This page of our site will include act as an evergreen glossary of statistics used on BBall Index. We will make update as appropriate, and hope to expand into more comprehensive explanations/graphics/videos over time for metrics from us and elsewhere.

Read about our LEBRON impact metric on its introduction page here.


Opportunity & Usage Metrics

Ball Dominance

Ball Dominance is the percentage of a team’s time of possession (ball in a player’s hands, not being passed) that a player commands, among the time that they’re on the court.

Formula: Hog Rate = (Time of Possession/Offensive Possessions) / (Seconds on Offense per Possession on Court/Offensive Possessions)

Read a 25% value for LeBron James as “LeBron had the ball in his hands for 25% of the Lakers’ time of possession when he was on-court.”


Minutes/Gameplay Consistency

Our consistency calculations look at game by game performance/minutes and calculate their variance. Players that have higher consistency ratings have values that are more similar to each other game to game. Higher consistency of performance can be good, but players with lower consistency and outlier higher performances also have value. For more information on the methodology and calculations, go to this link or this one.


Scoring Possessions

A “scoring possession” is any possession ended by a player through a shot (made or missed), turnover, or trip to the foul line.

Some examples for an Anthony Davis post up:

  • He shoots and misses – Yes, this is a scoring possession
  • He has to pass back out for a reset – No, this is not a scoring possession
  • He turns the ball over on a pass out – Yes, this is a scoring possession
  • He passes to a cutter who scores, giving him an assist – No, this is not a scoring possession


Team Minutes/Possession/Touch/etc. Share

These calculations look at the percentage of the team’s minutes/possessions/touches/etc. that an individual player commands. If a player has a 5% minute share, their minutes make up 5% of their team’s total minutes played.

Formula: Value Share = Player’s Value / Team Total Value


Time of Possession

Time of Possession is the length of time a player has the ball in their hands. At the team level, we use this data to calculate how long the ball is in a player’s hands vs in the air for passes.

Formula: Time of Possession = Touches * Time per Touch


Total Offensive Load

Total Offensive Load is an estimate of how much a player directly contributes to an individual possession through their shooting, creating, passing, and turning the ball over (while attempting to shoot, create, or pass).

Offensive load was designed by Ben Taylor (@ElGee35). For more information on methodology, click here.

Formula: Offensive Load = (Assists – (0.38 * Box Creation)) * 0.75) + FGA + FTA * 0.44 + Box Creation + Turnovers


True Usage

True Usage is an estimate of usage that incorporates tracking data to better measure the true usage a player has of the team’s offense. This is done by incorporating potential assists.

True Usage was designed by Seth Partnow (@SethPartnow). For more information on methodology, click here.


Contextual Data

Lineup Talent, and associated measures

Lineup Talent/Defensive Talent/Playmaking/etc. are estimates of the environment a player plays within based on the lineups they’re used in. These will not be uniform for players on the same team. Our current published talent grades are assigned for each player in every lineup, with averages of the four players playing alongside the player in question being averaged for that lineup’s rating. Lineup ratings are then averaged, weighted by minutes played, to calculate the final average lineup rating.


Lineup Spacing

Lineup Spacing is an estimate of the degree to which the offensive players’ willingness and ability to shoot from the perimeter forces defenders to defend the 3-point line, thus creating space for drives/cuts/etc. This is the only contextual lineup stat that looks at all five players in a lineup to calculate the rating, rather than just the four players alongside the player in question. 3-point shooting frequency and accuracy are both accounted for to calculate a rating for each lineup, which are then averaged, weighted by minutes played, to calculate the final average lineup spacing rating.


Matchup Difficulty

Matchup Difficulty is an estimate of the difficulty a defender takes on with their defensive matchups/assignments. We develop matchup difficulty using partial possession player tracking data, to capture switches, help defense, etc., which allows us to capture how much time a player spends defending each opposing defender (rather than 1 player per possession calculations you may find elsewhere).

Calculations look at the average usage of players defended, as well as the average offensive impact (via O-LEBRON) to derive a final difficulty value.


D Position/Role Versatility – Versatility Defending Offensive Positions/Offensive Roles

Versatility Defending Offensive Positions/Roles captures a player’s versatility through the lens of defending players of differing offensive positions or offensive roles. The metric looks only at actual time spend defending those roles, not performance by those offensive players in those situations.

