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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.

76 players

Impact: points per 100 possessions better or worse than an average player, with 80% ranges. Rank is league wide among players with 250+ minutes.

#PlayerImpact-9.00+9.0ODMin
1Shai Gilgeous-AlexanderG · OKC+7.0+4.7+2.32,257
6Derrick WhiteG · BOS+5.2+1.5+3.72,621
7Dyson DanielsG · ATL+4.9+2.6+2.32,518
8Marcus SmartG · HOU+4.5+1.6+3.01,765
9Amen ThompsonG-F · HOU+4.3+2.1+2.32,953
14Donovan MitchellG · CLE+4.2+3.7+0.52,342
16Ausar ThompsonG-F · DET+4.1+1.6+2.51,894
18Devin VassellG-F · SAS+4.0+3.0+1.02,044
20Cade CunninghamG-F · DET+4.0+2.8+1.22,171
21Donte DiVincenzoG · MIN+3.9+1.7+2.22,494
25Devin BookerG · PHX+3.3+1.3+2.02,144
26Jrue HolidayG · POR+3.3+0.7+2.61,557
27Jalen SuggsG · ORL+3.2+0.3+2.91,573
30Davion MitchellG · MIA+3.1+1.8+1.31,999
32Collin GillespieG · PHX+2.9+1.3+1.62,280
33CJ McCollumG · ATL+2.8+0.8+2.02,263
39Austin ReavesG · LAL+2.7+1.3+1.41,762
42Cedric CowardG · MEM+2.6+2.1+0.61,598
45Jamal SheadG · TOR+2.6+0.8+1.81,850
47Scottie BarnesG-F · TOR+2.5+0.4+2.02,681
50Cason WallaceG · OKC+2.4-0.3+2.72,045
51LaMelo BallG · MIN+2.4+3.8-1.42,017
54De'Aaron FoxG · SAS+2.4+2.2+0.22,231
55Dylan HarperG · SAS+2.4+0.7+1.71,558
58Nickeil Alexander-WalkerG · ATL+2.3+2.0+0.32,602
60Desmond BaneG · ORL+2.2+0.9+1.42,751
66Moses MoodyG · GSW+2.1-0.5+2.51,536
74Kevin HuerterG · DET+1.8+1.0+0.81,549
76Peyton WatsonG · CLE+1.8+2.8-1.01,598
80Ryan RollinsG · MIL+1.7+1.2+0.52,374
83Isaiah JoeG · DET+1.7+0.6+1.01,506
84Kon KnueppelG-F · CHA+1.7+1.4+0.22,548
90Tyrese MaxeyG · PHI+1.6+0.4+1.22,661
94Jaime Jaquez Jr.G · MIL+1.5+1.0+0.52,115
96VJ EdgecombeG · PHI+1.5+0.9+0.62,623
101Tim Hardaway Jr.G · MIA+1.3+1.2+0.12,127
105Jordan GoodwinG · PHX+1.3+0.5+0.81,571
106Immanuel QuickleyG · TOR+1.3+0.8+0.52,231
109Jamal MurrayG · DEN+1.2+4.0-2.82,652
110Payton PritchardG · BOS+1.2+1.5-0.32,553
114Jalen BrunsonG · NYK+1.2+2.3-1.12,590
115Pelle LarssonG · MIA+1.2+0.8+0.41,848
123Nique CliffordG · SAC+1.1+0.2+0.91,881
138Kris DunnG · LAC+0.9+0.1+0.82,223
144Stephon CastleG · SAS+0.8-0.3+1.12,038
153Luke KennardG · PHX+0.7+1.4-0.71,679
169Quentin GrimesG · LAL+0.5-0.9+1.42,206
170Sion JamesG · CHA+0.5-0.8+1.41,840
172Jaylon TysonG-F · CLE+0.5+0.4+0.21,778
175Josh HartG · NYK+0.5+0.2+0.31,993
176Tre JonesG · CHI+0.5+0.2+0.31,752
178Anthony BlackG · ORL+0.5+0.2+0.21,905
185Anthony EdwardsG · MIN+0.4+2.0-1.62,137
199A.J. GreenG · MIL+0.3+0.8-0.52,270
209Dillon BrooksG-F · PHX+0.2+0.5-0.31,701
220Jaylen BrownG · PHI+0.1+0.8-0.72,442
222Cam SpencerG · MEM+0.1+0.1-0.11,715
225James HardenG · CLE+0.1+2.0-2.02,438
255Brandin PodziemskiG · GSW-0.2-1.6+1.42,333
269Josh GiddeyG · CHI-0.4+0.2-0.51,731
284Luguentz DortG · ATL-0.5-0.3-0.21,848
288Reed SheppardG · HOU-0.6-0.1-0.42,147
289Terance MannG · BKN-0.6-0.3-0.31,529
291Dennis SchroderG · CHA-0.6-0.60.01,696
303Ayo DosunmuG · MIN-0.7-0.70.01,881
305Bennedict MathurinG-F · NOP-0.8-0.80.01,620
317Russell WestbrookG · SAC-0.90.0-0.91,852
346Keyonte GeorgeG · UTA-1.2+1.7-2.91,780
358Max ChristieG · DAL-1.4-1.1-0.32,244
399Collin SextonG · LAL-2.0-1.5-0.41,613
413Andrew NembhardG-F · IND-2.1-0.5-1.61,786
419DeMar DeRozanG · DEN-2.3+0.6-2.92,405
424Jeremiah FearsG · NOP-2.6-0.9-1.62,112
426Bruce BrownG · NYK-2.6-2.3-0.31,998
446Isaiah CollierG · UTA-4.8-1.5-3.21,517
448Carlton CarringtonG · WAS-5.0-1.9-3.12,270

Bars show the 80% range on a scale from -9.0 to +9.0. The tick is the rating. A range that crosses 0 means we cannot tell the player apart from average yet.

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".