yes Philadelphia,yes Over 216.5 points scored
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 20:32 UTC | View → |
| Kalshi | — | — | — | 23:43 UTC | View → |
| Kalshi | — | — | — | 20:22 UTC | View → |
| Kalshi | — | — | — | 23:43 UTC | View → |
| Kalshi | — | — | — | 12:52 UTC | View → |
| Kalshi | — | — | — | 14:08 UTC | View → |
| Kalshi | — | — | — | 15:33 UTC | View → |
| Kalshi | — | — | — | 13:59 UTC | View → |
| Kalshi | — | — | — | 14:14 UTC | View → |
| Kalshi | — | — | — | 15:16 UTC | View → |
| Kalshi | — | — | — | 12:50 UTC | View → |
| Kalshi | — | — | — | 15:15 UTC | View → |
| Kalshi | — | — | — | 22:26 UTC | View → |
| Kalshi | — | — | — | 16:31 UTC | View → |
| Kalshi | — | — | — | 07:59 UTC | View → |
| Kalshi | — | — | — | 12:30 UTC | View → |
| Kalshi | — | — | — | 00:38 UTC | View → |
| Kalshi | — | — | — | 09:42 UTC | View → |
| Kalshi | — | — | — | 13:38 UTC | View → |
| Kalshi | — | — | — | 15:51 UTC | View → |
| Kalshi | — | — | — | 13:39 UTC | View → |
| Kalshi | — | — | — | 12:13 UTC | View → |
| Kalshi | — | — | — | 00:20 UTC | View → |
| Kalshi | — | — | — | 07:47 UTC | View → |
| Kalshi | — | — | — | 12:13 UTC | View → |
| Kalshi | — | — | — | 15:23 UTC | View → |
| Kalshi | — | — | — | 08:32 UTC | View → |
| Kalshi | — | — | — | 17:08 UTC | View → |
| Kalshi | — | — | — | 00:03 UTC | View → |
| Kalshi | — | — | — | 19:19 UTC | View → |
| Kalshi | — | — | — | 11:54 UTC | View → |
| Kalshi | — | — | — | 20:30 UTC | View → |
| Kalshi | — | — | — | 23:24 UTC | View → |
| Kalshi | — | — | — | 23:11 UTC | View → |
| Kalshi | — | — | — | 08:08 UTC | View → |
| Kalshi | — | — | — | 08:08 UTC | View → |
| Kalshi | — | — | — | 19:55 UTC | View → |
| Kalshi | — | — | — | 18:48 UTC | View → |
| Kalshi | — | — | — | 18:16 UTC | View → |
| Kalshi | — | — | — | 13:03 UTC | View → |
| Kalshi | — | — | — | 11:01 UTC | View → |
| Kalshi | — | — | — | 21:58 UTC | View → |
| Kalshi | — | — | — | 23:02 UTC | View → |
| Kalshi | — | — | — | 23:40 UTC | View → |
| Kalshi | — | — | — | 22:39 UTC | View → |
| Kalshi | — | — | — | 16:34 UTC | View → |
| Kalshi | — | — | — | 21:19 UTC | View → |
| Kalshi | — | — | — | 21:15 UTC | View → |
| Kalshi | — | — | — | 22:16 UTC | View → |
| Kalshi | — | — | — | 13:11 UTC | View → |
| Kalshi | — | — | — | 18:11 UTC | View → |
| Kalshi | — | — | — | 18:11 UTC | View → |
| Kalshi | — | — | — | 13:03 UTC | View → |
| Kalshi | — | — | — | 21:18 UTC | View → |
| Kalshi | — | — | — | 19:40 UTC | View → |
| Kalshi | — | — | — | 11:10 UTC | View → |
| Kalshi | — | — | — | 11:10 UTC | View → |
| Kalshi | — | — | — | 00:24 UTC | View → |
| Kalshi | — | — | — | 09:28 UTC | View → |
| Kalshi | — | — | — | 11:41 UTC | View → |
| Kalshi | — | — | — | 20:47 UTC | View → |
| Kalshi | — | — | — | 22:47 UTC | View → |
| Kalshi | — | — | — | 01:04 UTC | View → |
| Kalshi | — | — | — | 23:03 UTC | View → |
| Kalshi | — | — | — | 21:58 UTC | View → |
| Kalshi | — | — | — | 16:54 UTC | View → |
| Kalshi | — | — | — | 11:00 UTC | View → |
| Kalshi | — | — | — | 19:10 UTC | View → |
| Kalshi | — | — | — | 13:38 UTC | View → |
| Kalshi | — | — | — | 01:11 UTC | View → |
| Kalshi | — | — | — | 15:09 UTC | View → |
| Kalshi | — | — | — | 21:53 UTC | View → |
| Kalshi | — | — | — | 11:10 UTC | View → |
| Kalshi | — | — | — | 13:52 UTC | View → |
What do these odds mean?
Cross-platform data for yes Philadelphia,yes Over 216.5 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.