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Model Card

Neutral-context sum of Points For Elo and Points Prevention Elo. This is the cleanest interpretable 2D replacement for 'offense points created / defense points prevented' before moving to richer vector models.

Validation here is out of sample. From two weeks into each season, a model trained on earlier games predicts each following week without learning from that week. Predictions use the players' observed minutes; each point is the minutes-weighted predicted and actual rate in that player's games. When validation was computed, its source snapshot was verified against this published model revision.

Selected League / Season

Change league with the League selector at the top of the page.

Held-out player predictions

Plot Points Net Elo against any counting stat (one point per player, every season with a served list).

NBA 2026-2027: 184 players with at least 100 held-out minutes. Across players, predicted and actual correlate at r = 0.68 (weighted by held-out minutes). Across these player averages, weighted R² = 0.33 (relative to the weighted mean of actual held-out outcomes). Per held-out team game, squared error is 11.8% below predicting the league average. Each dot is a player (184 with the most held-out minutes are plotted; larger dots had more); on the dashed line, actual equals predicted. Select a dot for the player.

Held-out miss against playing time

Each dot is a player's actual minus predicted team minus opponent points per 100 possessions in the player's games against their held-out minutes (log scale). Orange points are the minutes-weighted mean miss of eight equal groups of players by playing time: a model that shrinks its low-minutes players too far, or not far enough, shows a slope.

Output Coverage

Rows in each model's current published run (or legacy rows where no run is selected).