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Historical Evaluation
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Forecast Calibration
Probability Calibration
A well-calibrated model assigns probabilities that match observed frequencies. Bins close to the dashed reference line indicate good calibration.
Perfect calibration (y = x)Observed bins (size = sample count)
Calibration Error
0.0603
Lower is better
Forecast Sharpness
1.2369
Lower is better
vs Uniform Baseline
—
Baseline not provided
Sample size: 2250 observations
Note: Only Round 4 of 2026 has actual results; no multi-season historical (predicted, observed) pairs are available in-repo. Isotonic calibration requires materially more data (target: 2023+2024+2025 backfilled) before calibration.applied can be set to true. Until then, exported probabilities are raw Plackett-Luce Monte Carlo outputs.
Generated: 5/27/2026, 11:20:13 AM