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How do they measure efficiency in the NBA?

The NBA measures and publishes player efficiency in the form of their stat “Player Impact Estimate” (PIE). This measure looks to take into account a player’s overall offensive and defensive impact on the court. This has replaced the previously used efficiency rating “EFF”. Since 2007 ESPN have published their advanced stat “Player Efficiency Rating” which was created by John Hollinger and looks to achieve a similar outcome to the NBA’s PIE rating.

Below we dive into these two advanced statistics to better understand how they are calculated and what they hope to achieve. Then we take a look at a comparison to see if they achieve their goal and which one performs better.

What is PIE? (

Player Impact Estimate (PIE) is a simple metric that seeks to give an indication of performance at both the team and player level. It is published on under Advanced Statistics and is seen as a major improvement to their previous EFF Rating. The definition and formula for creating PIE is stated on as the below:

Player Impact Estimate (PIE)

Definition: PIE measures a player’s overall statistical contribution against the total statistics in games they play in. PIE yields results which are comparable to other advanced statistics (e.g. PER) using a simple formula.

Formula: (PTS + FGM + FTM – FGA – FTA + DREB + (.5 * OREB) + AST + STL + (.5 * BLK) – PF – TO) / (GmPTS + GmFGM + GmFTM – GmFGA – GmFTA + GmDREB + (.5 * GmOREB) + GmAST + GmSTL + (.5 * GmBLK) – GmPF – GmTO)

The update from EFF to PIE has two Notable changes.  They have included “Personal Fouls” and they have added a “denominator”. The idea of these changes is to use the denominator as an “automatic equalizer”. Using the denominator, removes the need to consider the varying amount of possessions per game of the statistics that are being analyzed.

In its simplest terms, PIE aims to show what percentage of game events the subject impacted. The base statistics being used are traditional basketball statistics;

  • Points (PTS)
  • Rebounds (REB) both offensive (O) and defensive (D)
  • Assists (AST)
  • Turnovers (TOV)
  • Steals (STL)
  • Blocks (BLK)
  • Field Goals Made (FGM)
  • Field Goal Attempts (FGA)
  • Free Throws Made (FTM)
  • Free Throw Attempts (FTA)
  • Game (Gm) is used as the denominator to put the individual stats into the context of the game they happened in. explains how to read the statistic as below:

“A team that achieves more than 50% is likely to be a winning team. A player that achieves more than 10% is likely to be better than the average player. A high PIE % is highly correlated to winning. In fact, a team’s PIE rating and a team’s winning percentage correlate at an R square of .908 which indicates a “strong” correlation. We’ve introduced this statistic because we feel it incorporates a bit of defense into the equation. When a team misses a shot, all 5 players on the other team’s PIE rating goes up.”

We have pulled the 2022-23 Regular Season data for PIE and qualified it by using minimum 25 games played and minimum 25 minutes per game averaged. We have displayed this data below for the top 30 players in the league that season. – Player Impact Estimate (PIE) – Min 25 Games Played 25 Minutes pG
RankPLAYERTEAMGames PlayedAverage MinutesPIE
1Joel EmbiidPHI6634.621.3
2Nikola JokicDEN6933.721.1
3Giannis AntetokounmpoMIL6332.120.4
4Luka DoncicDAL6636.220.2
5Anthony DavisLAL563418.6
6Kevin DurantPHX4735.618.5
7Jimmy ButlerMIA6433.417.7
8Ja MorantMEM6131.917.5
9Shai Gilgeous-AlexanderOKC6835.517.5
10Damian LillardPOR5836.317.3
11LeBron JamesLAL5535.517.2
12Zion WilliamsonNOP293317.1
13Jayson TatumBOS7436.916.7
14Stephen CurryGSW5634.716.6
15Domantas SabonisSAC7934.616.1
16Kawhi LeonardLAC5233.615.9
17Tyrese HaliburtonIND5633.615.9
18James HardenPHI5836.815.9
19Lauri MarkkanenUTA6634.415.4
20Donovan MitchellCLE6835.814.9
21Julius RandleNYK7735.514.8
22Devin BookerPHX5334.614.7
23Kyrie IrvingDAL6037.314.6
25Kristaps PorzingisWAS6532.614.5
24Deandre AytonPHX6730.414.5
27Brandon IngramNOP4534.214.4
26Christian WoodDAL6725.914.4
29Trae YoungATL7334.814.3
28Nikola VucevicCHI8233.514.3
30Bam AdebayoMIA7534.614.2

You can see the accuracy of it at first glance. The top 3 players all finished in the top 3 of MVP voting, with the number 1 ranked player for PIE, Joel Embiid, winning MVP. In addition scanning the list reads like a who’s who of NBA talent. We are going to look at ESPN’s player efficiency statistics next and then come back and compare the two to see if they agree with each other.

What is Player Efficiency Rating (PER): ESPN

Player Efficiency Rating (PER) is a statistic ESPN publishes and is commonly used by the media to discuss players efficiency and performance in the NBA.

To generate PER, statistician and former ESPN journalist John Hollinger created formulas that he outlined in detail in his 2005 book “Pro Basketball Forecast”. PERs formulas return a value for each of a player’s in-game accomplishments. This includes both positive accomplishments such as field goals, free throws, 3-pointers, assists, rebounds, blocks and steals, and negative ones such as missed shots, turnovers and personal fouls. PER is “per-minute” and is pace-adjusted, so it accounts for the varying amount of possessions that take place in a 48 minute NBA game and can be used to compare players who have seen different amounts of court time.

