At its core, basketball is the management of possessions.
Every possession begins with control of the ball and moves through a simple chain of events. The offense can only produce a scoring attempt if it avoids turning the ball over; once it reaches a shot, that attempt may produce points, miss and end with a defensive rebound, or miss and continue through an offensive rebound.
The structure of the game can therefore be expressed as a branching tree:
possession ├── turnover (possession ends, - oTOV / + dTOV) └── shot (first chance reaches a scoring attempt, + oTOV / - dTOV) ├── points (possession ends, + oTS / - dTS) └── miss (first shot does not score, - oTS / + dTS) ├── defensive rebound (possession ends, - oSC / + dSC) └── offensive rebound (possession continues) ├── points (possession ends, + oSC / - dSC) └── no points (possession ends, - oSC / + dSC)
Six-Factor RAPM measures how players influence each stage of this process. Offensive impact and defensive impact can each be decomposed into three mechanisms. Together they form total player impact.
RAPM = oTS + oTOV + oSC + dTS + dTOV + dSC
Each factor captures a different way players change the expected value of possessions while they are on the floor.
oTS — Offensive Scoring Efficiency
This factor measures how much a player increases the points their team generates per scoring attempt.
The primary driver of offensive scoring efficiency is the combination of usage and true shooting percentage. Players who take a large share of their team's attempts and convert them efficiently have the strongest direct impact on this factor.
However, scoring efficiency is not created through shooting alone. Players also influence it through playmaking, ball movement, spacing, and transition offense, all of which affect the quality of shots their teammates receive. Passers who generate open looks, movers who keep the offense flowing, and players who create advantages in transition can raise team scoring efficiency even when they are not the one finishing the play.
Drawing fouls is also critical, since free throws increase the points generated per scoring attempt.
oTOV — Offensive Turnover Control
Before a scoring attempt can occur, the offense must maintain possession of the ball.
Turnovers end possessions without producing a shot. This factor measures how a player influences the rate at which their team loses possessions before reaching the shot phase.
The primary drivers of offensive turnover impact are offensive load and turnover rate. Players who carry a large share of the offense—through scoring attempts, passing volume, and time on the ball—naturally have greater influence on this factor, since turnovers can only occur when a player is directly involved in the play.
Ball security, decision-making, and the ability to handle defensive pressure all contribute here. Players who maintain control of the offense under heavy responsibility allow their team to reach scoring attempts more consistently.
oSC — Offensive Second-Chance Impact
Even when a shot misses, the possession may not be finished.
oSC measures how much a player helps turn missed shots into second-chance value. Offensive rebounds are the main input, but the factor is about the full value of the extra possession: keeping the ball alive, creating another scoring attempt, and converting or supporting the points that come after the rebound.
Second-chance opportunities increase the total number of attempts a team has to convert possessions into points, so this factor captures possession extension after misses.
dTS — Defensive Scoring Efficiency Suppression
This factor measures how much a player lowers the points opponents generate per scoring attempt.
Defenders influence scoring efficiency through several mechanisms. Contesting shots, navigating screens, and containing isolation attacks reduce shot quality on the perimeter. At the rim, defenders protect the basket through positioning, verticality, and shot blocking.
Interior defenders also create rim deterrence, forcing offenses to avoid high-value attempts at the basket altogether. Foul discipline is also important. Because scoring efficiency includes free throws, defenders who avoid unnecessary fouls—or commit smart fouls that prevent easy layups—can reduce the points opponents generate per attempt.
Together, these actions lower the efficiency of opponent scoring attempts.
dTOV — Defensive Turnover Creation
Turnovers immediately end possessions before a scoring attempt can occur.
Defenders generate turnovers through steals, deflections, and disruptive pressure on the ball and in passing lanes. Pressure can also force rushed decisions that lead to mistakes by the offense.
Another important source of defensive turnovers comes from offensive fouls drawn, including charges and moving screen violations. Players who position themselves effectively to draw these calls can end possessions without allowing a shot.
By forcing turnovers, defenders prevent scoring attempts from occurring in the first place.
dSC — Defensive Second-Chance Prevention
Even strong defensive possessions remain incomplete if the offense secures the rebound.
dSC measures how much a player suppresses opponent second-chance value after missed shots. Defensive rebounding is the main input, but the factor is about ending the possession and preventing the extra attempts and points that follow offensive rebounds.
Strong defensive second-chance prevention ensures that missed shots actually end the opponent's possession.
