NHL Prediction Accuracy | Model Performance Explained

This page explains how to interpret our model's prediction accuracy metrics and provides historical evaluation context. Visit Model Performance for detailed results.

Historical Fit — Includes Training Data

All four cards use the same historical evaluation summary. These results include training data and are not an estimate of performance on unseen games. The export does not identify a model version or evaluation date range. Monthly report coverage: 2021-10-01 to 2026-11-01. Brier = probabilistic score (lower is better, 0.25 = 50% assigned to every game). MAE Total = mean absolute error on predicted total goals (the model's point estimate is the median, which MAE — not RMSE — is the consistent error metric for).

What the Metrics Mean

Accuracy

The fraction of games where the model correctly predicted the winner (the team with win probability >50%). A 50/50 baseline is useful for comparison, but an evaluation must identify its model version, date range, and whether forecasts preceded the games.

Brier Score

The Brier score measures probabilistic accuracy: it is the mean squared difference between the predicted probability and the binary outcome (1 = home win, 0 = home loss). A random model predicting 50% every game scores 0.25. Lower Brier scores are better. Compare models on the same held-out games and report the sample size.

Calibration

Calibration measures whether predicted probabilities match observed frequencies. If the model says 65% in 100 games, those teams should win about 65 of them. Production win probabilities use the model blend without a fitted calibration. Early in the season they are pulled toward the league's home-win rate, by half at first and not at all once teams have played about 60 games, because the model's early probabilities test as about twice too confident (in place since October 21, 2026). Calibration quality must be measured on unseen games. See the Performance page for calibration curves.

MAE (Totals)

Mean absolute error on predicted total goals (over/under). The model's point estimate is the median of its simulated total, and the median minimizes MAE (the mean minimizes RMSE), so MAE is the error metric consistent with how the point estimate is chosen. A perfect model would score 0. The cross-validation table below reports RMSE instead, because those folds score the mean (expected goals), where RMSE is the consistent metric.

Cross-Validation Results (3 folds)

Walk-forward cross-validation. Feature and upstream-data timing require separate audits. Avg Brier: 0.2545  |  Avg Log-loss: 0.7027  |  Avg RMSE (Totals): 2.408

Monthly Accuracy Trend

Win/loss prediction accuracy by calendar month. Larger samples = more stable estimates.

Monthly Breakdown

Evaluating Unseen Games

A forward test trains only on earlier games and freezes each forecast before its outcome is known. Historical fits and cross-validation folds are separate evaluations and should retain their own model versions, date ranges, and sample sizes.

No current-model forward accuracy estimate is supplied by this page's summary export. See Model Performance for the available dated reports.

Why Predictions Remain Uncertain

A probability describes uncertainty in an outcome. Even a strong favorite can lose. Sources of unpredictability include:

• Goalie performance variance (hot/cold streaks)
• Injuries and lineup uncertainty announced close to game time
• Puck luck (post hits, lucky bounces)
• Small sample sizes — 84-game seasons (82 before 2026–27) with nightly scheduling

We evaluate whether probabilities reflect uncertainty, even when no single prediction is guaranteed.

See also: Live Model Performance | Full Methodology | Today's Predictions