We develop this versatility metric using partial possession player tracking data, to capture switches, help defense, etc. which allows us to capture how much time a player spends defending each opposing defender (rather than 1 player per possession calculations you may find elsewhere). From this, we can infer that the team’s coaching staff trusts the player to defend a wider range of offensive talents.

A player defending a wider array of positions/roles will have a higher value for their versatility, whereas players guarding fewer positions or roles will see lower versatility ratings.

Future versions of this metric will include an adjustment to better account for team schemes.

Performance Consistency

Please refer to Minutes/Gameplay Consistency metric writeup above.


Pace Impact Estimate

Pace Impact Estimate estimates the degree to which a player’s court presence impacts the pace of play for their team, through looking at on/off-court impacts as well as stats correlating to pace increases and decreases. For example, committing or causing turnovers correlates to increased pace.

Pace Impact Estimate was originally calculated by 538. You can read more about their methodology here.


Foul Trouble Percentage

Our Foul Trouble Percentage shows the percentage of a player’s minutes they’re in foul trouble, using data from We define foul trouble as having:

  • 2-5 fouls in Q1
  • 3-5 fouls in Q2
  • 4-5 fouls in Q3 and Q4
  • 5 fouls in overtime

Perimeter Shooting

Openness Rating

Openness Ratings show a z-score value estimating the degree of openness a player has on average for their 3-point attempts. Input data from is used, along with some internal calculations (to try to increase accuracy), to derive these values.


Percent of 3PTA Open

These values estimate the percentage of a player’s 3-point attempts that are completely open and unimpeded by any defensive pressure. Input data from is used, along with some internal calculations (to try to increase accuracy), to derive these values.


Avg. 3PT Shot Distance

These values use SportRadar player tracking data to capture average distance (in feet) from the rim for 3-point attempts. Players with the highest values with have higher percentiles. You’ll see a mix of players heaving the ball (like some centers) and guys like Trae Young and Dame Lillard at the top of the leaderboard. A the bottom, taking closer (easier) shots, you’ll find players with a higher percentage of their 3-point attempts from the (shorter) corners.


3-Point Ratios

C&S : PU Ratio: ratio of Catch & Shoot 3-point attempts to Pull Up 3-point attempts

C3 : ATB Ratio: ratio of Corner 3-point attempts to Above the Break 3-point attempts


3PT Shot Quality

3-point shot quality looks at all of our data on openness, whether a shot is self created or not, location on the court and internal estimates of player movement on 3s (the same used for stationary vs movement shooter designations for our offensive archetypes) to estimate an overall shot quality on 3-point attempts. These values are represented using z-scores.


3-Point Gravity

Gravity data looks at player frequency/volume and efficiency to estimate the degree to which their shooting ability from that part of the floor will influence defenses to cover them more closely. Negative values are good, and the higher the value, the more we’d expect a defense to respect that player’s scoring from that area. Additionally, for 3-pointers, shots from further out past the 3-point line are rewarded more than 3-pointers right at the line.

Having high 3-point gravity and demanding more defensive attention should open up cutting, post ups, and driving lanes. Having high rim gravity should help collapse the defense on cuts, post ups, or drives to open up outside shooting opportunities.

BBall Index’s Andrew Patton first developed the gravity calculations in 2019, and did his methodology writeup here.

For full NBA, WNBA, and NCAA gravity data and 3D charts, go here.


3PT Foul Rate

3-point foul rate captures the percentage of 3-point attempts a player draws a foul on.

Formula: 3PT Foul Rate = (3-shot Fouls + 3PT & 1 fouls) / (3PA + 3PT & 1 fouls)


3PTA Rate

3-point attempt rate is the percentage of a player’s shots taken that are from 3-point distance.

Formula: 3PTA Rate = 3PA / FGA


3PT Shot Making

Our Shot Making ratings look at 3-point shooting above expectations based on shot quality (as described above). This answers, “Given the player’s degree of difficulty, how well are they shooting?” These values are represented using z-scores.

If you see a player with a high 3PT% but an average Shot Making rating, you’re likely looking at a player in a favorable situation in terms of quality of shots that’s reaping the benefits of their environment. Likewise, we may see a player with a similar 3PT% but lower Shot Quality, that ends up having a higher Shot Making rating.


