yes Aaron Pico,yes Mateusz Gamrot,yes Kelvin Gastelum,yes Randy Brown,yes Marquel Mederos,yes Francisco Prado,yes Johnny Walker,yes Tatiana Suarez,yes Cub Swanson
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 23:13 UTC | View → |
| Kalshi | — | — | — | 04:35 UTC | View → |
| Kalshi | — | — | — | 20:36 UTC | View → |
| Kalshi | — | — | — | 20:36 UTC | View → |
| Kalshi | — | — | — | 20:36 UTC | View → |
| Kalshi | — | — | — | 19:39 UTC | View → |
| Kalshi | — | — | — | 18:15 UTC | View → |
| Kalshi | — | — | — | 16:57 UTC | View → |
| Kalshi | — | — | — | 14:34 UTC | View → |
| Kalshi | — | — | — | 18:27 UTC | View → |
| Kalshi | — | — | — | 23:18 UTC | View → |
| Kalshi | — | — | — | 20:08 UTC | View → |
| Kalshi | — | — | — | 00:55 UTC | View → |
| Kalshi | — | — | — | 04:55 UTC | View → |
| Kalshi | — | — | — | 05:22 UTC | View → |
| Kalshi | — | — | — | 05:37 UTC | View → |
| Kalshi | — | — | — | 14:23 UTC | View → |
| Kalshi | — | — | — | 04:55 UTC | View → |
| Kalshi | — | — | — | 23:11 UTC | View → |
| Kalshi | — | — | — | 08:52 UTC | View → |
| Kalshi | — | — | — | 18:27 UTC | View → |
| Kalshi | — | — | — | 00:48 UTC | View → |
| Kalshi | — | — | — | 04:35 UTC | View → |
| Kalshi | — | — | — | 13:21 UTC | View → |
| Kalshi | — | — | — | 09:42 UTC | View → |
| Kalshi | — | — | — | 00:57 UTC | View → |
| Kalshi | — | — | — | 10:49 UTC | View → |
| Kalshi | — | — | — | 09:42 UTC | View → |
| Kalshi | — | — | — | 09:42 UTC | View → |
| Kalshi | — | — | — | 20:06 UTC | View → |
| Kalshi | — | — | — | 19:39 UTC | View → |
| Kalshi | — | — | — | 04:29 UTC | View → |
| Kalshi | — | — | — | 20:35 UTC | View → |
| Kalshi | — | — | — | 04:33 UTC | View → |
| Kalshi | — | — | — | 14:20 UTC | View → |
| Kalshi | — | — | — | 00:22 UTC | View → |
| Kalshi | — | — | — | 07:14 UTC | View → |
| Kalshi | — | — | — | 04:52 UTC | View → |
| Kalshi | — | — | — | 04:23 UTC | View → |
| Kalshi | — | — | — | 16:33 UTC | View → |
| Kalshi | — | — | — | 01:06 UTC | View → |
| Kalshi | — | — | — | 05:28 UTC | View → |
| Kalshi | — | — | — | 16:19 UTC | View → |
| Kalshi | — | — | — | 13:31 UTC | View → |
| Kalshi | — | — | — | 00:04 UTC | View → |
| Kalshi | — | — | — | 14:20 UTC | View → |
| Kalshi | — | — | — | 07:14 UTC | View → |
| Kalshi | — | — | — | 15:40 UTC | View → |
| Kalshi | — | — | — | 15:40 UTC | View → |
| Kalshi | — | — | — | 07:14 UTC | View → |
| Kalshi | — | — | — | 13:31 UTC | View → |
| Kalshi | — | — | — | 20:36 UTC | View → |
| Kalshi | — | — | — | 16:15 UTC | View → |
| Kalshi | — | — | — | 09:22 UTC | View → |
| Kalshi | — | — | — | 07:47 UTC | View → |
| Kalshi | — | — | — | 10:15 UTC | View → |
| Kalshi | — | — | — | 07:34 UTC | View → |
| Kalshi | — | — | — | 06:58 UTC | View → |
| Kalshi | — | — | — | 17:19 UTC | View → |
| Kalshi | — | — | — | 06:45 UTC | View → |
| Kalshi | — | — | — | 17:19 UTC | View → |
| Kalshi | — | — | — | 06:45 UTC | View → |
| Kalshi | — | — | — | 18:15 UTC | View → |
| Kalshi | — | — | — | 19:54 UTC | View → |
| Kalshi | — | — | — | 04:23 UTC | View → |
| Kalshi | — | — | — | 20:34 UTC | View → |
| Kalshi | — | — | — | 16:36 UTC | View → |
| Kalshi | — | — | — | 16:39 UTC | View → |
| Kalshi | — | — | — | 18:28 UTC | View → |
| Kalshi | — | — | — | 08:40 UTC | View → |
| Kalshi | — | — | — | 14:20 UTC | View → |
| Kalshi | — | — | — | 15:46 UTC | View → |
| Kalshi | — | — | — | 08:34 UTC | View → |
| Kalshi | — | — | — | 04:33 UTC | View → |
| Kalshi | — | — | — | 16:28 UTC | View → |
| Kalshi | — | — | — | 08:44 UTC | View → |
What do these odds mean?
Cross-platform data for yes Aaron Pico,yes Mateusz Gamrot,yes Kelvin Gastelum,yes Randy Brown,yes Marquel Mederos,yes Francisco Prado,yes Johnny Walker,yes Tatiana Suarez,yes Cub Swanson 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.