yes Brooklyn,yes Minnesota,yes Oklahoma City
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 07:22 UTC | View → |
| Kalshi | — | — | — | 23:00 UTC | View → |
| Kalshi | — | — | — | 13:19 UTC | View → |
| Kalshi | — | — | — | 20:36 UTC | View → |
| Kalshi | — | — | — | 13:59 UTC | View → |
| Kalshi | — | — | — | 07:58 UTC | View → |
| Kalshi | — | — | — | 21:10 UTC | View → |
| Kalshi | — | — | — | 15:20 UTC | View → |
| Kalshi | — | — | — | 07:20 UTC | View → |
| Kalshi | — | — | — | 07:59 UTC | View → |
| Kalshi | — | — | — | 07:29 UTC | View → |
| Kalshi | — | — | — | 13:23 UTC | View → |
| Kalshi | — | — | — | 15:51 UTC | View → |
| Kalshi | — | — | — | 07:29 UTC | View → |
| Kalshi | — | — | — | 10:35 UTC | View → |
| Kalshi | — | — | — | 00:14 UTC | View → |
| Kalshi | — | — | — | 14:00 UTC | View → |
| Kalshi | — | — | — | 07:26 UTC | View → |
| Kalshi | — | — | — | 07:21 UTC | View → |
| Kalshi | — | — | — | 07:55 UTC | View → |
| Kalshi | — | — | — | 07:43 UTC | View → |
| Kalshi | — | — | — | 20:40 UTC | View → |
| Kalshi | — | — | — | 10:38 UTC | View → |
| Kalshi | — | — | — | 07:33 UTC | View → |
| Kalshi | — | — | — | 08:42 UTC | View → |
| Kalshi | — | — | — | 07:08 UTC | View → |
| Kalshi | — | — | — | 07:43 UTC | View → |
| Kalshi | — | — | — | 08:41 UTC | View → |
| Kalshi | — | — | — | 00:14 UTC | View → |
| Kalshi | — | — | — | 20:52 UTC | View → |
| Kalshi | — | — | — | 14:43 UTC | View → |
| Kalshi | — | — | — | 07:48 UTC | View → |
| Kalshi | — | — | — | 18:22 UTC | View → |
| Kalshi | — | — | — | 12:24 UTC | View → |
| Kalshi | — | — | — | 10:38 UTC | View → |
| Kalshi | — | — | — | 09:38 UTC | View → |
| Kalshi | — | — | — | 07:55 UTC | View → |
| Kalshi | — | — | — | 08:51 UTC | View → |
| Kalshi | — | — | — | 07:54 UTC | View → |
| Kalshi | — | — | — | 23:46 UTC | View → |
| Kalshi | — | — | — | 08:35 UTC | View → |
| Kalshi | — | — | — | 07:20 UTC | View → |
What do these odds mean?
Cross-platform data for yes Brooklyn,yes Minnesota,yes Oklahoma City 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.