2015 WNBA Model Performance
Season
Every ranked model predicted the same 169 games (2015-07-02 to 2015-10-14, 15 weekly windows) out of sample: each week's games were predicted by models fit only on the season's earlier games.
Model Performance Leaderboard
Ranked by full-season AUC. The final week is the season's last 7 played days (3 games, 2015-10-08 to 2015-10-14), predicted from every earlier game: a small sample that shows the latest form.
| # | Model | AUC | Acc | Brier | LogLoss | AUC final wk | Acc final wk |
|---|---|---|---|---|---|---|---|
| 1 | Elo Elo Streaming paired-comparison rating with recency baked into sequential updates. More → | 0.697 | 68.0% | 0.221 | 0.635 | 0.750 | 66.7% |
| 2 | Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | 0.681 | 68.0% | 0.225 | 0.641 | 0.750 | 66.7% |
| 3 | Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | 0.680 | 67.5% | 0.225 | 0.641 | 0.750 | 66.7% |
| 4 | Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.654 | 66.3% | 0.230 | 0.652 | 0.750 | 66.7% |
| 5 | Adjusted Context Blend Adjusted Context Blend Chronologically trained component blend with rest and signed venue context. More → | 0.652 | 63.3% | 0.238 | 0.673 | 0.750 | 66.7% |
| 6 | Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.647 | 64.5% | 0.231 | 0.652 | 0.750 | 66.7% |
| 7 | Home Team Baseline Home Team Baseline Always favor the home team with a fixed prior. More → | 0.642 | 63.9% | 0.232 | 0.657 | 0.750 | 66.7% |
| 8 | Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. More → | 0.623 | 59.2% | 0.236 | 0.665 | 0.750 | 33.3% |
| 9 | Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.622 | 59.2% | 0.236 | 0.665 | 0.750 | 66.7% |
| 10 | Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 0.604 | 57.4% | 0.240 | 0.675 | 0.500 | 33.3% |
| 11 | Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 0.603 | 55.0% | 0.249 | 0.699 | 0.500 | 33.3% |
| 12 | Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 0.598 | 56.2% | 0.243 | 0.681 | 0.500 | 33.3% |
| 13 | Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.578 | 52.7% | 0.248 | 0.690 | 0.500 | 66.7% |
| 14 | Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.577 | 52.7% | 0.248 | 0.689 | 0.500 | 66.7% |
| 15 | Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.568 | 50.9% | 0.240 | 0.673 | 0.500 | 66.7% |
| 16 | Avg Margin Baseline Avg Margin Baseline Predict from simple average scoring margin in the training window. More → | 0.551 | 51.5% | 0.247 | 0.687 | 0.500 | 66.7% |
Methodology
Out-of-sample windows
- The season is split into 7-day windows ending on its last played day
- Each window is predicted by models refit on all earlier games of the season
- The first window starts after 21 days of games
- Every model is scored on the same games; a model that cannot predict most of them is listed as not compared
Elo / Bradley-Terry
- Elo: the published league Elo (preseason priors and carryover where configured), frozen at the window start
- Bradley-Terry: static logistic fit on the earlier games
- Recency variants weight games with a 120-day half-life in NCAA leagues (keeping the non-conference games that link conferences) and a 30-day half-life in pro leagues
Pythagorean and margin models
- Raw, adjusted and log-adjusted Pythagorean from scores; efficiency from box-score possessions
- Margin: ridge regression on point margin with a learned home advantage
- Ensembles average the log-odds of five families
Baselines
- Home Team: the home team wins 60% (50% at neutral sites)
- Avg Margin: the higher average margin so far wins
- Models should beat these to add value