yes James Harden: 1+,yes Kawhi Leonard: 1+,yes San Antonio,yes Detroit,yes Miami,yes Houston,yes Boston,yes Orlando,yes New York,yes Scottie Barnes: 10+,no Over 249.5 points scored
| Platform | Yes | No | Volume | Last seen | Link |
|---|---|---|---|---|---|
| Kalshi | — | — | — | 05:23 UTC | View → |
| Kalshi | — | — | — | 14:11 UTC | View → |
| Kalshi | — | — | — | 07:20 UTC | View → |
| Kalshi | — | — | — | 14:10 UTC | View → |
| Kalshi | — | — | — | 13:41 UTC | View → |
| Kalshi | — | — | — | 13:42 UTC | View → |
| Kalshi | — | — | — | 06:48 UTC | View → |
| Kalshi | — | — | — | 13:41 UTC | View → |
| Kalshi | — | — | — | 11:41 UTC | View → |
| Kalshi | — | — | — | 06:53 UTC | View → |
| Kalshi | — | — | — | 06:01 UTC | View → |
| Kalshi | — | — | — | 06:47 UTC | View → |
| Kalshi | — | — | — | 05:47 UTC | View → |
| Kalshi | — | — | — | 20:11 UTC | View → |
| Kalshi | — | — | — | 20:14 UTC | View → |
| Kalshi | — | — | — | 18:17 UTC | View → |
| Kalshi | — | — | — | 21:26 UTC | View → |
| Kalshi | — | — | — | 06:41 UTC | View → |
| Kalshi | — | — | — | 20:04 UTC | View → |
| Kalshi | — | — | — | 15:23 UTC | View → |
| Kalshi | — | — | — | 22:30 UTC | View → |
| Kalshi | — | — | — | 13:45 UTC | View → |
| Kalshi | — | — | — | 09:59 UTC | View → |
| Kalshi | — | — | — | 19:03 UTC | View → |
| Kalshi | — | — | — | 11:39 UTC | View → |
| Kalshi | — | — | — | 15:51 UTC | View → |
| Kalshi | — | — | — | 09:58 UTC | View → |
| Kalshi | — | — | — | 18:52 UTC | View → |
| Kalshi | — | — | — | 06:46 UTC | View → |
| Kalshi | — | — | — | 10:04 UTC | View → |
| Kalshi | — | — | — | 10:05 UTC | View → |
| Kalshi | — | — | — | 09:41 UTC | View → |
| Kalshi | — | — | — | 15:04 UTC | View → |
| Kalshi | — | — | — | 16:20 UTC | View → |
| Kalshi | — | — | — | 09:02 UTC | View → |
| Kalshi | — | — | — | 16:54 UTC | View → |
| Kalshi | — | — | — | 10:32 UTC | View → |
| Kalshi | — | — | — | 07:10 UTC | View → |
| Kalshi | — | — | — | 21:48 UTC | View → |
| Kalshi | — | — | — | 20:18 UTC | View → |
| Kalshi | — | — | — | 11:39 UTC | View → |
| Kalshi | — | — | — | 09:59 UTC | View → |
| Kalshi | — | — | — | 06:24 UTC | View → |
| Kalshi | — | — | — | 12:05 UTC | View → |
| Kalshi | — | — | — | 11:39 UTC | View → |
| Kalshi | — | — | — | 22:39 UTC | View → |
| Kalshi | — | — | — | 10:27 UTC | View → |
| Kalshi | — | — | — | 11:39 UTC | View → |
| Kalshi | — | — | — | 08:46 UTC | View → |
| Kalshi | — | — | — | 16:54 UTC | View → |
| Kalshi | — | — | — | 16:16 UTC | View → |
| Kalshi | — | — | — | 08:22 UTC | View → |
| Kalshi | — | — | — | 07:02 UTC | View → |
| Kalshi | — | — | — | 10:02 UTC | View → |
| Kalshi | — | — | — | 09:02 UTC | View → |
| Kalshi | — | — | — | 09:59 UTC | View → |
| Kalshi | — | — | — | 09:59 UTC | View → |
| Kalshi | — | — | — | 21:48 UTC | View → |
| Kalshi | — | — | — | 06:01 UTC | View → |
| Kalshi | — | — | — | 09:08 UTC | View → |
| Kalshi | — | — | — | 07:04 UTC | View → |
| Kalshi | — | — | — | 05:40 UTC | View → |
| Kalshi | — | — | — | 06:02 UTC | View → |
| Kalshi | — | — | — | 15:51 UTC | View → |
| Kalshi | — | — | — | 07:06 UTC | View → |
| Kalshi | — | — | — | 23:04 UTC | View → |
| Kalshi | — | — | — | 10:02 UTC | View → |
| Kalshi | — | — | — | 06:57 UTC | View → |
| Kalshi | — | — | — | 15:02 UTC | View → |
| Kalshi | — | — | — | 09:59 UTC | View → |
| Kalshi | — | — | — | 16:58 UTC | View → |
| Kalshi | — | — | — | 09:01 UTC | View → |
What do these odds mean?
Cross-platform data for yes James Harden: 1+,yes Kawhi Leonard: 1+,yes San Antonio,yes Detroit,yes Miami,yes Houston,yes Boston,yes Orlando,yes New York,yes Scottie Barnes: 10+,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.