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2026 WNBA Model Performance Analysis

Scope
All Games Ranked Games

All scored games in the selected league and season. AP Poll is excluded here.

Season
2026 2025

Comparing prediction accuracy across 38 games using multiple rating models.

Model Catalog

7-day holdout coverage: 16/17 models .

Rolling Holdout Curves

Each point is a strict weekly holdout: train on all games before that week, test on that week. This first version uses a 21-day warmup, then 7-day holdouts stepped forward weekly.

Log Loss Brier AUC Accuracy

Weekly strict holdout log loss. Lower is better. Showing 16 models across 2 windows. Click legend items to hide/show series.

Recent Window Winners

Holdout Best Log Loss Runner-up Models
May 29 - Jun 3 Dynamic Bradley-Terry 0.586 Elo (0.621) 16
May 22 - May 28 Bradley-Terry Recency 0.660 Bradley-Terry (0.662) 16

Model Performance Leaderboard

Models ranked by strict holdout AUC when available (fallback: full-season AUC). Hover over column headers for explanations.

# Model 7d Split AUC Acc Brier LogLoss n AUC 7d Acc 7d Brier 7d n 7d
1 Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → STRICT
18g
 - - - - 0 0.722 72.2% 0.194 18
2 Home Team Baseline Home Team Baseline Always favor the home team with a fixed prior. More → STRICT
18g
 - - - - 0 0.667 61.1% 0.238 18
3 Elo Elo Streaming paired-comparison rating with recency baked into sequential updates. More → STRICT
18g
 - - - - 0 0.611 50.0% 0.223 18
4 Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → STRICT
18g
 - - - - 0 0.569 61.1% 0.237 18
5 Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → STRICT
18g
 - - - - 0 0.569 72.2% 0.232 18
6 Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. More → STRICT
18g
 - - - - 0 0.569 72.2% 0.231 18
7 Adjusted Context Blend Adjusted Context Blend Experimental context-heavy win model blending strong team components with rest and venue context. More → STRICT
18g
 - - - - 0 0.556 55.6% 0.251 18
8 Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → STRICT
18g
 - - - - 0 0.542 66.7% 0.226 18
9 Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → STRICT
18g
 - - - - 0 0.542 61.1% 0.252 18
10 Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → STRICT
18g
 - - - - 0 0.542 61.1% 0.252 18
11 Avg Margin Baseline Avg Margin Baseline Predict from simple average scoring margin in the training window. More → STRICT
18g
 - - - - 0 0.542 61.1% 0.237 18
12 Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → STRICT
18g
 - - - - 0 0.528 61.1% 0.244 18
13 Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → STRICT
18g
 - - - - 0 0.514 66.7% 0.229 18
14 Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → STRICT
18g
 - - - - 0 0.514 66.7% 0.242 18
15 Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → STRICT
18g
 - - - - 0 0.514 66.7% 0.242 18
16 Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → STRICT
18g
 - - - - 0 0.514 61.1% 0.246 18
- Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. More → FULL
no 7d
 - - - - 0 - - - 0

Methodology

ELO / Bradley-Terry

  • ELO: Iterative updates, K=64, HCA=100
  • BT: Static logistic regression on all games
  • Both model win probability, not margin
  • ELO updates after each game; BT fits once

Pythagorean Models

  • Raw: Classic points scored/allowed formula
  • Efficiency: Pace-adjusted (pts per possession)
  • Adjusted: Opponent-adjusted efficiency
  • Log: Log-linear multiplicative scale

Margin Regression

  • Team-level ridge regression on point margin
  • Linear Bradley-Terry (margin target)
  • Alpha=0.05 (CV-tuned)
  • Learns home advantage from data (~6 pts)

Baselines

  • Home Team: Always predict home wins (60%)
  • Avg Margin: Higher average margin wins
  • Models should beat these to add value