yes New York Y,yes LA Kings
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 21:38 UTC | View → |
| Kalshi | — | — | — | 20:17 UTC | View → |
| Kalshi | — | — | — | 06:58 UTC | View → |
| Kalshi | — | — | — | 13:41 UTC | View → |
| Kalshi | — | — | — | 09:14 UTC | View → |
| Kalshi | — | — | — | 01:45 UTC | View → |
| Kalshi | — | — | — | 12:30 UTC | View → |
| Kalshi | — | — | — | 21:06 UTC | View → |
| Kalshi | — | — | — | 08:20 UTC | View → |
| Kalshi | — | — | — | 14:07 UTC | View → |
| Kalshi | — | — | — | 23:14 UTC | View → |
| Kalshi | — | — | — | 23:20 UTC | View → |
| Kalshi | — | — | — | 23:29 UTC | View → |
| Kalshi | — | — | — | 08:20 UTC | View → |
| Kalshi | — | — | — | 01:45 UTC | View → |
| Kalshi | — | — | — | 09:43 UTC | View → |
| Kalshi | — | — | — | 09:43 UTC | View → |
| Kalshi | — | — | — | 17:03 UTC | View → |
| Kalshi | — | — | — | 07:28 UTC | View → |
| Kalshi | — | — | — | 16:39 UTC | View → |
| Kalshi | — | — | — | 07:27 UTC | View → |
| Kalshi | — | — | — | 11:28 UTC | View → |
| Kalshi | — | — | — | 08:24 UTC | View → |
| Kalshi | — | — | — | 06:58 UTC | View → |
| Kalshi | — | — | — | 06:58 UTC | View → |
| Kalshi | — | — | — | 18:03 UTC | View → |
| Kalshi | — | — | — | 08:17 UTC | View → |
| Kalshi | — | — | — | 08:13 UTC | View → |
| Kalshi | — | — | — | 04:43 UTC | View → |
| Kalshi | — | — | — | 07:24 UTC | View → |
| Kalshi | — | — | — | 11:28 UTC | View → |
| Kalshi | — | — | — | 09:14 UTC | View → |
| Kalshi | — | — | — | 04:42 UTC | View → |
| Kalshi | — | — | — | 23:23 UTC | View → |
| Kalshi | — | — | — | 02:24 UTC | View → |
| Kalshi | — | — | — | 04:42 UTC | View → |
| Kalshi | — | — | — | 04:58 UTC | View → |
| Kalshi | — | — | — | 01:19 UTC | View → |
| Kalshi | — | — | — | 19:09 UTC | View → |
| Kalshi | — | — | — | 13:34 UTC | View → |
| Kalshi | — | — | — | 19:37 UTC | View → |
| Kalshi | — | — | — | 15:56 UTC | View → |
| Kalshi | — | — | — | 10:15 UTC | View → |
| Kalshi | — | — | — | 23:10 UTC | View → |
| Kalshi | — | — | — | 04:48 UTC | View → |
| Kalshi | — | — | — | 08:00 UTC | View → |
| Kalshi | — | — | — | 09:01 UTC | View → |
| Kalshi | — | — | — | 21:57 UTC | View → |
| Kalshi | — | — | — | 12:29 UTC | View → |
| Kalshi | — | — | — | 14:22 UTC | View → |
| Kalshi | — | — | — | 14:20 UTC | View → |
| Kalshi | — | — | — | 14:19 UTC | View → |
| Kalshi | — | — | — | 10:12 UTC | View → |
| Kalshi | — | — | — | 15:48 UTC | View → |
| Kalshi | — | — | — | 09:03 UTC | View → |
| Kalshi | — | — | — | 06:14 UTC | View → |
| Kalshi | — | — | — | 14:20 UTC | View → |
| Kalshi | — | — | — | 07:55 UTC | View → |
| Kalshi | — | — | — | 10:26 UTC | View → |
| Kalshi | — | — | — | 16:27 UTC | View → |
| Kalshi | — | — | — | 10:33 UTC | View → |
| Kalshi | — | — | — | 09:10 UTC | View → |
| Kalshi | — | — | — | 00:12 UTC | View → |
| Kalshi | — | — | — | 20:34 UTC | View → |
| Kalshi | — | — | — | 22:23 UTC | View → |
| Kalshi | — | — | — | 09:32 UTC | View → |
| Kalshi | — | — | — | 23:52 UTC | View → |
| Kalshi | — | — | — | 22:22 UTC | View → |
| Kalshi | — | — | — | 01:48 UTC | View → |
| Kalshi | — | — | — | 09:06 UTC | View → |
| Kalshi | — | — | — | 09:07 UTC | View → |
| Kalshi | — | — | — | 17:41 UTC | View → |
What do these odds mean?
Cross-platform data for yes New York Y,yes LA Kings 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.