yes Brooklyn,yes Detroit,yes Los Angeles C
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 13:35 UTC | View → |
| Kalshi | — | — | — | 16:08 UTC | View → |
| Kalshi | — | — | — | 21:16 UTC | View → |
| Kalshi | — | — | — | 00:06 UTC | View → |
| Kalshi | — | — | — | 05:54 UTC | View → |
| Kalshi | — | — | — | 09:33 UTC | View → |
| Kalshi | — | — | — | 20:10 UTC | View → |
| Kalshi | — | — | — | 09:32 UTC | View → |
| Kalshi | — | — | — | 17:02 UTC | View → |
| Kalshi | — | — | — | 07:59 UTC | View → |
| Kalshi | — | — | — | 12:20 UTC | View → |
| Kalshi | — | — | — | 07:50 UTC | View → |
| Kalshi | — | — | — | 00:01 UTC | View → |
| Kalshi | — | — | — | 18:35 UTC | View → |
| Kalshi | — | — | — | 13:56 UTC | View → |
| Kalshi | — | — | — | 13:36 UTC | View → |
| Kalshi | — | — | — | 18:35 UTC | View → |
| Kalshi | — | — | — | 16:10 UTC | View → |
| Kalshi | — | — | — | 20:27 UTC | View → |
| Kalshi | — | — | — | 19:20 UTC | View → |
| Kalshi | — | — | — | 11:22 UTC | View → |
| Kalshi | — | — | — | 11:22 UTC | View → |
| Kalshi | — | — | — | 18:13 UTC | View → |
| Kalshi | — | — | — | 10:11 UTC | View → |
| Kalshi | — | — | — | 13:47 UTC | View → |
| Kalshi | — | — | — | 17:02 UTC | View → |
| Kalshi | — | — | — | 17:02 UTC | View → |
| Kalshi | — | — | — | 11:48 UTC | View → |
| Kalshi | — | — | — | 08:51 UTC | View → |
| Kalshi | — | — | — | 20:59 UTC | View → |
| Kalshi | — | — | — | 07:47 UTC | View → |
| Kalshi | — | — | — | 09:08 UTC | View → |
| Kalshi | — | — | — | 09:41 UTC | View → |
| Kalshi | — | — | — | 07:20 UTC | View → |
| Kalshi | — | — | — | 18:00 UTC | View → |
| Kalshi | — | — | — | 07:33 UTC | View → |
| Kalshi | — | — | — | 00:11 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 09:46 UTC | View → |
| Kalshi | — | — | — | 08:51 UTC | View → |
| Kalshi | — | — | — | 11:47 UTC | View → |
| Kalshi | — | — | — | 11:47 UTC | View → |
| Kalshi | — | — | — | 07:33 UTC | View → |
| Kalshi | — | — | — | 11:41 UTC | View → |
| Kalshi | — | — | — | 15:20 UTC | View → |
| Kalshi | — | — | — | 16:05 UTC | View → |
| Kalshi | — | — | — | 07:37 UTC | View → |
| Kalshi | — | — | — | 17:48 UTC | View → |
| Kalshi | — | — | — | 17:48 UTC | View → |
| Kalshi | — | — | — | 07:38 UTC | View → |
| Kalshi | — | — | — | 07:38 UTC | View → |
| Kalshi | — | — | — | 11:23 UTC | View → |
| Kalshi | — | — | — | 14:10 UTC | View → |
| Kalshi | — | — | — | 09:11 UTC | View → |
| Kalshi | — | — | — | 18:30 UTC | View → |
| Kalshi | — | — | — | 16:02 UTC | View → |
| Kalshi | — | — | — | 18:01 UTC | View → |
| Kalshi | — | — | — | 09:53 UTC | View → |
| Kalshi | — | — | — | 17:19 UTC | View → |
| Kalshi | — | — | — | 07:33 UTC | View → |
| Kalshi | — | — | — | 11:02 UTC | View → |
| Kalshi | — | — | — | 11:02 UTC | View → |
| Kalshi | — | — | — | 07:53 UTC | View → |
| Kalshi | — | — | — | 12:40 UTC | View → |
| Kalshi | — | — | — | 08:51 UTC | View → |
| Kalshi | — | — | — | 20:29 UTC | View → |
| Kalshi | — | — | — | 17:19 UTC | View → |
| Kalshi | — | — | — | 07:56 UTC | View → |
| Kalshi | — | — | — | 07:37 UTC | View → |
| Kalshi | — | — | — | 17:02 UTC | View → |
| Kalshi | — | — | — | 14:39 UTC | View → |
| Kalshi | — | — | — | 19:48 UTC | View → |
| Kalshi | — | — | — | 08:51 UTC | View → |
What do these odds mean?
Cross-platform data for yes Brooklyn,yes Detroit,yes Los Angeles C 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.