yes Kansas City,yes San Diego,yes Over 4.5 runs scored,no Houston wins by over 10.5 points,yes Over 216.5 points scored
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 23:03 UTC | View → |
| Kalshi | — | — | — | 20:24 UTC | View → |
| Kalshi | — | — | — | 07:57 UTC | View → |
| Kalshi | — | — | — | 07:57 UTC | View → |
| Kalshi | — | — | — | 15:32 UTC | View → |
| Kalshi | — | — | — | 16:50 UTC | View → |
| Kalshi | — | — | — | 21:00 UTC | View → |
| Kalshi | — | — | — | 06:44 UTC | View → |
| Kalshi | — | — | — | 08:42 UTC | View → |
| Kalshi | — | — | — | 08:18 UTC | View → |
| Kalshi | — | — | — | 23:03 UTC | View → |
| Kalshi | — | — | — | 06:44 UTC | View → |
| Kalshi | — | — | — | 06:45 UTC | View → |
| Kalshi | — | — | — | 04:57 UTC | View → |
| Kalshi | — | — | — | 22:47 UTC | View → |
| Kalshi | — | — | — | 21:20 UTC | View → |
| Kalshi | — | — | — | 20:37 UTC | View → |
| Kalshi | — | — | — | 16:04 UTC | View → |
| Kalshi | — | — | — | 17:25 UTC | View → |
| Kalshi | — | — | — | 07:56 UTC | View → |
| Kalshi | — | — | — | 07:56 UTC | View → |
| Kalshi | — | — | — | 20:25 UTC | View → |
| Kalshi | — | — | — | 00:19 UTC | View → |
| Kalshi | — | — | — | 16:27 UTC | View → |
| Kalshi | — | — | — | 07:56 UTC | View → |
| Kalshi | — | — | — | 11:17 UTC | View → |
| Kalshi | — | — | — | 06:32 UTC | View → |
| Kalshi | — | — | — | 06:45 UTC | View → |
| Kalshi | — | — | — | 20:42 UTC | View → |
| Kalshi | — | — | — | 06:46 UTC | View → |
| Kalshi | — | — | — | 21:20 UTC | View → |
| Kalshi | — | — | — | 13:38 UTC | View → |
| Kalshi | — | — | — | 08:06 UTC | View → |
| Kalshi | — | — | — | 16:00 UTC | View → |
| Kalshi | — | — | — | 23:40 UTC | View → |
| Kalshi | — | — | — | 21:15 UTC | View → |
| Kalshi | — | — | — | 18:00 UTC | View → |
| Kalshi | — | — | — | 21:16 UTC | View → |
| Kalshi | — | — | — | 15:05 UTC | View → |
| Kalshi | — | — | — | 00:45 UTC | View → |
| Kalshi | — | — | — | 06:20 UTC | View → |
| Kalshi | — | — | — | 08:03 UTC | View → |
| Kalshi | — | — | — | 16:25 UTC | View → |
| Kalshi | — | — | — | 00:14 UTC | View → |
| Kalshi | — | — | — | 21:28 UTC | View → |
| Kalshi | — | — | — | 21:28 UTC | View → |
| Kalshi | — | — | — | 07:19 UTC | View → |
| Kalshi | — | — | — | 13:27 UTC | View → |
| Kalshi | — | — | — | 13:57 UTC | View → |
| Kalshi | — | — | — | 18:25 UTC | View → |
| Kalshi | — | — | — | 20:55 UTC | View → |
| Kalshi | — | — | — | 10:57 UTC | View → |
| Kalshi | — | — | — | 20:23 UTC | View → |
| Kalshi | — | — | — | 08:47 UTC | View → |
| Kalshi | — | — | — | 08:02 UTC | View → |
| Kalshi | — | — | — | 23:40 UTC | View → |
| Kalshi | — | — | — | 18:37 UTC | View → |
| Kalshi | — | — | — | 10:55 UTC | View → |
| Kalshi | — | — | — | 01:03 UTC | View → |
| Kalshi | — | — | — | 11:10 UTC | View → |
| Kalshi | — | — | — | 16:32 UTC | View → |
| Kalshi | — | — | — | 02:17 UTC | View → |
| Kalshi | — | — | — | 21:32 UTC | View → |
| Kalshi | — | — | — | 21:29 UTC | View → |
| Kalshi | — | — | — | 09:44 UTC | View → |
| Kalshi | — | — | — | 19:57 UTC | View → |
| Kalshi | — | — | — | 06:20 UTC | View → |
| Kalshi | — | — | — | 20:38 UTC | View → |
| Kalshi | — | — | — | 21:16 UTC | View → |
What do these odds mean?
Cross-platform data for yes Kansas City,yes San Diego,yes Over 4.5 runs scored,no Houston wins by over 10.5 points,yes Over 216.5 points scored 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.