3PT Shot Creation

Our Shot Creation rating looks at a player’s tendencies to create their own 3-point attempts. Percentage of 3-pointers unassisted, along with unassisted 3-pointers per 100 possessions on the court, are leveraged for these calculations. Smaller samples for both inputs are stabilized using a padding method. These values are represented using z-scores.

Note: 3PT Shot Creation does not look at proficiency. This is purely capturing whether or not the player is creating their own shot on 3s.


Perimeter Shooting Talent Grade (2.0)

Our revamped Perimeter Shooting talent grade leverages our Shot Making and Shot Creation values as inputs, as well as accounting for volume to regress down smaller samples. This seeks to capture how well of a 3-point shooter a player is in a neutral environment.

For example, Will Barton and TJ Warren have the same exact 3PT% but very different Perimeter Shooting grades (A- for Barton, D+ for Warren). Barton is achieving his 3PT% on a higher degree of difficulty (looking at his Shot Quality rating), thus has a higher Shot Making rating. He’s also creating 3-point looks at a far better rate than Warren (A vs F ratings in 3PT Shot Creation). Those two factors combined result in Barton having an A- while Warren has a D+, and would tell us that Barton is the more talented 3-point shooter (that should perform more highly in a neutral environment).


One on One

Total Isolations

Totals isolations capture a player’s scoring possession volume in one on one situations on both the perimeter and interior.

Formula: Total Isolations = Perimeter Isolation Scoring Possessions + Post Up Isolation Scoring Possessions


Total Isolation Impact

Total Isolation Impact seeks to capture the points a player adds above/below what an average player would score if given the same volume of possessions in similar situations.

The next update of this metric will include stabilized values for players with volumes below the calculated thresholds based off of their offensive role. For example, if a player is 15 possessions below the threshold and is a Pick & Pop Big, their data will be infused with 15 average efficiency possessions for Pick & Pop Bigs in that play type, then reduced down to the original possession volume.

Formula: Total Isolation Impact = Total Isolation Points – ((Perimeter Isolation Possessions * League Average Perimeter Isolation Efficiency) + (Post Up Possessions * League Average Post Up Efficiency))


Isolation Foul Drawn Rate

Isolation Foul Drawn Rate captures the percentage of isolation scoring possessions a player draws a shooting foul.

Formula: Isolation Foul Drawn Rate = Shooting Fouls Drawn during Isolation Possessions / Total Isolation Possessions


Isolation Turnover Rate

This metric shows the percentage of a player’s total scoring possessions spent in perimeter isolation and posting up where they turn the ball over.

Formula: Isolation Turnover Rate = Turnovers during Isolation Possessions / Total Isolation Possessions


Off-Ball Movement

Movement Attack Rate

Movement Attack Rate measures the percentage of a player’s first chance half court scoring possessions they spend in one of our two movement categories, either cutting to the rim (no dump offs) or in an off-screen action.

Formula: (Off Screen Possessions + (Cutting Possessions – Dump Offs)) / (Half Court Possessions – (Miscellaneous Possessions + Putbacks))


Movement Distance Rating

Movement Distance Rating seeks to answer: “does the player cover a lot of ground for the offensive role they’re in?” It’s calculated as offensive feet traveled per minute played, with a role adjustment to adjust for the types of actions a player spends their time in and show distance traveled relative to other players in the same offensive role.


Movement Speed Rating

Movement Speed Rating is role adjusted average offensive speed, which captures how fast a player moved relative to other players in their offensive role.


Movement Points

Movement Points are all points from cuts (no dump offs) and off-screen scoring possessions.


Movement Impact

Movement Impact seeks to capture the points a player adds above/below what an average player would score if given the same volume of possessions in similar situations.

The next update of this metric will include stabilized values for players with volumes below the calculated thresholds based off of their offensive role. For example, if a player is 15 possessions below the threshold and is a Pick & Pop Big, their data will be infused with 15 average efficiency possessions for Pick & Pop Bigs in that play type, then reduced down to the original possession volume.

Formula: Movement Impact = Total Movement Points – ((Non-Dump Off Cutting Possessions * League Average Non-Dump Off Cutting Efficiency) + (Off Screen Possessions * League Average Off Screen Efficiency))



Adjusted Drives

Adjusted Drives per 75 offensive possessions on court is just that, with a regression of league average driving rate possessions to stabilize small samples.


