2000-2001 NBA Model Performance
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1996-1997
Every ranked model predicted the same 1069 games (2000-11-25 to 2001-06-15, 29 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 (1 games, 2001-06-09 to 2001-06-15), 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 | Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | 0.736 | 67.8% | 0.211 | 0.611 | - | 0.0% |
| 2 | Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | 0.733 | 67.1% | 0.210 | 0.609 | - | 100.0% |
| 3 | Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.732 | 66.6% | 0.212 | 0.613 | - | 0.0% |
| 4 | Elo Elo Streaming paired-comparison rating with recency baked into sequential updates. More → | 0.731 | 67.3% | 0.209 | 0.602 | - | 100.0% |
| 5 | Adjusted Context Blend Adjusted Context Blend Chronologically trained component blend with rest and signed venue context. More → | 0.730 | 67.1% | 0.209 | 0.603 | - | 100.0% |
| 6 | Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.729 | 66.0% | 0.212 | 0.613 | - | 100.0% |
| 7 | Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.718 | 65.9% | 0.212 | 0.611 | - | 100.0% |
| 8 | Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. More → | 0.717 | 65.9% | 0.212 | 0.612 | - | 100.0% |
| 9 | Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.710 | 64.8% | 0.223 | 0.638 | - | 100.0% |
| 10 | Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 0.709 | 64.5% | 0.214 | 0.616 | - | 100.0% |
| 11 | Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.709 | 64.1% | 0.215 | 0.618 | - | 100.0% |
| 12 | Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.709 | 64.3% | 0.215 | 0.618 | - | 100.0% |
| 13 | Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 0.704 | 64.1% | 0.217 | 0.622 | - | 100.0% |
| 14 | Avg Margin Baseline Avg Margin Baseline Predict from simple average scoring margin in the training window. More → | 0.703 | 64.7% | 0.219 | 0.628 | - | 100.0% |
| 15 | Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 0.700 | 63.6% | 0.217 | 0.623 | - | 100.0% |
| 16 | Home Team Baseline Home Team Baseline Always favor the home team with a fixed prior. More → | 0.594 | 59.3% | 0.241 | 0.676 | - | 0.0% |
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