A Kalshi price is a claimed probability. This page checks the claim: we record market prices daily, watch those markets settle, and measure how often each price level actually came true. No model, no opinion, just settled outcomes against the price roughly 24 hours before close.
Based on 29,334 settled markets matched to our recorded price history. This dataset grows every day.
Measured this week
Kalshi markets priced 10–19¢ settled YES 15.3% of the time.
That's almost exactly what the price implied, measured across 2,910 settled markets at our recorded pre-event prices. Source: contracttax.com/calibration
All markets
PriceActual YES rateActualImpliedn
0–9¢
6.5%5%2,304
10–19¢
15.3%15%2,910
20–29¢
25.6%25%3,320
30–39¢
35.0%35%3,577
40–49¢
43.8%45%4,634
50–59¢
56.5%55%4,085
60–69¢
66.4%65%2,863
70–79¢
76.6%75%2,120
80–89¢
85.8%85%1,701
90–99¢
93.8%95%1,820
White tick = what the price implies. Bar = what actually happened. Green bars beat their price; red bars fell short.
Sports · 22,405 settled
PriceActual YES rateActualImpliedn
0–9¢
6.5%5%1,865
10–19¢
15.3%15%2,431
20–29¢
26.0%25%2,698
30–39¢
35.5%35%2,772
40–49¢
43.7%45%3,228
50–59¢
56.7%55%2,890
60–69¢
68.4%65%1,999
70–79¢
77.8%75%1,638
80–89¢
87.3%85%1,367
90–99¢
94.6%95%1,517
Esports · 3,809 settled
PriceActual YES rateActualImpliedn
0–9¢
11.4%5%35
10–19¢
17.9%15%156
20–29¢
28.0%25%275
30–39¢
33.9%35%513
40–49¢
41.4%45%982
50–59¢
54.4%55%747
60–69¢
60.9%65%548
70–79¢
71.8%75%301
80–89¢
81.0%85%189
90–99¢
84.1%95%63
Economics · 504 settled
PriceActual YES rateActualImpliedn
0–9¢
8.7%5%69
10–19¢
30.0%15%20
20–29¢
30.8%25%39
30–39¢
53.3%35%45
40–49¢
64.5%45%62
50–59¢
75.8%55%66
60–69¢
75.9%65%54
70–79¢
87.2%75%47
80–89¢
76.1%85%46
90–99¢
83.9%95%56
Crypto · 290 settled
PriceActual YES rateActualImpliedn
0–9¢
5.3%5%57
10–19¢
5.0%15%40
20–29¢
32.1%25%28
30–39¢
15.4%35%26
40–49¢
36.0%45%25
50–59¢
33.3%55%15
60–69¢
42.9%65%21
70–79¢
42.9%75%21
80–89¢
57.1%85%21
90–99¢
88.9%95%36
Politics · 66 settled
PriceActual YES rateActualImpliedn
0–9¢
8.3%5%12
90–99¢
90.9%95%11
Data integrity check
Extreme-bucket sample share and accuracy are consistent between the 24-hour anchor and a strictly older 72-hour anchor (share gap -0.3 pts, accuracy gap 1.0 pts). No contamination signature detected.
Methodology, honestly
For each settled market we take our recorded price nearest to 24 hours before the event, anchored on the earlier of the market’s close time and its expected expiration, because Kalshi frequently extends close times days past the actual event, and sampling after the outcome is known would fake perfect calibration. We accept snapshots from 2 to 48 hours out, bucket by decile, and compare each bucket’s implied probability to the share that settled YES. Our snapshots cover the higher-volume end of the board, so results describe the markets people actually trade, and thin buckets are hidden rather than shown with false confidence.
Why it matters: betting research has long documented a favorite-longshot bias, favorites slightly underpriced, longshots overpriced. Whether and where that holds on Kalshi is an empirical question, and this page is the running answer, category by category. Past calibration is not a guarantee about any single market. Its sibling, the Momentum Machine, measures what changing prices mean the way this page measures standing ones. Not financial advice.
Does the crowd learn anything before the event?
The curve above is anchored on a price recorded about a day before each event. We also hold prices recorded up to two weeks out, which is a much larger sample (29,884 observations against 29,334) and genuinely weaker evidence: a forecast made two weeks early is a harder one. Comparing them answers a question nobody has measured on this exchange: does a Kalshi price actually get better as the event approaches?
Across the bands where both samples are real, the two curves settle within 0.1 points of each other. A price two weeks out is about as good as a price a day out.
Band
A day out
Up to 2 weeks out
Difference
0–9¢
6.5% n=2,304
6.4% n=2,380
+0.1
10–19¢
15.3% n=2,910
15.3% n=2,967
+0.0
20–29¢
25.6% n=3,320
25.6% n=3,368
-0.0
30–39¢
35.0% n=3,577
35.1% n=3,619
-0.1
40–49¢
43.8% n=4,634
44.0% n=4,736
-0.1
50–59¢
56.5% n=4,085
56.4% n=4,160
+0.1
60–69¢
66.4% n=2,863
66.3% n=2,887
+0.0
70–79¢
76.6% n=2,120
76.6% n=2,153
+0.0
80–89¢
85.8% n=1,701
85.9% n=1,741
-0.0
90–99¢
93.8% n=1,820
93.9% n=1,873
-0.0
Only bands where both anchors have at least 30 settled markets are compared. The headline curve above, and every number we publish as fact, uses the day-out anchor. The wider one exists so that categories with a thin day-out record can still say something, clearly labelled as weaker evidence, rather than saying nothing.
Accuracy by category
The curve above is every market averaged together, and that hides the most useful thing in this dataset: Kalshi is a different forecaster depending on what it is forecasting. A crowd pricing a Fed decision and a crowd pricing an awards show are not the same crowd, and they are not equally good. Here is each category measured on its own.