Getting to Rim Rating

Our Getting to Rim rating analyzes a player’s ability to create their own shots at the rim, by looking at the percentage of their shots at the rim that are self-created and their self-created attempts per 100 possessions on the court.

Note: this metric doesn’t care about assisted shots at the rim. A player driving and getting to the rim, or posting up and creating their own shots at the rim, will be rewarded more than a player finishing dump offs.

These values are represented using z-scores.


Drive Passout Rate

Drive Passout Rate is the percentage of drives a player passes to a teammate, rather than attempting to score.


Drive Assist Rate

Drive Assist Rate is the percentage of drives a player passes to a teammate and is credited with an assist


Drive Foul Drawn Rate

Drive Foul Drawn Rate is the percentage of drives a player draws a shooting foul during their drive.


Contact Finish Rate

Contact Finish Rate is the percentage of shooting fouls a player converts on. Without other data to measure contact, shooting fouls are proxied in to estimate that aspect of the game.


Adjusted FG% at Rim

Adjusted FG% at the Rim shows FG% at the rim, with small samples adjusted downward by a Sigmoid function.


Shot Quality at Rim

Our Shot Quality at Rim rating seeks to capture how difficult a player’s attempts at the rim were. We do so by incorporating multiple variables such as shot distance, the way a player got to their shot (dump off vs drive, etc.), the spacing of the lineups they are in, whether the shot was set up by a teammate or not, and the rim protecting abilities of the defensive team.

These values are represented using z-scores.


Finishing at Rim

Our Finishing at Rim rating seeks to capture how well a player scores at the rim on the attempts they have once there, and doing so while capturing and adjusting for variables that may impact performance finishing at the rim (such as spacing, if they were creating their own shot or not, location of shots, etc.).

These values are represented using z-scores.


Overall Finishing Talent Grade (2.0)

This metric analyzes a player’s ability to get to and finish at the rim, using our Getting to Rim and Finishing at Rim ratings as inputs, as well as regressing down smaller samples. You can use this metric to compare among players, with the confidence that degree of difficulty is being captured and adjusted for to allow comparison of players’ talent in as neutralized an environment as possible.

The way the math is calculated, players creating their own offense rather than finishing dump offs will be rewarded more in this metric, which results in fewer big men dominating the top of this list (compared to our 1.0 version of this metric).



Role Adjusted Assist Points

Role adjusted Assist Points per 75 offensive possessions on court shows assist point performance relative to expectation based on others in the same role. This metric seeks to answer: “Are they a good passer for their role?”

We like to use this to help identify players in non-traditional playmaking roles that are good ball movers, as well as separate the true playmakers from the rest among players within roles that will accrue assist volumes just based on what the players are asked to do.


Passing Aggressiveness

Passing Aggressiveness is the percentage of passes a player has that are a bad pass turnover, which we proxy as aggressiveness. Lower bad pass turnover percentages indicate a player is making safer passes and is more of a ball mover than a playmaker.

Other metrics within the playmaking category use this metric to help identify if a player is a good playmaker for their level of aggressiveness. That isn’t done here with this measure alone.

Formula: Passing Aggressiveness = Bad Pass Turnovers / Passes


High Value Assists

High Value Assists, which we may also refer to as Morey Assists, are 3-point assists, rim assists, and free throw assists.


Box Creation

Box Creation is an estimate of open shots carved out for teammates by drawing defensive attention using box score metrics only.

Calculations developed by Ben Taylor (@ElGee35). You can read more about his methodology here.


Passing Creation Volume

Passing Creation Volume analyzes a player’s volume of playmaking contributions to their teammates through their passing. This metric looks at passes a player makes per 75 possessions on the court offensively that lead to a shot from a teammate. We pad these rates to regress smaller samples back towards the average.

These values are represented using z-scores.


Passing Efficiency

Passing Efficiency analyzes how well a player takes care of the ball as a passer, and does so by comparing rates of bad pass turnovers with expected rates, given the player’s ball dominance, how often they’re generating shots for teammates (via Passing Creation Volume), the quality of those shots (via Passing Creation Quality), and the versatility in pass types executed (via Passing Versatility).

