NHL Prediction Model Performance & Calibration

See how our NHL prediction model performs: accuracy, Brier scores, calibration, and error metrics across thousands of games.

To understand each metric, read Understanding Performance Metrics . To apply this in practice, view today's NHL predictions, our NHL playoff odds, and in-game win probability charts.

How to Read These Metrics

Accuracy: The percentage of games where the predicted winner (team with >50% win probability) actually won. Simple but incomplete — it ignores how confident the model was.
Brier Score: Measures the mean squared error of probability predictions (0–1 scale, lower is better). A coin-flip baseline yields 0.25; our model targets values below 0.24. Brier score rewards well-calibrated confidence levels, not just picking the right side.
Log Loss: A logarithmic scoring rule that heavily penalises confident wrong predictions. Assigning 90% to a team that loses costs far more than assigning 55%. This keeps the model honest about uncertainty.
Calibration: Shows whether stated probabilities match real outcomes. In a well-calibrated model, games given a 70% win probability should be won about 70% of the time. The calibration tables below group predictions into decile bins so you can verify this directly.
MAE / RMSE (Total Goals Error): Both measure how far predicted totals are from actual totals, in goal units. The game-level windows report MAE (mean absolute error) because the point estimate is the median of the simulated total, and the median minimizes MAE. The cross-validation folds report RMSE because they score the mean (expected goals), where RMSE is the consistent metric.

Why calibration matters most: For probabilistic predictions, calibration is more important than raw accuracy. A model that says "55%" every game can be 55% accurate but useless for decision-making. A well-calibrated model tells you how much to trust each prediction. Learn more in our analytics guide and methodology.

Game Predictions (Multi-Window)

Window	Start	End	Games	Accuracy	Brier	Log Loss	Avg Winner Prob	MAE Total
last 30	2026-03-17	2026-04-16	245	58.0%	0.2315	0.6538	53.6%	1.901
season to date	2025-10-01	2026-04-16	1312	56.3%	0.2416	0.6755	52.4%	1.845
multi season	2023-10-10	2026-04-16	3936	61.4%	0.2287	0.6484	53.6%	1.869

Totals (Over 5.5)

Window	Games	Accuracy	Brier	Log Loss	Avg Outcome Prob
last 30	245	57.6%	0.2452	0.6838	51.5%
season to date	1312	57.0%	0.2466	0.6864	51.1%
multi season	3936	55.8%	0.2484	0.6902	50.8%

Playoff Game Performance

Start	End	Games	Accuracy	Brier	Log Loss
2024-04-20	2026-06-14	256	59.8%	0.2358	0.6648

Current Matchups

CAR vs VGK model odds

Daily Performance

Show:

Date	Games	Accuracy	Brier	Log Loss

Prediction Recap Highlights

Definition: High confidence means the model assigned the predicted team a win probability well above 50%. Edges that hit are the highest-confidence correct calls, misses are the highest-confidence incorrect calls, and surprise results show the largest absolute gap between win probability and the actual outcome.

Show:

Biggest Model Edges That Hit

Date	Matchup	Win Prob	Outcome

Biggest Misses (High Confidence)

Date	Matchup	Win Prob	Outcome

Surprise Results

Date	Matchup	Surprise	Outcome

Calibration (Win Prob Deciles) — Last 30

Bin	Count	Mean Pred	Observed
1	1	14.8%	0.0%
2	3	25.1%	0.0%
3	17	36.8%	23.5%
4	64	45.4%	48.4%
5	82	54.6%	48.8%
6	48	64.7%	56.2%
7	25	74.0%	84.0%
8	5	83.9%	80.0%

Calibration (Win Prob Deciles) — Season To Date

Bin	Count	Mean Pred	Observed
1	2	16.4%	0.0%
2	12	26.4%	0.0%
3	86	36.3%	45.3%
4	330	45.5%	45.2%
5	475	54.8%	50.1%
6	273	64.5%	59.3%
7	112	74.2%	70.5%
8	22	82.0%	81.8%

Calibration (Win Prob Deciles) — Multi Season

Bin	Count	Mean Pred	Observed
1	4	17.8%	0.0%
2	60	26.6%	10.0%
3	299	36.3%	34.8%
4	965	45.6%	42.7%
5	1397	54.8%	53.7%
6	824	64.4%	66.4%
7	323	73.9%	79.3%
8	63	82.5%	90.5%
9	1	90.7%	100.0%

Calibration (Over 5.5) — Last 30

Bin	Count	Mean Pred	Observed
5	151	56.7%	60.3%
6	90	62.6%	53.3%
7	4	71.0%	50.0%

Calibration (Over 5.5) — Season To Date

Bin	Count	Mean Pred	Observed
4	22	48.2%	59.1%
5	941	56.4%	58.1%
6	343	62.6%	55.1%
7	6	71.4%	50.0%

