UTC | View → |
| Kalshi | — | — | — | 23:25 UTC | View → |
| Kalshi | — | — | — | 08:35 UTC | View → |
| Kalshi | — | — | — | 14:49 UTC | View → |
| Kalshi | — | — | — | 10:19 UTC | View → |
| Kalshi | — | — | — | 21:18 UTC | View → |
| Kalshi | — | — | — | 19:50 UTC | View → |
| Kalshi | — | — | — | 15:06 UTC | View → |
| Kalshi | — | — | — | 22:46 UTC | View → |
| Kalshi | — | — | — | 06:32 UTC | View → |
| Kalshi | — | — | — | 10:00 UTC | View → |
| Kalshi | — | — | — | 18:45 UTC | View → |
| Kalshi | — | — | — | 07:45 UTC | View → |
| Kalshi | — | — | — | 19:49 UTC | View → |
| Kalshi | — | — | — | 23:39 UTC | View → |
| Kalshi | — | — | — | 00:22 UTC | View → |
| Kalshi | — | — | — | 07:27 UTC | View → |
| Kalshi | — | — | — | 06:33 UTC | View → |
| Kalshi | — | — | — | 10:35 UTC | View → |
| Kalshi | — | — | — | 10:35 UTC | View → |
| Kalshi | — | — | — | 17:26 UTC | View → |
| Kalshi | — | — | — | 17:15 UTC | View → |
| Kalshi | — | — | — | 10:35 UTC | View → |
| Kalshi | — | — | — | 06:33 UTC | View → |
| Kalshi | — | — | — | 12:28 UTC | View → |
| Kalshi | — | — | — | 14:17 UTC | View → |
| Kalshi | — | — | — | 08:02 UTC | View → |
| Kalshi | — | — | — | 23:23 UTC | View → |
| Kalshi | — | — | — | 23:23 UTC | View → |
| Kalshi | — | — | — | 17:26 UTC | View → |
| Kalshi | — | — | — | 23:26 UTC | View → |
| Kalshi | — | — | — | 01:06 UTC | View → |
| Kalshi | — | — | — | 08:17 UTC | View → |
| Kalshi | — | — | — | 01:21 UTC | View → |
| Kalshi | — | — | — | 14:50 UTC | View → |
| Kalshi | — | — | — | 23:34 UTC | View → |
| Kalshi | — | — | — | 22:15 UTC | View → |
| Kalshi | — | — | — | 09:23 UTC | View → |
| Kalshi | — | — | — | 11:51 UTC | View → |
| Kalshi | — | — | — | 07:55 UTC | View → |
| Kalshi | — | — | — | 23:30 UTC | View → |
| Kalshi | — | — | — | 07:55 UTC | View → |
| Kalshi | — | — | — | 10:41 UTC | View → |
| Kalshi | — | — | — | 21:38 UTC | View → |
| Kalshi | — | — | — | 08:19 UTC | View → |
| Kalshi | — | — | — | 08:24 UTC | View → |
| Kalshi | — | — | — | 08:23 UTC | View → |
| Kalshi | — | — | — | 08:53 UTC | View → |
| Kalshi | — | — | — | 08:27 UTC | View → |
| Kalshi | — | — | — | 22:19 UTC | View → |
| Kalshi | — | — | — | 16:11 UTC | View → |
| Kalshi | — | — | — | 00:27 UTC | View → |
| Kalshi | — | — | — | 22:54 UTC | View → |
| Kalshi | — | — | — | 21:12 UTC | View → |
| Kalshi | — | — | — | 17:44 UTC | View → |
| Kalshi | — | — | — | 09:14 UTC | View → |
| Kalshi | — | — | — | 20:50 UTC | View → |
| Kalshi | — | — | — | 08:38 UTC | View → |
| Kalshi | — | — | — | 07:25 UTC | View → |
| Kalshi | — | — | — | 16:11 UTC | View → |
| Kalshi | — | — | — | 23:19 UTC | View → |
| Kalshi | — | — | — | 08:58 UTC | View → |
| Kalshi | — | — | — | 07:56 UTC | View → |
| Kalshi | — | — | — | 23:27 UTC | View → |
| Kalshi | — | — | — | 07:25 UTC | View → |
| Kalshi | — | — | — | 18:43 UTC | View → |
| Kalshi | — | — | — | 23:27 UTC | View → |
| Kalshi | — | — | — | 08:58 UTC | View → |
| Kalshi | — | — | — | 06:34 UTC | View → |
| Kalshi | — | — | — | 08:24 UTC | View → |
| Kalshi | — | — | — | 20:08 UTC | View → |
| Kalshi | — | — | — | 22:27 UTC | View → |
| Kalshi | — | — | — | 07:54 UTC | View → |
| Kalshi | — | — | — | 10:41 UTC | View → |
| Kalshi | — | — | — | 11:44 UTC | View → |
| Kalshi | — | — | — | 06:36 UTC | View → |
| Kalshi | — | — | — | 08:28 UTC | View → |
| Kalshi | — | — | — | 10:41 UTC | View → |
| Kalshi | — | — | — | 08:58 UTC | View → |
| Kalshi | — | — | — | 08:24 UTC | View → |
| Kalshi | — | — | — | 08:42 UTC | View → |
| Kalshi | — | — | — | 08:24 UTC | View → |
| Kalshi | — | — | — | 08:34 UTC | View → |
| Kalshi | — | — | — | 23:41 UTC | View → |
| Kalshi | — | — | — | 23:22 UTC | View → |
| Kalshi | — | — | — | 08:36 UTC | View → |
| Kalshi | — | — | — | 19:15 UTC | View → |
| Kalshi | — | — | — | 08:58 UTC | View → |
| Kalshi | — | — | — | 07:54 UTC | View → |
| Kalshi | — | — | — | 23:41 UTC | View → |
| Kalshi | — | — | — | 23:18 UTC | View → |
| Kalshi | — | — | — | 09:23 UTC | View → |
| Kalshi | — | — | — | 11:51 UTC | View → |
| Kalshi | — | — | — | 21:35 UTC | View → |
| Kalshi | — | — | — | 23:46 UTC | View → |
| Kalshi | — | — | — | 23:33 UTC | View → |
| Kalshi | — | — | — | 17:14 UTC | View → |
| Kalshi | — | — | — | 08:21 UTC | View → |
| Kalshi | — | — | — | 19:30 UTC | View → |
| Kalshi | — | — | — | 06:34 UTC | View → |
| Kalshi | — | — | — | 07:22 UTC | View → |
| Kalshi | — | — | — | 23:23 UTC | View → |
| Kalshi | — | — | — | 08:33 UTC | View → |
| Kalshi | — | — | — | 08:28 UTC | View → |
| Kalshi | — | — | — | 23:23 UTC | View → |
| Kalshi | — | — | — | 08:28 UTC | View → |
| Kalshi | — | — | — | 22:45 UTC | View → |
| Kalshi | — | — | — | 23:59 UTC | View → |
| Kalshi | — | — | — | 17:36 UTC | View → |
| Kalshi | — | — | — | 17:43 UTC | View → |
| Kalshi | — | — | — | 11:44 UTC | View → |
What do these odds mean?
Cross-platform data for yes Minnesota,yes New York M,yes New York Y,no Detroit wins by over 1.5 runs,yes Los Angeles D wins by over 1.5 runs,yes Josh Hokit,yes Esteban Ribovics,yes Randy Brown,yes Azamat Murzakanov,yes Francisco Prado,yes Lupita Godinez,yes Nate Landwehr 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.