If you look just at turnover rates, you’re lumping in a lot of turnovers that have nothing to do with passing.

If looking just at rates of bad pass turnovers, real playmakers will naturally look worse and players rarely creating for others will look better.

If ignoring quality, you’ll see players pursuing high quality and versatile attempts like Luka Doncic and Trae Young with lower efficiency values because the metric isn’t properly capturing the degree of difficulty on those passes.

To be holistic in capturing all of that, we establish the baseline of what’s expected, given a player’s playmaking ask, to compare with instead and enable smarter analysis.

Here’s a visual example showing how generating high value assists has a natural relationship with bad pass turnovers:

These values are represented using z-scores.


Passing Versatility

Passing Versatility analyzes a player’s playmaking ability by quantifying how full the passing repertoire is for a player, through looking at Synergy passing data from scoring play types as well as SportRadar data on assist locations. This allows us to gauge who is has the most range in their pass types and is a more well rounded passer.

A player may have excellent performance within their passing style but not have the range as a playmaker to make other kinds of passes (kick outs on drives, etc.). This metric identifies that spread of playmaking versatility and rewards players who are more versatile.

Several tiers of spread are identified through the data, which is why you’ll see groups of players with the same values. The higher the spread, the fewer players you’ll see in the tier.

These values are represented using z-scores.


Passing Creation Quality

Passing Creation Quality analyzes playmaking ability through the quality of scoring opportunities a player creates for their teammates through their passing.

Higher caliber playmakers tend to create opportunities for teammates that are easier than a non-playmaker. Part of that comes from offensive scoring ability, part comes from vision to see the right player to pass to, a part is the execution of those passes to be both accurate, so the ball isn’t turned over, and placed well, so the shooting player doesn’t need to spend time pulling the ball up from their shoelaces or down from the sky and lose their would-be openness on a shot. This all manifests into the shot quality created.

Data used to calculate Passing Creation Quality includes location of assists, as well as conversion rates on potential assists for players relative to rates for those same players passed to from other teammates and league average on types of scoring looks.


Scoring Gravity

Scoring Gravity is a component of analyzing playmaking ability calculated using the same Gravity data referenced in our Perimeter Shooting section, just averaged by Rim, Midrange, and 3PT ranges.

In helping to quantify a more context-neutral playmaking talent metric, recognizing players who bring their offense with them and naturally open up scoring opportunities for teammates rather than requiring Xs and Os to facilitate that process is important.


Playmaking Talent

Our Playmaking Talent grade (version 2.0) analyzes a player’s playmaking for teammates through their ratings in Passing Creation Volume, Passing Creation Quality, Passing Versatility, Passing Efficiency, and Scoring Gravity (which has a small weight). As designed, the metric is about half measuring capability and volume of playmaking and half measuring effectiveness playmaking for others.

This metric is designed to be as context-neutral as possible, enabling values more accurately capturing true playmaking talent and resulting in stability from year to year, even with players changing teams.


Roll Gravity

Team Roll Man Share

Please refer to the Minutes Share notes in the Opportunity & Usage section of the glossary.


Roll/Pop/Slip/Total Roll Man Impact

Please refer to the Isolation Impact or Movement Impact notes in the One on One and Off-Ball Movement sections of the glossary.


Screen Assists

Screen Assists capture screens that free up players for a score, crediting the screener with a screen assist.

If you’d like to learn more, ask Jazz Twitter and they’ll tell you all about them.


Rim Gravity

Please refer to the Gravity notes in the Perimeter Shooting section of the glossary.


Screening Talent

Our Screening Talent metric is calculated via a private BBall Index dataset that captures how well players screen for each other, looking at contact on screens and value add for players utilizing screens


Roll Gravity

Post Style Rating

Post Style Rating evaluates a player’s post style in terms of the types of shots they take (jumpers, to rim, hook shots, up and under, or post pin) in terms of degree of difficulty, based on league average efficiencies.

A player with a high Post Style Rating is attempting easier shots in the post, whereas a player with a lower rating has a style geared toward lower efficiency shots.


Post Up Draw Foul Rate

Post Up Draw Foul Rate shows the percentage of a player’s post scoring possessions they draw a shooting foul.


