Talent, not impact. The goal of these grades is to statistically measure how good a player is at a specific skill. That is a very different target than popular all-in-one metrics such as RPM, PIPM, and POE. Those metrics are designed to measure how much a player helps their team within their role by what they do on the court, which is an incredibly useful tool but one that does not help describe what a player specifically is good at. Those impact based metrics are still highly team and, especially, role dependent despite how good they are at adjusting for other players and opponents.
The most important thing to remember regarding these grades is that they are attempting to use every publicly available statistic to describe specific skills that a player has and to measure how good or bad the player is at that skill. These grades aim to take all of the added layers of information (coaching, scheme, teammates, etc.) and strip them back to just the underlying skills that a player has.
Players are graded on two different defensive skills and two rebounding skills: Perimeter Defense, Interior Defense, Offensive Rebounding and Defensive Rebounding.
Perimeter Defense is graded using Synergy play type data, NBA contest data, NBA movement tracking data, NBA hustle data and luck-adjusted on/off data. Perimeter defense is by far the most complex category to attempt to grade on an individual level. So much of perimeter scheme is about bending, not breaking and forcing opponents to shoot the shots they want. Furthermore, not all steals are equal. Some players gamble for steals, and while they may end up with two per game, they also may end up giving up three easy scores to the opponent because they gambled their way out of position. Despite these difficulties, the process for grading perimeter defense is as follows.
Three point shot contest data from the NBA is used to measure a player’s ability to get in position to make a shot more difficult. A Synergy blend consisting of pick and roll ball handler, isolation, handoff and offscreen is used to compared to expectation and volume in each play type is used to look at specific defensive actions, though this blend is a small portion of the final perimeter defense grade because of the well known errors in Synergy defensive data. NBA defensive movement tracking data is used as a proxy for players who are active on the defensive side of the court, however it is used as a small factor in the overall grade because activity does not equal ability.
The three other components of perimeter defense are steals per 75 possessions, regressed for small samples, deflections per 75 possessions, also regressed, and luck-adjusted defensive on/off data. The largest amount of weight goes to steals, deflections and three point shot contests.
Interior Defense is graded using NBA defensive rim tracking data and luck-adjusted on/off data. With interior defense, there are strong statistics for measuring rim defensive field goal attempts at the team level and individual level. Rim attempts contested are adjusted to account for how often each team allows a shot at the rim. Points at the rim allowed is adjusted for this frequency of rim attempts allowed and compared to the expected value of those shots if there was no contest at the rim. These are then adjusted another time for volume of attempts, to pull small samples down towards zero, and the expected value of a contest on the final total of points saved. The process is a slightly more complex version of what Seth Partnow, now with the Bucks, outlines here. Points saved from attempts at the rim is then combined with luck-adjusted defensive on/off data to incorporate a player’s defensive impact on defense overall with their specific impact for shots around the rim.
Offensive and Defensive Rebounding
The process for both offensive and defensive rebounding are nearly identical with one exception: the inclusion of putback Synergy data in offensive rebounding grades. Both use rebounding on/off data and NBA tracking rebounding data. NBA adjusts rebounding chance for deferred opportunities to create an efficiency of a player when they’re in a position to secure a rebound. This is than adjusted for volume of rebounds secured with double weight being given to contested rebounds. After regressing small sample players, this is blended with putback points per possession and rebounding on/off data to create a rebounding talent grade.