Wait What?
Player Tracking Plus Minus (PT-PM) is a player metric based on a WLS model against stabilized Plus Minus (RAPM) scores of NBA players last year using a combination of traditional box score data and publicly released SportVu data. This is a beta version, meaning it is still in development.
Ok, but... Why?
Good question:
- Because what the world needs now is another all in one basketball player metric.
- In order to better understand the relationship of the new SportVu data released by the NBA to efficiency, traditional box score metrics
- Ultimately to try to better understand the game of basketball. Though given that there is only one year of league wide data, this is only an exploratory analysis.
- For fun. People like rankings, they're the #1 click bait generator on the internet^{1}.
Hypothetically, if I was sitting home with a merged copy of SportVu player tracking data with box score data and possessions, how would I calculate it?
Glad you asked.
RAPM is split into offense and defense in terms of estimates of a player's impact on the floor, so I used the two scores to perform two separate regressions weighted by the number of possessions on the court for each player, using box score data and SportVu from NBA.com and RAPM from Jerry Engelmann's Stat's for the NBA.
The not-so secret formula for Offensive PT-PM (OPT-PM), with everything scaled per 100 possessions unless otherwise noted:
-4.123 + .594 * Points + -.559 * FGA + Passing Efficiency * 21.809 + -.587 * TOV per 100 touches + 4.241 * Contested Rebound Pct + .043 * Minutes per Game + -.247 * FTA + .138 * Rebs * Three Pt Rate
Here a couple of brief definitions of some of the new measures:
- Passing Efficiency is a SportVu derived metric that I detailed here, it measures the points created per pass attempts, it could also be thought of as Passing Effectiveness.
- TOV per 100 touches (TOVtouch100) is another SportVu derived metric that measures the number of turn overs committed per 100 touches of the ball. I went into that one in more detail here and here for the Celtics last year.
- Contested Rebound Pct, also via SportVu, this is the percentage of rebounds a player gets that are up for grabs with the opposing team
So what does that mean?
There are a couple of potential take aways from this study, I think.
Passing Efficiency was consistently more important than assist count in every random sub-sample run for regression and using a couple of different statistical filters, indicating that it gives a better measure of passing creativity and effective "ball movement" than the traditional measure.
The distance between TOVtouch100 and simple turnover count per team possession was smaller, though consistent, indicating that the additional control for touches is adding some context to the player's role not captured by other variables in the regression.
Finally, Contested Rebound Pct, I found last year that contested rebounds were mostly offensive, so it's not a surprise that this measure showed up as significant. It was a surprise that the relationship without respect to rebound count proved to be so consistently a better indicator than offensive rebounds. The analysis may be picking up some 'hustle and grit' vibe from this, though I think there needs to be further study on this one.
What about defense?
Oh, yeah that.
I also performed a WLS on the defensive RAPM score for each player. As is often the case, the data available gives a weaker fitting model than on the offensive side of the ball. But there was some indication of real explanatory power from the rim protection data from SportVu. In the end I chose to go with a more parsimonious, but stable, model.
The resulting formula is below:
1.605+ -5.627 * Opponent FG%Rim + .953 * Steal100 + .145 * OppFGARim + -.159 * PF100
The number of blocks a player has doesn't appear to be important once we have the SportVu rim protection data. In the formula OpponentFG%Rim is the percentage a players opponent shot at the basket when they were in position to contest the shot, the higher the opponent's shot percentage the less effective the player is on defense. The other SportVu related factor OppFGARim, is the number of times per 100 possessions the player was in position to contest a shot at the rim. The more they were in a position to contest the better.
Ok, where's the list? Pretty sure I was promised a list.
Sure, keeping in mind that this is a beta version. The defensive model, especially, is underpowered, but here it goes.
Below is the top 20 players with their Player Tracking Plus Minus scores and the original RAPM scores. Notice that LeBron James is at the top with Kevin Durant and Chris Paul are on top so our priors are at least satisfied.
A couple of the more surprising names this high up include Eric Bledsoe, who was listed as one of the best defenders in game by RAPM last year. Bledsoe was the best at protecting the rim among back court players last year. Also, Brook Lopez was surprisingly effect and active protecting the rim in the brief time he was healthy last year.
For all of the new metrics it is a real question how consistent players results will be year to year. Rim protection seems like it is particularly apt for variance, as defensive measures tend to have high variance in basketball.
Needless to say there's more work to do on this. Some of the issues I want to look at include exploring positional issues, looking at team and line-up data relationships, and turning it into a predictive measure.
1. Unverified
A version of this metric is currently performing best at predicting 2014-15 team wins out of about 25 entries at the apbrmetrics forum.
Posted by: Rj McKey | 02/26/2015 at 01:12 PM