The Full Structure of Impact
The entire game can be decomposed into three mechanisms applied on both offense and defense:
Shot Value: oTS · dTS
Possession Survival: oTOV · dTOV
Second-Chance Value: oSC · dSC
Six-Factor RAPM estimates how much each player shifts these components relative to league average while they are on the floor. Instead of collapsing player value into a single number, the model reveals where that value is created within the lifecycle of a possession—through improving scoring efficiency, protecting or ending possessions, and controlling what happens when shots miss.
Credits: Steph Noh's NBA Salary Model, DARKO, and BBall Index.
Impact metrics all try to answer one question: how much better is a team when a given player is on the floor?
The box score records what a player did — points, rebounds, assists — but not what those actions were actually worth, and it misses whole categories of value entirely: screening, spacing, defensive communication, shot deterrence, making the right pass one beat earlier. Impact metrics start from the scoreboard instead. They track how the score moves while a player is on the court, then use regression to adjust for the quality of every teammate and opponent sharing the floor with them.
Almost every metric on this page is expressed the same way: points per 100 possessions, relative to an average NBA player. A +4 means the team plays about 4 points per 100 possessions better than it would with an average player in that spot — an All-Star-level number. 0 is average, and most rotation players land between -2 and +2. Offense and defense are usually reported separately and summed for the total.
One reading convention for this site: green is always good for the player's team, red is always bad — on both ends. A positive defensive number means the player helps his defense, even when the underlying stat (like opponent shooting) is something the defense wants to push down.
The foundation of every impact metric on this site. RAPM estimates how many points per 100 possessions a player adds, using nothing but the scoreboard and who was on the floor — no box score at all. If a player makes his team better, RAPM sees it, whether or not it shows up in his stats.
Time Decay RAPM: Uses a player's full history but exponentially down-weights older possessions, so the estimate reflects the current player while still borrowing stability from the past. This is the best single answer to "how good is he right now?"
3-Year RAPM: Restricted to the last three seasons, with recent possessions weighted more heavily. A middle ground between single-season noise and career-long inertia.
Lifetime RAPM: A 28-year calculation with an age curve built in, so a player is compared against expectations for his age at every point in his career. Built for career-arc and historical comparisons rather than current-season evaluation.
Our signature metric. Regular RAPM tells you how much a player helps; Six-Factor RAPM tells you where the impact comes from. It splits total impact into the three ways a possession can be won or lost — shooting efficiency, turnovers, and second chances — on both offense and defense.
Every possession ends one of three ways: the offense turns the ball over, it gets a shot off and scores (or doesn't), and if the shot misses, someone controls the rebound. Six-Factor RAPM measures how a player bends each of those stages in his team's favor:
The six pieces sum back to the player's total: RAPM = oTS + oTOV + oSC + dTS + dTOV + dSC. This is a zero-residual decomposition — the factors account for total RAPM exactly (R² = 1), so every point of a player's impact on the scoreboard flows through one of these six channels.
Same engine as RAPM, run three times. Instead of one regression predicting the score, three separate ridge regressions on the same lineup data predict shooting efficiency, turnover rate, and rebounding rate — each producing an offensive and defensive estimate per player. A second stage converts each factor into points per 100 possessions, which is what the tables display.
The 6Factor tab breaks these down further — shot-location value (rim, mid-range, three), free throws, and turnover types — and the Decomp tab shows the full offense/defense mirror.
EPM, created by Taylor Snarr at Dunks & Threes, is a hybrid impact metric: RAPM stabilized with a statistical prior built from box-score and player-tracking data. It trades some of pure RAPM's neutrality for much more stability and predictive power.
The core problem with pure RAPM is noise: it needs years of possessions to settle. EPM's answer is to give the regression a smart starting point. Instead of assuming every player is average until the lineup data says otherwise, EPM assumes each player is what his stats suggest he is — and then lets the lineup data push that estimate up or down.
DARKO, created by Kostya Medvedovsky, is a projection system rather than a rating: its headline number, DPM (Daily Plus-Minus), is an estimate of how good a player is right now, updated after every game. It's consistently among the most predictive public impact metrics.
DARKO is built around a Kalman filter — the same technique used to track moving objects from noisy sensor readings. Each game is a new noisy reading of a player's true skill. The filter blends every new reading with everything it already believes about the player, weighting by how reliable each source is. A rookie's estimate moves fast because the model knows little; a ten-year veteran's estimate barely budges on one hot week.
LEBRON is Basketball Index's flagship impact metric. Like EPM, it's RAPM with a box-score prior — its two distinguishing ideas are a "luck adjustment" that strips variance out of the on/off data and a prior tailored to each player's offensive role.