Calibration (Over 5.5) — Multi Season

Bin	Count	Mean Pred	Observed
4	72	48.3%	59.7%
5	3041	56.1%	57.0%
6	813	62.3%	52.8%
7	10	70.9%	50.0%

Team Calibration (Home, Top 15 by Volume)

Team	Count	Mean Pred	Observed	Bias
EDM	148	62.5%	62.2%	+0.3%
CAR	147	71.0%	70.7%	+0.3%
DAL	146	56.6%	63.0%	-6.4%
FLA	146	56.8%	62.3%	-5.5%
VGK	143	60.5%	60.1%	+0.3%
COL	138	69.1%	66.7%	+2.5%
WPG	133	55.0%	62.4%	-7.5%
BOS	133	51.1%	56.4%	-5.3%
TOR	133	51.1%	54.1%	-3.0%
MTL	133	48.6%	49.6%	-1.0%
TBL	132	62.3%	62.1%	+0.2%
WSH	131	54.2%	58.8%	-4.6%
MIN	131	52.9%	52.7%	+0.3%
NYR	131	51.7%	51.9%	-0.2%
LAK	130	59.3%	53.8%	+5.4%

Team Calibration (Pred vs Observed)

Mean Pred Observed

Starter Calibration (Home)

Window	Starter Status	Games	Accuracy	Brier	Log Loss
last 30	Starter	245	58.0%	0.2315	0.6538
season to date	Starter	1312	56.3%	0.2416	0.6755
multi season	Unknown	18	83.3%	0.2115	0.6148
multi season	Starter	3918	61.3%	0.2288	0.6485

Cross-Validation (Expanding Window)

Summary: 3 folds | Brier: 0.2480 | Log Loss: 0.6894 | RMSE Total: 2.406

Show fold details

Fold	Train N	Val N	Brier	Log Loss	RMSE
Fold 1	701	2,097	0.2510	0.6957	2.446
Fold 2	1,399	1,399	0.2490	0.6914	2.390
Fold 3	2,103	695	0.2441	0.6813	2.382

In-Game Checkpoints — Last 30

Checkpoint	Games	Accuracy	Brier	Log Loss
end_p1	17	64.7%	0.2108	0.6250
end_p2	17	76.5%	0.1587	0.4810
ot_start	5	60.0%	0.2178	0.6143
p3_10	17	94.1%	0.0727	0.2360
p3_5	17	88.2%	0.0824	0.2520
pregame	17	41.2%	0.2552	0.7059

In-Game Checkpoints — Season To Date

Checkpoint	Games	Accuracy	Brier	Log Loss
end_p1	1394	65.8%	0.2111	0.6083
end_p2	1394	77.7%	0.1483	0.4510
ot_start	348	63.8%	0.1956	0.5583
p3_10	1394	84.0%	0.1033	0.3231
p3_5	1394	85.9%	0.0880	0.2766
pregame	1394	56.0%	0.2429	0.6786

In-Game Calibration — Pregame (Last 30 Days)

Bin	Count	Mean Pred	Observed
4	6	44.8%	50.0%
5	6	54.1%	16.7%
6	2	63.5%	50.0%
7	3	73.5%	66.7%

In-Game Calibration — End P2 (Last 30 Days)

Bin	Count	Mean Pred	Observed
0	4	8.4%	0.0%
1	2	11.1%	50.0%
2	1	28.8%	0.0%
3	1	35.6%	0.0%
4	2	44.0%	50.0%
5	1	52.2%	0.0%
6	1	64.0%	100.0%
7	1	78.8%	100.0%
8	2	83.8%	50.0%
9	2	98.4%	100.0%

In-Game Calibration — P3 10 (Last 30 Days)

Bin	Count	Mean Pred	Observed
0	5	2.6%	0.0%
1	3	16.6%	0.0%
2	1	25.5%	100.0%
3	1	37.5%	0.0%
4	1	48.4%	0.0%
5	1	55.6%	100.0%
8	1	89.1%	100.0%
9	4	96.7%	100.0%

xG Holdout — Contextual

Train: 2023-10-10 – 2025-12-27 | Test: 2025-12-28 – 2026-06-14

Games (test): 794 | Shots (test): 68188 | ROC AUC: 0.785 | Log Loss: 0.2211 | Brier: 0.0602

xG Splits — Contextual Strength State

Split	Shots	Goal Rate	AUC	Log Loss	Brier
Even	54578	6.3%	0.780	0.2022	0.0541
PP	11485	10.6%	0.723	0.2915	0.0807
PK	1491	7.2%	0.838	0.2132	0.0609
EmptyNet	634	50.3%	0.754	0.5979	0.2059

