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Current Impact

All 5 of the major metrics: EPM, LEBRON, DARKO, Time Decay RAPM, and 3 Year Recency RAPM. Click a player's name to see their profile.

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Time Decay RAPM

All history, Weighs recency heavily zero box score stats, lineup only Credit: @kmedved, Popularized: @Fiizop

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2017-2024 RAPM vs strong and weak

Experimental

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3 Year RAPM

Age adj.for midpoint of next szn lineup only, weighed for recency Credit: @JerryEngelmann

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28 Year Lifetime RAPM: Measuring Career Impact Relative to Age

This is the most important table on the site

What is it?

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.

Key points:

It looks at how well a team performs when a player is on the court vs. off the court.
It accounts for the quality of teammates and opponents.
It uses an "age curve" to estimate a player's value at different ages.
It measures how well a player performs relative to expectations for their age.
Regularized Adjusted Four Factor data is also available for a player's career.

Four Factors and Their Importance:

The Four Factors are key components that contribute to a team's success. They are:

EFG (Effective Field Goal Percentage): Measures shooting efficiency, accounting for the fact that 3-pointers are worth more than 2-pointers.
TOV (Turnover Percentage): The rate at which a team turns the ball over per possession.
ORB (Offensive Rebound Percentage): The rate at which a team rebounds its own missed shots.
FTR (Free Throw Rate): The rate at which a team gets to the free throw line relative to its field goal attempts.

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.

Why use an age curve?

It helps the model be more accurate over long time periods.
Traditional RAPM calculations value a player as a constant, which isn't realistic over a long career.
The age curve allows the model to account for typical improvement and decline patterns.
It helps compare players fairly across different stages of their careers.

What's special about it?

Long-term view: It considers a player's entire career, not just individual seasons.
Age-relative performance: It measures how well a player performs compared to expectations for their age.
Four Factors analysis: It breaks down a player's impact into specific areas, helping explain their overall RAPM.

Things to keep in mind:

Not perfect: Some players age differently than the typical curve, which can affect their rating.
Limited data: For older players, we're missing data from before 1997.
Team effects: A player's rating can be influenced by long-time teammates.

How to use it:

Use it as one tool among many to evaluate a player's career impact.
Look at the Four Factors to understand why a player rates high or low in RAPM.
Consider how a player performed relative to expectations for their age throughout their career.
Remember that it's a career estimate, not an exact measure of a player's peak value.
A player's peak 4 or 5 year rapm is a better estimate of their peak in the NBA. Some guys just decline way faster than others.

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.

Credit: @JerryEngelmann

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RAPM peaks

This is everyone's Offensive, Defensive, and Total RAPM peaks. Ranks are the ranks of their peak RAPMs among everyone's peak RAPMs on this page, not their peak rank within that specific multi-year sample. NOT SCALED. Better data coming.

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MAMBA

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DPM (DARKO)

Hybrid box score/rapm metric Pads boxscore inputs Credit: darko.app

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Playoff Scoring

Top 300 Playoff Scorers (1997-2024). Click a player's name for series and year information. Adj rTS (adjusted relative true shooting) is measured against their playoff opponents regular season TS% allowed (instead of league average TS% as we would in the regular season), so we get a more fair measure of shooting efficiency. TS Add uses that adjusted rTS value to calculate a theoretical total points above average shooting efficiency multiplied by the volume of shots. TS Add Per 100 shows how much of this value this player creates per 100 possessions. TS Add +2 rewards volume scorers more by measuring against against -2 rTS instead of 0. High volume +0 rTS scoring is still valuable -- a normal TS Add analysis would not recognize this value. Playoff rORTG and rDRTG (relative Offensive/Defensive Ratings) is the player's career on court offensive/defensive ratings in the playoffs relative to their playoff opponents' reg season defensive ratings. This gives us a more fair measure of how the team offense/defense performed.

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Historical Raptor, goes back to 1977

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LEBRON

Hybrid box score/rapm metric Luck adjustment to rapm numbers Credit: @bballindex

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Forced Turnovers: Box Score vs. Lineup Analysis

This section explores the relationship between two metrics measuring a player's impact on opponent turnovers: rFTOV (Relative Forced Turnovers) and RA-DTOV (Regularized Adjusted Defensive Turnover Rate).

This section explores the intriguing relationship between two metrics that measure a player's impact on opponent turnovers:

rFTOV (Relative Forced 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.

RA-DTOV (Regularized Adjusted Defensive Turnover Rate)

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.