These measure the two halves of shot defense: how often a player challenges shots, and what happens to those shots when he does. They also track the team-level question that matters most for rim protectors — does the rim get attacked less, and less successfully, with him out there?
For the Dif%, accuracy, and frequency metrics, negative is good — opponents shooting worse or attacking the rim less. For the contest-volume metrics (per 100), higher means more defensive activity.
Assists undercount playmaking: they require a teammate to make the shot, and they say nothing about how much time on the ball the passer needed. These metrics separate the three things that actually matter — how much a player creates, how efficiently he creates it, and what his passes cost in turnovers.
Volume (potential assists), efficiency (per minute on-ball), quality (assist eFG%), and risk (BadPass%) are four different skills. The best playmakers score on all four; a high-volume creator with a high BadPass% and low assist eFG% is producing quantity, not quality.
These measure the plays that end opponent possessions outright — steals, charges, and blocks the defense actually recovers. The theme throughout: a defensive play only counts fully if your team ends up with the ball.
Higher is better across the board. STOP% is the best single summary; the components tell you how a player generates his stops.
The Four Factors are key components that contribute to a team's success. They are:
These factors are calculated using on/off court data. By comparing how these factors change when a player is on the court versus off, we can estimate their career impact in each area on both ends of the court.
Importantly, a player's Four Factors can predict their non-age adjusted RAPM with a 0.95 r^2. This means the Four Factors are extremely valuable in explaining why a player may have a high or low RAPM.
Read these statistics as percentage increases or decreases to the team's four factors Effective Field Goal %, Turnover %, Offensive Rebound %, Free Throw rate These RA-four factors reveal the drivers behind a player's RAPM Positive numbers indicate positive impact in that area.
I've carefully chosen these stats to provide insight into a player's passing impact. Here's what they mean:
28-Year Lifetime RAPM is a stat that measures how much a player impacts winning over their entire career, relative to their age, using data from 1997 to 2024.
The Four Factors are key components that contribute to a team's success. They are:
These factors are calculated using on/off court data. By comparing how these factors change when a player is on the court versus off, we can estimate their career impact in each area on both ends of the court.
Importantly, a player's Four Factors can predict their non-age adjusted RAPM with a 0.95 r^2. This means the Four Factors are extremely valuable in explaining why a player may have a high or low RAPM.
By considering both the overall RAPM and the Four Factors, you can get a more complete picture of how a player contributes to their team's success over their career, taking into account their performance relative to their age and specific areas of impact.
This section explores the intriguing relationship between two metrics that measure a player's impact on opponent turnovers:
A measure of a player's ability to disrupt opposing offenses, representing the number of turnovers they force per 100 possessions above or below the league average. This metric is derived from box score statistics, specifically combining steals and offensive fouls drawn.
The impact on opponent turnover rate derived from RAPM (Regularized Adjusted Plus-Minus) analysis of lineup data, independent of box score statistics.
By comparing these metrics, we gain insight into the value of lineup-based analysis versus traditional box score statistics. This comparison reveals potential hidden contributions to forcing turnovers that may not be captured by steals and drawn fouls alone. It's worth noting that while RA-DTOV may capture indirect ways of forcing turnovers, it is also subject to the inherent variability in RAPM and lineup analysis. Nevertheless, it shows how well lineup-based analyses can largely agree with what the boxscore based analysis would suggest.
The significance of this analysis is underscored by the substantial impact of turnovers on game outcomes. In terms of plus-minus value, forcing an additional turnover per 100 possessions is roughly equivalent to +1 points per 100 in value, representing a crucial aspect of a player's defensive impact that should not be overlooked.
Note: For players with over 10,000 defensive possessions, rFTOV predicts RA-DTOV with an R² of 0.70, demonstrating a strong correlation between box score-derived and lineup-based turnover impact metrics. This predictive power increases with larger sample sizes, reaching an R² of 0.77 for players with over 40,000 defensive possessions, despite potential underestimation due to mismatched time periods in the datasets.
No historical data available for this dataset.
At its core, basketball is the management of possessions.
Every possession begins with control of the ball and moves through a simple chain of events. The offense can only produce a scoring attempt if it avoids turning the ball over; once it reaches a shot, that attempt may produce points, miss and end with a defensive rebound, or miss and continue through an offensive rebound.
