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 seven different offensive skills: Perimeter Shooting, Off-Ball Movement, One on One, Finishing, Roll Gravity, Playmaking, and Post Play.
A player’s perimeter shooting is grades on two factors: how good a player is at three point shooting compared to the openness of their shots and at what volume a player attempts three point shots. Openness is calculated using data from NBA.com/stats, effectively calculating an expected 3P% based off shot openness to compare to actual results. Volume is accounted for using a sigmoid function to regress low volume players. A sigmoid function was selected because it does not impact high volume players, while creating an S curve for regressing lower volume players towards 0. This category is aiming to grade what players best converted their three point attempts relative to how easy their attempts were.
Off-ball movement is graded using player tracking average velocity data and Synergy play type data. Average offensive velocity is a small portion of the final grade, but it’s included to improve accuracy players who were constantly moving well on offense. Cutting and off-screen Synergy play type data is adjusted for efficiency and volume on the play type. Again, a sigmoid function is utlizied to regress low volume players for Synergy data and low playing type players for movement data. This category is aiming to grade what players moved well without the ball and used that movement to create off-ball scoring opportunities.
One on One
One on one is graded using NBA driving data along with Synergy play type data. Driving data from tracking cameras is adjusted for efficiency and frequency. Isolation and handoff Synergy play type data is adjusted for efficiency and frequency as well. This category is aiming to calculate what players best used the ball in their hands to apply pressure on the defense and create individual scoring opportunities.
Finishing is graded using rim shooting data along with Synergy play type data. Shots within three feet of the rim and any dunk or layup attempts that occur outside of that range are adjusted for how often they are assisted versus unassisted and the volume at which a player shoots. Putback play type data from Synergy is used with an adjustment for volume, but none for efficiency. The rationale behind not adjusted putbacks for efficiency is that they are effectively free attempts at points and thus the expectation on those attempts is effectively 0 points. This category is aiming to calculate what players best made use of their opportunities around the rim, accounting for how those shots are created.
Roll Gravity is a newer concept that we have introduced as a part of the grades we put together. The idea behind them is simple: in the same way that a great shooter can pull defenders out of the paint when they’re off-ball, a great roll man can suck defenses into the paint to cover them. It’s an attempt to quantify a player’s vertical spacing.
Roll Gravity is graded using Alley-Oop data, NBA screen assist data and Synergy play type data. Alley-oop data is adjusted for volume and efficiency, and the specific play type is a great indicator of how much verticality a player is offensively able to provide in the middle of the court. Screen assist data, adjusted for volume to regress small samples, is used to indicate what players are best creating space for the ball handler in a pick and roll. Screen assists help clarify what players are creating opportunities for their teammates by setting a good screen that creates space for a teammate to score. Roll man Synergy data is used to measure an individual player’s efficiency when they receive the ball back from a pick and roll. It is adjusted for both volume and efficiency. This category is aiming to calculate what players do the best job creating vertical spacing for their team.
Playmaking is graded using NBA passing data, Synergy data and PBPStats passing data. From NBA passing data, and along with offensive role data calculated using this method developed by Todd Whitehead, an expectation for assists can be calculated using teammate efficiency, potential assists and passes. This expectation is compared to actual assists, allowing to measure how good a player was at creating assists within their offense. The other main factor is box creation, developed by Ben Taylor, which attempts to measure a player’s ability to create scoring opportunities for teammates outside of using just assists. This category is aiming to calculate how good a player is at creating opportunities for teammates with their passing, with role and teammates accounted for.
Post play is graded using Synergy play type data. Post up data from Synergy is compared to expected efficiency of the play and adjusted for volume attempted. Putback data is adjusted the same way it is for Finishing, accounting for the fact that they are effectively free points for the offense if they can convert. This category is aiming to calculate how good a player is at post activities.