month stringdate 2026-01-01 00:00:00 2026-03-01 00:00:00 | count int64 1.05k 52.9k | mean_brier float64 0 0.02 | mean_log_loss float64 0.02 0.06 | by_venue listlengths 1 1 | by_category listlengths 10 10 | calibration_buckets listlengths 10 10 | filter dict | generated_at stringclasses 3
values |
|---|---|---|---|---|---|---|---|---|
2026-01 | 1,045 | 0.0027 | 0.0215 | [
{
"venue": "kalshi",
"count": 1045,
"mean_brier": 0.0027,
"mean_log_loss": 0.0215
}
] | [
{
"category": "Entertainment",
"count": 410,
"mean_brier": 0.0005
},
{
"category": "Sports",
"count": 278,
"mean_brier": 0.0006
},
{
"category": "Mentions",
"count": 158,
"mean_brier": 0.004
},
{
"category": "Companies",
"count": 86,
"mean_brier": 0.0074
... | [
{
"bucket_lo": 0,
"bucket_hi": 0.1,
"count": 721,
"actual_rate": 0
},
{
"bucket_lo": 0.1,
"bucket_hi": 0.2,
"count": 10,
"actual_rate": 0
},
{
"bucket_lo": 0.2,
"bucket_hi": 0.3,
"count": 2,
"actual_rate": 0.5
},
{
"bucket_lo": 0.3,
"bucket_hi": 0.... | {
"min_volume": 100
} | 2026-05-10T06:50:44.687Z |
2026-02 | 1,348 | 0.0039 | 0.0271 | [
{
"venue": "kalshi",
"count": 1348,
"mean_brier": 0.0039,
"mean_log_loss": 0.0271
}
] | [
{
"category": "Mentions",
"count": 534,
"mean_brier": 0.0002
},
{
"category": "Entertainment",
"count": 238,
"mean_brier": 0.012
},
{
"category": "Politics",
"count": 166,
"mean_brier": 0.0021
},
{
"category": "Sports",
"count": 122,
"mean_brier": 0.0088
... | [
{
"bucket_lo": 0,
"bucket_hi": 0.1,
"count": 759,
"actual_rate": 0
},
{
"bucket_lo": 0.1,
"bucket_hi": 0.2,
"count": 17,
"actual_rate": 0
},
{
"bucket_lo": 0.2,
"bucket_hi": 0.3,
"count": 10,
"actual_rate": 0
},
{
"bucket_lo": 0.3,
"bucket_hi": 0.4... | {
"min_volume": 100
} | 2026-05-10T06:50:43.531Z |
2026-03 | 52,881 | 0.0154 | 0.063 | [
{
"venue": "kalshi",
"count": 52881,
"mean_brier": 0.0154,
"mean_log_loss": 0.063
}
] | [
{
"category": "Sports",
"count": 29819,
"mean_brier": 0.0171
},
{
"category": "Crypto",
"count": 14951,
"mean_brier": 0.0115
},
{
"category": "Mentions",
"count": 2590,
"mean_brier": 0.0025
},
{
"category": "Financials",
"count": 2058,
"mean_brier": 0.0441... | [
{
"bucket_lo": 0,
"bucket_hi": 0.1,
"count": 27332,
"actual_rate": 0.0022
},
{
"bucket_lo": 0.1,
"bucket_hi": 0.2,
"count": 1343,
"actual_rate": 0.0246
},
{
"bucket_lo": 0.2,
"bucket_hi": 0.3,
"count": 693,
"actual_rate": 0.0505
},
{
"bucket_lo": 0.3,
... | {
"min_volume": 100
} | 2026-05-10T06:50:42.612Z |
Prediction Market Calibration Scorecards
Monthly Brier + log-loss calibration breakdowns for Kalshi + Polymarket. Each month provides mean Brier, mean log-loss, per-venue and per-category breakdowns, and a 10-bucket calibration histogram (actual vs predicted). Published with a 14-day delay after month-end to capture late resolutions.
License and Use
This dataset is released under Creative Commons Attribution 4.0 International (CC-BY-4.0; https://creativecommons.org/licenses/by/4.0/). You may use it freely for personal, research, educational, and commercial purposes — including training, evaluating, and fine-tuning machine-learning models. Attribution is required when the dataset is redistributed in substantially its original form or cited in published work; credit as "SimpleFunctions (simplefunctions.dev)".
Additional terms apply: you may not re-host this dataset, in whole or in substantial part, as an API or service that functionally substitutes for a SimpleFunctions endpoint. See Terms §13.2 at https://simplefunctions.dev/terms.
Provenance, update cadence, and schema are documented below.
Update cadence
Monthly (14-day delay).
Provenance
Source: https://simplefunctions.dev Generator: SimpleFunctions public data pipeline Contact: patrick@simplefunctions.dev
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