yes Baltimore,no San Diego wins by over 1.5 runs,no New York M wins by over 3.5 runs,no Baltimore wins by over 3.5 runs,yes Atlanta,yes Charlotte,no New York wins by over 9.5 points
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 23:45 UTC | View → |
| Kalshi | — | — | — | 10:17 UTC | View → |
| Kalshi | — | — | — | 11:14 UTC | View → |
| Kalshi | — | — | — | 07:26 UTC | View → |
| Kalshi | — | — | — | 04:42 UTC | View → |
| Kalshi | — | — | — | 06:53 UTC | View → |
| Kalshi | — | — | — | 10:17 UTC | View → |
| Kalshi | — | — | — | 04:36 UTC | View → |
| Kalshi | — | — | — | 04:36 UTC | View → |
| Kalshi | — | — | — | 11:14 UTC | View → |
| Kalshi | — | — | — | 23:24 UTC | View → |
| Kalshi | — | — | — | 20:25 UTC | View → |
| Kalshi | — | — | — | 23:03 UTC | View → |
| Kalshi | — | — | — | 11:36 UTC | View → |
| Kalshi | — | — | — | 08:59 UTC | View → |
| Kalshi | — | — | — | 14:18 UTC | View → |
| Kalshi | — | — | — | 00:04 UTC | View → |
| Kalshi | — | — | — | 04:40 UTC | View → |
| Kalshi | — | — | — | 15:00 UTC | View → |
| Kalshi | — | — | — | 09:00 UTC | View → |
| Kalshi | — | — | — | 09:00 UTC | View → |
| Kalshi | — | — | — | 20:48 UTC | View → |
| Kalshi | — | — | — | 04:40 UTC | View → |
| Kalshi | — | — | — | 20:20 UTC | View → |
| Kalshi | — | — | — | 09:00 UTC | View → |
What do these odds mean?
Cross-platform data for yes Baltimore,no San Diego wins by over 1.5 runs,no New York M wins by over 3.5 runs,no Baltimore wins by over 3.5 runs,yes Atlanta,yes Charlotte,no New York wins by over 9.5 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.
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