The structure of the game can therefore be expressed as a branching tree:
possession ├── turnover (possession ends, - oTOV / + dTOV) └── shot (first chance reaches a scoring attempt, + oTOV / - dTOV) ├── points (possession ends, + oTS / - dTS) └── miss (first shot does not score, - oTS / + dTS) ├── defensive rebound (possession ends, - oSC / + dSC) └── offensive rebound (possession continues) ├── points (possession ends, + oSC / - dSC) └── no points (possession ends, - oSC / + dSC)
Six-Factor RAPM measures how players influence each stage of this process. Offensive impact and defensive impact can each be decomposed into three mechanisms. Together they form total player impact.
RAPM = oTS + oTOV + oSC + dTS + dTOV + dSC
Each factor captures a different way players change the expected value of possessions while they are on the floor.
oTS — Offensive Scoring Efficiency
This factor measures how much a player increases the points their team generates per scoring attempt.
The primary driver of offensive scoring efficiency is the combination of usage and true shooting percentage. Players who take a large share of their team's attempts and convert them efficiently have the strongest direct impact on this factor.
However, scoring efficiency is not created through shooting alone. Players also influence it through playmaking, ball movement, spacing, and transition offense, all of which affect the quality of shots their teammates receive. Passers who generate open looks, movers who keep the offense flowing, and players who create advantages in transition can raise team scoring efficiency even when they are not the one finishing the play.
Drawing fouls is also critical, since free throws increase the points generated per scoring attempt.
oTOV — Offensive Turnover Control
Before a scoring attempt can occur, the offense must maintain possession of the ball.
Turnovers end possessions without producing a shot. This factor measures how a player influences the rate at which their team loses possessions before reaching the shot phase.
The primary drivers of offensive turnover impact are offensive load and turnover rate. Players who carry a large share of the offense—through scoring attempts, passing volume, and time on the ball—naturally have greater influence on this factor, since turnovers can only occur when a player is directly involved in the play.
Ball security, decision-making, and the ability to handle defensive pressure all contribute here. Players who maintain control of the offense under heavy responsibility allow their team to reach scoring attempts more consistently.
oSC — Offensive Second-Chance Impact
Even when a shot misses, the possession may not be finished.
oSC measures how much a player helps turn missed shots into second-chance value. Offensive rebounds are the main input, but the factor is about the full value of the extra possession: keeping the ball alive, creating another scoring attempt, and converting or supporting the points that come after the rebound.
Second-chance opportunities increase the total number of attempts a team has to convert possessions into points, so this factor captures possession extension after misses.
dTS — Defensive Scoring Efficiency Suppression
This factor measures how much a player lowers the points opponents generate per scoring attempt.
Defenders influence scoring efficiency through several mechanisms. Contesting shots, navigating screens, and containing isolation attacks reduce shot quality on the perimeter. At the rim, defenders protect the basket through positioning, verticality, and shot blocking.
Interior defenders also create rim deterrence, forcing offenses to avoid high-value attempts at the basket altogether. Foul discipline is also important. Because scoring efficiency includes free throws, defenders who avoid unnecessary fouls—or commit smart fouls that prevent easy layups—can reduce the points opponents generate per attempt.
Together, these actions lower the efficiency of opponent scoring attempts.
dTOV — Defensive Turnover Creation
Turnovers immediately end possessions before a scoring attempt can occur.
Defenders generate turnovers through steals, deflections, and disruptive pressure on the ball and in passing lanes. Pressure can also force rushed decisions that lead to mistakes by the offense.
Another important source of defensive turnovers comes from offensive fouls drawn, including charges and moving screen violations. Players who position themselves effectively to draw these calls can end possessions without allowing a shot.
By forcing turnovers, defenders prevent scoring attempts from occurring in the first place.
dSC — Defensive Second-Chance Prevention
Even strong defensive possessions remain incomplete if the offense secures the rebound.
dSC measures how much a player suppresses opponent second-chance value after missed shots. Defensive rebounding is the main input, but the factor is about ending the possession and preventing the extra attempts and points that follow offensive rebounds.
Strong defensive second-chance prevention ensures that missed shots actually end the opponent's possession.
The Full Structure of Impact
The entire game can be decomposed into three mechanisms applied on both offense and defense:
Shot Value: oTS · dTS
Possession Survival: oTOV · dTOV
Second-Chance Value: oSC · dSC
Six-Factor RAPM estimates how much each player shifts these components relative to league average while they are on the floor. Instead of collapsing player value into a single number, the model reveals where that value is created within the lifecycle of a possession—through improving scoring efficiency, protecting or ending possessions, and controlling what happens when shots miss.