Post Up Impact

Please refer to the Isolation Impact or Movement Impact notes in the One on One and Off-Ball Movement sections of the glossary.


Potential Assists per Post Pass

Potential Assists per Post Pass capture facilitation ability of big men from the post through measuring how often their pass outs directly lead to a shot attempt by a teammate.


Roll Gravity

OReb/DReb Chance

OReb/DReb Chances per 75 possessions on court capture how frequently a player has an opportunity to obtain a rebound, based on their position relative to where the ball was rebounded.


Adjusted OReb/DReb Success Rate

Adjusted OReb/DReb Success Rate is a Second Spectrum stat capturing success rate on attempted rebounds, adjusted to exclude times the player deferred a rebound to a teammate.


OReb/DReb Positioning

OReb/DReb Positioning shows the average feet away from the rim a player was when they captured their rebounds. We can use this to tell how players are generally positioned on the court. Being closer to the rim will yield a higher percentile and letter grade.

From this, we can tell that Bigs with higher values (and thus lower percentiles) are being utilized more on the perimeter than others. The inverse is true for guards grabbing their rebounds closer to the rim.


Real Adjusted OReb/DReb Rate

Real Adjusted Rates use ridge regressions to capture the impact a player has on their team’s performance in a specific area based on their presence on-court.

This, along with Adjusted Reb Success Rates and Adjusted Box Out Rates, can help tell us what kind of rebounder a player is, and how much they help their team based on their role and performance within that role.

Source data is calculated at NBAShotCharts and can be found here.


Putback Impact

Please refer to the Isolation Impact or Movement Impact notes in the One on One and Off-Ball Movement sections of the glossary.


Adjusted Box Out Rate

Adjusted Box Out Rate is an estimate of defensive box outs per shot from the opposing team while a player is on court, with small samples regressed average box out rates by defensive role.


DReb Success vs Expectations

DReb Success vs Expectations evaluates the difference between what we’d expect a player’s success rate to be, based on their physical profile and situational factors, with their actual success rate on rebounds.

Expected rates are based off of the player’s height, weight, lineup box out tendencies, player defensive positioning, and contested DReb%.


Rebounding Crashing Data

Our rebounding crashing data looks at player performance putting themselves in position to rebound, based on player tracking data on player positioning and positioning when obtaining rebounds.


Rebounding Conversion Data

Our rebounding conversion data looks at player performance on obtaining rebounds in contested situations once in position to rebound, using various data points to estimate their expected conversion to then compare with actual conversion.


Overall Rebounding Talent

Our overall rebounding talent data evaluates player skill sets as rebounders, accounting for their crashing and conversion skills.


Matchup Data

All matchup data leverages partial possession player tracking data, which captures which offensive player each defender was defending for each portion of each defensive possession.

This better captures real defensive assignments and switches, help defense, etc. than saying that each defensive player defended one offensive player each possession.


Perimeter Defense

On-Ball Defense, Ball Handler Screen Defense, & Off-Ball Chaser Defense

The video in this tweet explains more about these three perimeter defensive metrics, which evaluate specific components of perimeter defense through the use of tracking data. These stats look at a defender’s ability to disrupt and suppress an opponent’s offense.


Real Adjusted Turnover Rate

Please refer to the Real Adjusted OReb/DReb Rate notes in the Offensive/Defensive Rebounding glossary section.


Loose Ball Recovery Rate

Loose Ball Recovery Rate seeks to capture a player’s ability to recover loose balls. Since we’ll only have data on specific participants in loose ball situations, those must be relied upon for this calculation.

Loose balls recovered is simple enough via tracking data, but participation in those situations is based off of loose ball fouls (tried, but collected a foul instead), lost ball turnovers off of steals (had the ball taken from you), and loose balls recovered (succeeded). If a player attempted to recover a loose ball but didn’t initially lose it, eventually get it, or commit a foul along the way, it won’t be tracked here.

Formula: Loose Ball Recovery Rate = Loose Balls recovered / (Loose Ball Fouls + Lost Ball Turnovers Off Steals + Loose Balls Recovered).

This metric was originally calculated by BBall Index’s Krishna Narsu. His initial writeup can be found here.


Pickpocket Rating

Pickpocket Rating attempts to capture how active a player is on-ball with steals, using Lost Ball Steals per 75 possessions on court defensively.

