How is the consensus probability calculated?

Consensus probability is the volume-weighted average YES probability across all matched markets on Polymarket, Kalshi, and Manifold. Markets without reported volume receive equal weight.

Why do prices differ between platforms?

Platforms have different fee structures, user bases, and liquidity pools, which creates small price differences even on the same event. Current spread: —.

Are prediction markets accurate?

Prediction markets have historically been well-calibrated for political and economic events, often outperforming traditional polling. Accuracy depends on liquidity and how close the market is to resolution.

yes Philadelphia,yes Miami,yes New York Y,yes Freddie Freeman: 1+

Q: What are prediction markets saying about yes Philadelphia,yes Miami,yes New York Y,yes Freddie Freeman: 1+?

As of the latest update, prediction markets are pricing yes Philadelphia,yes Miami,yes New York Y,yes Freddie Freeman: 1+ at — across 1 platforms (kalshi). Total volume: $0.

📅 Closes in 0 days

Platforms 1

Total Volume $0

Cross-platform spread —

Resolves 2026-04-15

Platform	Yes	No	Volume	Last seen	Link
Kalshi	—	—	—	14:19 UTC	View →
Kalshi	—	—	—	13:21 UTC	View →
Kalshi	—	—	—	15:19 UTC	View →
Kalshi	—	—	—	14:29 UTC	View →
Kalshi	—	—	—	06:10 UTC	View →
Kalshi	—	—	—	23:40 UTC	View →
Kalshi	—	—	—	16:02 UTC	View →
Kalshi	—	—	—	08:35 UTC	View →
Kalshi	—	—	—	22:09 UTC	View →
Kalshi	—	—	—	21:14 UTC	View →
Kalshi	—	—	—	10:02 UTC	View →
Kalshi	—	—	—	18:46 UTC	View →
Kalshi	—	—	—	11:42 UTC	View →
Kalshi	—	—	—	18:41 UTC	View →
Kalshi	—	—	—	23:17 UTC	View →
Kalshi	—	—	—	13:21 UTC	View →
Kalshi	—	—	—	18:23 UTC	View →
Kalshi	—	—	—	21:46 UTC	View →
Kalshi	—	—	—	22:31 UTC	View →
Kalshi	—	—	—	10:00 UTC	View →
Kalshi	—	—	—	22:29 UTC	View →
Kalshi	—	—	—	22:30 UTC	View →
Kalshi	—	—	—	14:19 UTC	View →
Kalshi	—	—	—	22:30 UTC	View →
Kalshi	—	—	—	21:30 UTC	View →
Kalshi	—	—	—	19:49 UTC	View →
Kalshi	—	—	—	17:04 UTC	View →
Kalshi	—	—	—	22:28 UTC	View →
Kalshi	—	—	—	06:05 UTC	View →
Kalshi	—	—	—	20:32 UTC	View →
Kalshi	—	—	—	12:22 UTC	View →
Kalshi	—	—	—	20:07 UTC	View →
Kalshi	—	—	—	14:28 UTC	View →
Kalshi	—	—	—	19:47 UTC	View →
Kalshi	—	—	—	17:02 UTC	View →
Kalshi	—	—	—	19:49 UTC	View →
Kalshi	—	—	—	18:53 UTC	View →
Kalshi	—	—	—	18:21 UTC	View →
Kalshi	—	—	—	22:35 UTC	View →
Kalshi	—	—	—	22:10 UTC	View →
Kalshi	—	—	—	12:22 UTC	View →
Kalshi	—	—	—	18:21 UTC	View →
Kalshi	—	—	—	22:31 UTC	View →
Kalshi	—	—	—	12:20 UTC	View →
Kalshi	—	—	—	14:27 UTC	View →
Kalshi	—	—	—	20:36 UTC	View →
Kalshi	—	—	—	22:45 UTC	View →
Kalshi	—	—	—	18:43 UTC	View →
Kalshi	—	—	—	16:22 UTC	View →
Kalshi	—	—	—	11:52 UTC	View →
Kalshi	—	—	—	18:21 UTC	View →
Kalshi	—	—	—	22:36 UTC	View →
Kalshi	—	—	—	18:21 UTC	View →
Kalshi	—	—	—	20:36 UTC	View →
Kalshi	—	—	—	20:33 UTC	View →
Kalshi	—	—	—	06:48 UTC	View →
Kalshi	—	—	—	16:03 UTC	View →
Kalshi	—	—	—	15:14 UTC	View →
Kalshi	—	—	—	20:12 UTC	View →
Kalshi	—	—	—	21:13 UTC	View →
Kalshi	—	—	—	20:35 UTC	View →
Kalshi	—	—	—	15:19 UTC	View →
Kalshi	—	—	—	18:43 UTC	View →
Kalshi	—	—	—	23:20 UTC	View →
Kalshi	—	—	—	23:20 UTC	View →
Kalshi	—	—	—	06:13 UTC	View →
Kalshi	—	—	—	21:31 UTC	View →
Kalshi	—	—	—	20:12 UTC	View →
Kalshi	—	—	—	22:08 UTC	View →
Kalshi	—	—	—	21:17 UTC	View →
Kalshi	—	—	—	23:36 UTC	View →