xG Splits — Contextual Shot Type

Split	Shots	Goal Rate	AUC	Log Loss	Brier
wrist	28569	7.2%	0.816	0.2060	0.0561
snap	17624	8.6%	0.774	0.2516	0.0711
slap	8178	4.8%	0.720	0.1788	0.0443
tip-in	6597	6.4%	0.666	0.2270	0.0582
backhand	5044	8.6%	0.818	0.2325	0.0644
deflected	1103	11.5%	0.702	0.3273	0.0951
wrap-around	407	5.4%	0.751	0.1820	0.0456
bat	357	7.8%	0.779	0.2346	0.0625
poke	217	8.8%	0.693	0.2704	0.0701
between-legs	48	12.5%	0.810	0.2950	0.0832
nan	37	59.5%	0.686	0.8924	0.2869
cradle	7	14.3%	1.000	0.2298	0.0636

xG Holdout — Neutral

Train: 2023-10-10 – 2025-12-27 | Test: 2025-12-28 – 2026-06-14

Games (test): 794 | Shots (test): 68188 | ROC AUC: 0.782 | Log Loss: 0.2246 | Brier: 0.0615

xG Splits — Neutral Strength State

Split	Shots	Goal Rate	AUC	Log Loss	Brier
Even	54578	6.3%	0.779	0.2026	0.0543
PP	11485	10.6%	0.703	0.3101	0.0881
PK	1491	7.2%	0.836	0.2131	0.0607
EmptyNet	634	50.3%	0.747	0.5975	0.2069

xG Splits — Neutral Shot Type

Split	Shots	Goal Rate	AUC	Log Loss	Brier
wrist	28569	7.2%	0.811	0.2099	0.0576
snap	17624	8.6%	0.770	0.2556	0.0726
slap	8178	4.8%	0.720	0.1789	0.0444
tip-in	6597	6.4%	0.661	0.2277	0.0584
backhand	5044	8.6%	0.804	0.2419	0.0680
deflected	1103	11.5%	0.705	0.3258	0.0946
wrap-around	407	5.4%	0.737	0.1868	0.0469
bat	357	7.8%	0.774	0.2388	0.0640
poke	217	8.8%	0.704	0.2674	0.0693
between-legs	48	12.5%	0.794	0.3017	0.0866
nan	37	59.5%	0.694	0.8424	0.2857
cradle	7	14.3%	1.000	0.2357	0.0685

Monthly Performance Trends

Track how model performance varies month-to-month across the season.

Month	Games	Accuracy	Brier	Log Loss
2023-10	140	60.7%	0.2206	0.6307
2023-11	213	62.9%	0.2291	0.6494
2023-12	219	63.5%	0.2241	0.6381
2024-01	208	60.6%	0.2221	0.6332
2024-02	172	66.3%	0.2195	0.6291
2024-03	228	69.3%	0.2089	0.6073
2024-04	132	61.4%	0.2253	0.6408
2024-10	166	76.5%	0.1943	0.5759
2024-11	220	62.7%	0.2209	0.6312
2024-12	214	70.6%	0.2077	0.6050
2025-01	224	59.4%	0.2306	0.6526
2025-02	122	53.3%	0.2496	0.6905
2025-03	234	65.0%	0.2275	0.6464
2025-04	132	56.8%	0.2463	0.6869
2025-10	180	57.8%	0.2395	0.6707
2025-11	225	53.8%	0.2397	0.6705
2025-12	226	54.9%	0.2501	0.6933
2026-01	240	54.6%	0.2486	0.6909
2026-02	74	67.6%	0.2221	0.6366
2026-03	242	54.5%	0.2453	0.6832
2026-04	125	61.6%	0.2237	0.6381

Playoff Model Performance

Game-level and series-level accuracy across playoff rounds.

Playoff Games

Round	Games	Accuracy	Brier	Log Loss
All Rounds	82	54.9%	0.2444	0.6818
Round 1	45	60.0%	0.2453	0.6838
Round 2	22	50.0%	0.2373	0.6673
Round 3	9	44.4%	0.2568	0.7073
Round 4	6	50.0%	0.2448	0.6826

Playoff Series

Round	Series	Accuracy	Brier	Log Loss
All Rounds	15	60.0%	0.2290	0.6502
Round 1	8	50.0%	0.2359	0.6645
Round 2	4	75.0%	0.2256	0.6437
Round 3	2	50.0%	0.2168	0.6230
Round 4	1	100.0%	0.2113	0.6156

Playoff Calibration (Pred vs Observed)

Mean Pred Observed