yes Detroit,yes Miami,yes Over 208.5 points scored,no Over 249.5 points scored
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 04:33 UTC | View → |
| Kalshi | — | — | — | 09:13 UTC | View → |
| Kalshi | — | — | — | 13:55 UTC | View → |
| Kalshi | — | — | — | 09:27 UTC | View → |
| Kalshi | — | — | — | 09:08 UTC | View → |
| Kalshi | — | — | — | 10:57 UTC | View → |
| Kalshi | — | — | — | 10:57 UTC | View → |
| Kalshi | — | — | — | 19:39 UTC | View → |
| Kalshi | — | — | — | 09:49 UTC | View → |
| Kalshi | — | — | — | 04:31 UTC | View → |
| Kalshi | — | — | — | 06:12 UTC | View → |
| Kalshi | — | — | — | 12:33 UTC | View → |
| Kalshi | — | — | — | 04:27 UTC | View → |
| Kalshi | — | — | — | 16:36 UTC | View → |
| Kalshi | — | — | — | 12:25 UTC | View → |
| Kalshi | — | — | — | 10:56 UTC | View → |
| Kalshi | — | — | — | 17:04 UTC | View → |
| Kalshi | — | — | — | 07:59 UTC | View → |
| Kalshi | — | — | — | 09:37 UTC | View → |
| Kalshi | — | — | — | 09:00 UTC | View → |
| Kalshi | — | — | — | 20:20 UTC | View → |
| Kalshi | — | — | — | 09:27 UTC | View → |
| Kalshi | — | — | — | 09:27 UTC | View → |
| Kalshi | — | — | — | 04:23 UTC | View → |
| Kalshi | — | — | — | 09:38 UTC | View → |
| Kalshi | — | — | — | 07:10 UTC | View → |
| Kalshi | — | — | — | 12:09 UTC | View → |
| Kalshi | — | — | — | 12:09 UTC | View → |
| Kalshi | — | — | — | 05:24 UTC | View → |
| Kalshi | — | — | — | 09:49 UTC | View → |
| Kalshi | — | — | — | 04:50 UTC | View → |
| Kalshi | — | — | — | 07:01 UTC | View → |
| Kalshi | — | — | — | 07:01 UTC | View → |
| Kalshi | — | — | — | 23:27 UTC | View → |
| Kalshi | — | — | — | 00:24 UTC | View → |
| Kalshi | — | — | — | 21:05 UTC | View → |
| Kalshi | — | — | — | 10:40 UTC | View → |
| Kalshi | — | — | — | 10:40 UTC | View → |
| Kalshi | — | — | — | 00:09 UTC | View → |
| Kalshi | — | — | — | 10:26 UTC | View → |
| Kalshi | — | — | — | 05:01 UTC | View → |
| Kalshi | — | — | — | 21:51 UTC | View → |
| Kalshi | — | — | — | 04:32 UTC | View → |
| Kalshi | — | — | — | 06:46 UTC | View → |
| Kalshi | — | — | — | 07:12 UTC | View → |
| Kalshi | — | — | — | 07:12 UTC | View → |
| Kalshi | — | — | — | 07:08 UTC | View → |
| Kalshi | — | — | — | 04:35 UTC | View → |
| Kalshi | — | — | — | 07:22 UTC | View → |
| Kalshi | — | — | — | 20:05 UTC | View → |
| Kalshi | — | — | — | 04:30 UTC | View → |
| Kalshi | — | — | — | 16:47 UTC | View → |
| Kalshi | — | — | — | 14:26 UTC | View → |
| Kalshi | — | — | — | 16:24 UTC | View → |
| Kalshi | — | — | — | 04:52 UTC | View → |
| Kalshi | — | — | — | 11:24 UTC | View → |
| Kalshi | — | — | — | 16:53 UTC | View → |
| Kalshi | — | — | — | 04:35 UTC | View → |
| Kalshi | — | — | — | 04:46 UTC | View → |
| Kalshi | — | — | — | 20:22 UTC | View → |
| Kalshi | — | — | — | 18:53 UTC | View → |
| Kalshi | — | — | — | 08:50 UTC | View → |
| Kalshi | — | — | — | 09:14 UTC | View → |
| Kalshi | — | — | — | 05:22 UTC | View → |
| Kalshi | — | — | — | 01:06 UTC | View → |
| Kalshi | — | — | — | 15:07 UTC | View → |
| Kalshi | — | — | — | 13:51 UTC | View → |
| Kalshi | — | — | — | 05:30 UTC | View → |
| Kalshi | — | — | — | 05:11 UTC | View → |
| Kalshi | — | — | — | 20:06 UTC | View → |
| Kalshi | — | — | — | 07:50 UTC | View → |
| Kalshi | — | — | — | 00:29 UTC | View → |
| Kalshi | — | — | — | 23:40 UTC | View → |
What do these odds mean?
Cross-platform data for yes Detroit,yes Miami,yes Over 208.5 points scored,no Over 249.5 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.