Formula: Pickpocket Rating = Lost Ball Steals / 75 Possessions


Passing Lane Defense

Passing Lane Defense captures how disruptive players are through their ability to intercept passes and deflect the ball. Passing Lane Defense is a rate stat, showing impact per 75 possessions on the court defensively.

Formula: Passing Lane Defense = Deflections / 75 Possessions + Interceptions / 75 Possessions


Matchup Adjusted Defensive Feet / Minute

Matchup Adjusted Defensive Feet per minute captures how far a player travels defensively for someone defending the players they defend. Using matchup tracking data and capturing the percentage of time a defender spends defending players in each offensive role, we can estimate how far they would be expected to travel and compare that with their actual travel distance.


Shot Profile Deterrence

Shot Profile Deterrence, or Q-RAD, is a defensive statistic that attempt to measure how a player deters the offense from taking high efficiency shots. Made or missed shots do not matter in this context, only attempts. This metric captures how offensive players’ shot profiles change when guarded by specific defenders, with value added through those profiles changing away from high efficiency areas for those specific players (the rim and corner 3s come to mind) and into lower efficiency areas (such as the mid range).

On its own, Q-RAD is not a measure of defensive quality. It is best to interpret as a player’s style in impacting the opposing offense, and digested along with other defensive stats and defensive impact stats for a full defensive evaluation.

Q-RAD was developed by BBall Index’s Andrew Patton. Andrew’s methodology and writeup on Q-RAD is found here.


Hunted in Perimeter Isolation

Our Hunted in Perimeter Isolation metric looks at how frequently opposing offenses attack players in perimeter isolations as a result of switches, with a defensive role adjustment to help account for the fact that a big man is more likely to be attacked in these situations than a shooting guard.

The role adjusted result shows who, with their role being considered, is seen by opposing defenses as a viable target. It also shows who defenses are less apt to attack in those situations.

Future iterations of this metric will further integrate defensive scheme estimates to help adjust players up/down that are where they are due to more or less switchiness in team defensive scheme.


Lineup Interior Defense

Please refer to the Lineup Talent notes in the Context Data portion of the glossary.


Interior Defense

Rim Deterrence

Rim Deterrence is a role adjusted measure of how a player’s presence on-court impacts opponents’ frequency of attacking the rim. This is done by analyzing how a player’s presence on-court relates to opponents’ shooting at the rim, both relative to teammates and to the league as a whole.

We care about this stat for Bigs, not Guards or nearly as much for Wings.


% Rim Shots Contested

This is the percentage of shots at the rim while the player is on the court defensively that they contest.


Block Rate on Contests

Block Rate on Contests shows a player’s success rate of blocking shots on the ones they contest.


Rim dFG% vs Expected

Rim dFG% vs Expected is the FG% players shoot on shots at the rim above/below what Second Spectrum would expect based on shot locations. Low samples are regressed with league average percentages.

This does not adjust for the shooting ability at the rim of specific players faced.


Adjusted Rim Points Saved / 36 Minutes

Adjusted Rim Points Saved / 36 Minutes is a measure of points “saved” on shots at the rim by the defender based off of their ability to contest shots, with smaller samples regressed with league and role average rim dFG% values.

This concept was first developed by Seth Partnow, and a writeup on the methodology and math behind the metric can be found here or here. We’ve made a few tweaks to these calculations, including an adjustment for defensive role.


Lineup Perimeter Defense

Please refer to the Lineup Talent notes in the Context Data portion of the glossary.


Impact Metics

Link back to explanation articles for LEBRON, RAPTOR, RPM, and BPM 2.0 from this page.

Rebounding, Turnover, FT Rate, and eFG% impact metrics use “Real Adjusted ____ Rate” stats from NBAShotCharts, which look at on/off impact by players in different areas of team success, while accounting for teammate and opponent quality.

Stable Metrics

These metrics use a similar padding approach as described here (and what we use for LEBRON) to make metric performance more predictive. This is done by infusing the actual sample of performance in the area of question with an expected performance sample. That expected performance is often impacted by a player’s attempt rate. For example, a player attempting 0.5 3s per game will have a lower expected 3PT% than a player attempting 4 3s per game. This is logical and taken care of using the bayesian approach followed for these calculations.