Hollinger himself expresses a warning that PER is not “all encompassing”. It doesn’t take into account a huge amount of defensive input, outside of traditional blocks, steals and rebounds. What he aims to do is summarizes a players statistical achievements in a comparable way so that analysis of their impact within the context of what they actually do on the court can have a more solid foundation.

The formula for PER is a lot more complex than the one used for NBA.coms PIE metric. We have listed it below, however it is not something that can be used easily to calculate the PER for your rec league team. In order for the PER formula to work the PER is set to 15.0 for the League wide average each season.

ESPN/Hollinger’s PER Formula:

uPER = (1 / MP) *

     [ 3P

     + (2/3) * AST

     + (2 – factor * (team_AST / team_FG)) * FG

     + (FT *0.5 * (1 + (1 – (team_AST / team_FG)) + (2/3) * (team_AST / team_FG)))

     – VOP * TOV

     – VOP * DRB% * (FGA – FG)

     – VOP * 0.44 * (0.44 + (0.56 * DRB%)) * (FTA – FT)

     + VOP * (1 – DRB%) * (TRB – ORB)

     + VOP * DRB% * ORB

     + VOP * STL

     + VOP * DRB% * BLK

     – PF * ((lg_FT / lg_PF) – 0.44 * (lg_FTA / lg_PF) * VOP) ]

If you would like to better understand the formula, please read Hollingers brilliant 2005 book, “Pro Basketball Forecast”.

As we did with PIE we have pulled PER from for the 2022-23 season. Hollingers more complex formula already contains qualifying measures, so we have not set the factors for min 25 games and 25 average minutes as we did with PIE.

ESPN: Hollinger Stats – Player Efficiency Rating (PER) – Qualified Players
RankPLAYERTeamGames PlayedAverage MinutesPER
1Nikola JokicDEN6933.731.78
2Joel EmbiidPHI6634.631.69
3Giannis AntetokounmpoMIL6332.129.29
4Luka DoncicDAL6636.228.99
5Anthony DavisLAL563428
6Jimmy ButlerMIA6433.427.82
7Shai Gilgeous-AlexanderOKC6835.527.43
8Damian LillardPOR5836.326.96
9Kevin DurantBKN/PHX4735.626.14
10Zion WilliamsonNO293325.43
11Stephen CurryGS5634.724.36
12Kawhi LeonardLAC5233.624.13
13LeBron JamesLAL5535.524.11
14Jayson TatumBOS7436.923.92
15Tyrese HaliburtonIND5633.623.79
16Domantas SabonisSAC7934.623.66
17Ja MorantMEM6131.923.52
18Kristaps PorzingisWSH6532.623.28
19Donovan MitchellCLE6835.823.13
20Kyrie IrvingBKN/DAL6037.422.65
21Lauri MarkkanenUTAH6634.422.32
22Clint CapelaATL6526.622.25
23Devin BookerPHX5334.622.21
24Trae YoungATL7334.822.15
25De’Aaron FoxSAC7333.421.98
26Jaren Jackson Jr.MEM6328.421.82
27James HardenPHI5836.821.78
28Walker KesslerUTAH742321.72
29Jalen BrunsonNY683521.38
30Jakob PoeltlSA/TOR7226.521.16

Comparing PIE and PER to understand efficiency in the NBA

When you compare the two tables side by side they are remarkably similar. We have looked at only the top 30 qualifying players from each list. We already noted that the top 3 MVP candidates finished in the top 3 on both metrics. An interesting point to note here is that while PIE had Joel Embiid, the crowned MVP, in top spot, PER had Nicola Jokic,the Finals MVP, in top spot. Possible evidence that Hollinger’s PER is a more accurate formula for predicting players impact and efficiency as it is widely believed following Denver’s title win that Jokic was truly the best player of the 2022-23 season and Embiid pipped him mostly for narrative reasons. That said, Embiid and Jokic are almost impossible to separate statistically as their PER and PIE numbers indicate.

The rest of the lists match up quite closely. We have to get to number 21 on PIE and 22 on PER before we find a player that didn’t make both lists. In total, from the top 30 of each metric there are only 36 players listed. 24 players that appear on both lists and 12 players who only appear in the top 30 of 1 of the lists.

These players are:

On PIE but not PER

  • Julius Randle
  • Deandre Ayton
  • Brandon Ingram
  • Christian Wood
  • Nikola Vucevic
  • Bam Adebayo

On PER but not PIE

  • Clint Capela
  • De’Aaron Fox
  • Jaren Jackson Jr.
  • Walker Kessler
  • Jalen Brunson
  • Jakob Poeltl

All of these players make the top 50 of both lists or would appear with slightly different qualifying criteria applied.

Our conclusion is that the newer Player Impact Estimate (PIE) from and the Player Efficiency Rating from both do a good job of creating a single number to represent a player’s impact or efficiency during an NBA season.

There are certainly no great differences between them and both appear to favor offense and defense in equal measure, based on the players who make up the lists we have shown.

As Hollinger states, he created this metric not to produce a fool proof list of who’s the best, but to shift the conversation from the minute detail of individual stats, to give us a single number to compare players within the context of their role on their NBA team. The joy of basketball is that all the players who take the floor during a game can have a great impact on the outcome, regardless of how much they touch the ball, shoot or pass. Statistics will never fully sum up who has made an impact that matters.

We have linked to NBA.coms PIE statistic here and ESPNs PER statistic here.