2025-26 regular season
Impact rating
How many points per 100 possessions a player adds to his team's margin, after accounting for the nine other players on the floor. Split into offense and defense.
Impact: points per 100 possessions better or worse than an average player, with 80% ranges. Rank is league wide among players with 250+ minutes.
| # | Player | Impact | -9.00+9.0 | O | D |
|---|---|---|---|---|---|
| 4 | Chet HolmgrenC-F · OKC | +5.7 | +1.6 | +4.1 | |
| 5 | Nikola JokicC · DEN | +5.3 | +4.8 | +0.6 | |
| 11 | Neemias QuetaC · BOS | +4.3 | +0.9 | +3.4 | |
| 17 | Bam AdebayoC-F · MIA | +4.1 | +2.2 | +1.9 | |
| 41 | Jarrett AllenC · CLE | +2.7 | +1.8 | +0.9 | |
| 44 | Jalen DurenC · DET | +2.6 | +1.5 | +1.1 | |
| 71 | Rudy GobertC · MIN | +1.9 | -2.2 | +4.1 | |
| 81 | Karl-Anthony TownsC · NYK | +1.7 | +1.8 | -0.1 | |
| 108 | Kyle FilipowskiC · UTA | +1.3 | +1.8 | -0.5 | |
| 191 | Nikola VucevicC · ORL | +0.3 | +0.1 | +0.2 | |
| 204 | Brook LopezC · LAC | +0.2 | -0.5 | +0.7 | |
| 242 | Donovan ClinganC · POR | -0.1 | +0.2 | -0.2 | |
| 307 | Deandre AytonC · WAS | -0.8 | -0.1 | -0.7 | |
| 326 | Kel'el WareC · MIL | -1.1 | -0.7 | -0.3 | |
| 341 | Jock LandaleC · ATL | -1.2 | +0.7 | -1.8 | |
| 363 | Myles TurnerC-F · MIL | -1.5 | -2.2 | +0.7 | |
| 403 | Derik QueenC · NOP | -2.0 | +0.4 | -2.3 | |
| 431 | Maxime RaynaudC · SAC | -2.9 | -0.6 | -2.3 |
How this works
The method
Every game is cut into stints: stretches where the same ten players are on the floor. For each stint we count the points each side scored per 100 possessions. One regression then asks: which players, when they are on the floor, go with more points for their team and fewer for the other team, after accounting for everyone else out there? That is the offense (O) and defense (D) number; Impact is the sum.
With about 500 players and a lot of noise, the plain answer overreacts, so every player is pulled toward average (a ridge regression). How hard to pull is picked by testing on games the model did not see: we split games into 5 groups, fit on 4 and score the fifth. Longer stints count more because they have more possessions.
The range comes from refitting the model 50 times on reshuffled sets of games. 8 in 10 of those fits land inside the bar.
What we measured
- Data
- 36,423 stints from 1,230 games, 582 players (450 with 250+ minutes)
- Pull toward average
- Strength 3,000, the best of 10 tried
- Test error
- 63.3 points per 100 on unseen games, against 63.5 for guessing league average (112.3) every stint: 0.32% better. Single stints are short and mostly luck, so the gain is small even when the player signal is real.
- Typical range width
- 3.2 points for the middle player
- Matches on/off
- Correlation 0.81 with raw on/off net rating (450 players)
- Matches Courtside score
- Correlation 0.47 with the box score based Courtside score (352 players)
- Fitted
- 2026-09-27 · rapm-v1
What it cannot see
- One season is noisy. Two players a point apart are usually not different; read the ranges, not the ranks.
- It uses no box score at all: no points, rebounds or steals. A player gets credit only for what the scoreboard did while he played, so bench players who share the floor with the same teammates are hard to separate.
- It does not know about role, matchups, garbage time or injuries, and players with few minutes stay close to 0 by design.
- Players under 250 minutes are stored but shown as "not enough minutes".