Kalshi	—	—	—	20:12 UTC	View →
Kalshi	—	—	—	18:59 UTC	View →
Kalshi	—	—	—	23:47 UTC	View →
Kalshi	—	—	—	14:19 UTC	View →
Kalshi	—	—	—	14:20 UTC	View →
Kalshi	—	—	—	11:09 UTC	View →
Kalshi	—	—	—	11:14 UTC	View →
Kalshi	—	—	—	17:35 UTC	View →
Kalshi	—	—	—	19:33 UTC	View →
Kalshi	—	—	—	16:01 UTC	View →
Kalshi	—	—	—	18:58 UTC	View →
Kalshi	—	—	—	00:24 UTC	View →
Kalshi	—	—	—	19:02 UTC	View →
Kalshi	—	—	—	17:12 UTC	View →
Kalshi	—	—	—	11:22 UTC	View →
Kalshi	—	—	—	09:58 UTC	View →
Kalshi	—	—	—	23:01 UTC	View →
Kalshi	—	—	—	17:44 UTC	View →
Kalshi	—	—	—	19:37 UTC	View →
Kalshi	—	—	—	22:36 UTC	View →
Kalshi	—	—	—	21:35 UTC	View →
Kalshi	—	—	—	23:40 UTC	View →
Kalshi	—	—	—	21:50 UTC	View →
Kalshi	—	—	—	22:37 UTC	View →
Kalshi	—	—	—	21:00 UTC	View →
Kalshi	—	—	—	00:48 UTC	View →
Kalshi	—	—	—	20:45 UTC	View →
Kalshi	—	—	—	23:50 UTC	View →
Kalshi	—	—	—	22:09 UTC	View →
Kalshi	—	—	—	23:16 UTC	View →
Kalshi	—	—	—	21:50 UTC	View →
Kalshi	—	—	—	14:05 UTC	View →
Kalshi	—	—	—	21:49 UTC	View →
Kalshi	—	—	—	19:37 UTC	View →
Kalshi	—	—	—	18:12 UTC	View →
Kalshi	—	—	—	18:33 UTC	View →
Kalshi	—	—	—	22:59 UTC	View →
Kalshi	—	—	—	21:46 UTC	View →
Kalshi	—	—	—	20:14 UTC	View →
Kalshi	—	—	—	22:09 UTC	View →
Kalshi	—	—	—	23:41 UTC	View →
Kalshi	—	—	—	01:01 UTC	View →
Kalshi	—	—	—	17:16 UTC	View →
Kalshi	—	—	—	22:43 UTC	View →
Kalshi	—	—	—	21:07 UTC	View →
Kalshi	—	—	—	21:20 UTC	View →
Kalshi	—	—	—	21:14 UTC	View →
Kalshi	—	—	—	20:59 UTC	View →
Kalshi	—	—	—	23:33 UTC	View →
Kalshi	—	—	—	00:18 UTC	View →
Kalshi	—	—	—	21:43 UTC	View →
Kalshi	—	—	—	22:35 UTC	View →
Kalshi	—	—	—	18:58 UTC	View →
Kalshi	—	—	—	21:49 UTC	View →
Kalshi	—	—	—	22:30 UTC	View →
Kalshi	—	—	—	07:15 UTC	View →
Kalshi	—	—	—	18:58 UTC	View →
Kalshi	—	—	—	20:50 UTC	View →
Kalshi	—	—	—	20:20 UTC	View →
Kalshi	—	—	—	10:44 UTC	View →
Kalshi	—	—	—	10:36 UTC	View →
Kalshi	—	—	—	00:27 UTC	View →
Kalshi	—	—	—	11:47 UTC	View →
Kalshi	—	—	—	11:09 UTC	View →
Kalshi	—	—	—	23:04 UTC	View →
Kalshi	—	—	—	14:04 UTC	View →
Kalshi	—	—	—	22:44 UTC	View →
Kalshi	—	—	—	20:20 UTC	View →
Kalshi	—	—	—	22:11 UTC	View →
Kalshi	—	—	—	22:23 UTC	View →
Kalshi	—	—	—	20:50 UTC	View →
Kalshi	—	—	—	22:06 UTC	View →
Kalshi	—	—	—	14:04 UTC	View →
Kalshi	—	—	—	23:50 UTC	View →
Kalshi	—	—	—	18:37 UTC	View →
Kalshi	—	—	—	07:15 UTC	View →
Kalshi	—	—	—	23:03 UTC	View →
Kalshi	—	—	—	20:50 UTC	View →
Kalshi	—	—	—	21:00 UTC	View →
Kalshi	—	—	—	09:59 UTC	View →
Kalshi	—	—	—	21:21 UTC	View →
Kalshi	—	—	—	23:44 UTC	View →
Kalshi	—	—	—	23:22 UTC	View →
Kalshi	—	—	—	00:27 UTC	View →
Kalshi	—	—	—	11:47 UTC	View →
Kalshi	—	—	—	21:33 UTC	View →
Kalshi	—	—	—	18:58 UTC	View →
Kalshi	—	—	—	20:50 UTC	View →
Kalshi	—	—	—	11:22 UTC	View →
Kalshi	—	—	—	07:15 UTC	View →

No history yet — first few snapshots pending.

Probability history, most recent 30 days

What do these odds mean?

Cross-platform data for yes Philadelphia,yes Miami,yes New York Y,yes Freddie Freeman: 1+ 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.

Last updated: 2026-04-12 11:14:24 UTC · Download JSON

News & context

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Your edge calculator

Enter your own probability estimate to see expected value and recommended position size using the Kelly Criterion.

Market price — Your estimate

50%

EV per $1: — · ¼ Kelly: — · Side: —