One of the most important characteristics of a basketball player is their playmaking ability. Not their ability to make a certain pass — that’s just one part of the equation. LeBron James, Chris Paul, and Steve Nash aren’t considered some of the best playmakers in league history because of their ability to pull off the flashiest passes. Instead, they are recognized for their brilliant ability to create more efficient opportunities for their teammates to score.
Unfortunately, this is a rather difficult skill to quantify. We mostly just settle with looking at a player’s total number of assists, but this is a pretty imperfect measure. Players with strong playmaking ability should certainly be beneficial to an offense’s productivity, yet the correlation between assists and team success is quite weak.
Each point represents a team’s season performance. I used data from the past 10 seasons, so with 30 teams, there are 300 points in each graph. Assist rate is defined as the percentage of made field goals that are assisted. Both graphs clearly demonstrate the lack of a strong relationship between assists and team performance. A few points at the top right of each graph may suggest otherwise, but they just represent the dominant Warriors’ dynasty. The Warriors led the league in assists for five straight seasons, but, as we saw in 2020, their offensive system would not work with just any players.
There’s also the fact that assists are extremely subjective at their core. An assist is defined as a pass that directly leads to a made field goal. But what exactly counts as being direct? How long can the scorer hold the ball before shooting after receiving a pass for it to still count as an assisted field goal? There isn’t a set definition — it’s entirely up to the interpretation of the scorekeeper.
So, I don’t think assists are a great way to quantify playmaking ability. But what is?
My idea is to use the NBA’s granular passing data to quantify playmaking ability. I could determine how efficiently each player’s teammates shoot on passes from that player and how efficiently they shoot on passes not from that player. The difference between the two could potentially represent the impact of that player’s playmaking. For instance, if Player A’s teammates shoot an eFG% of 60% on passes from him, while they shoot an eFG% of 55% on passes from their other teammates, we’d credit Player A’s playmaking ability for the 5% increase in eFG%.
It’s clearly a narrow-minded approach that ignores many other variables, but I’m curious as to what the results would look like. And here they are:
These are the full results for players this season who have made at least 600 passes to a teammate who subsequently shot the ball (which I’m calling “Assisted FGA”).
The very top of the leaderboard isn’t surprising. LeBron James’ teammates is enjoy an average 7.04% eFG% increase when shooting off of passes from him, the biggest boost in the league this season. After all, LeBron is one of the greatest playmakers in league history.
Trae Young, Draymond Green, Damian Lillard, Bam Adebayo, Ben Simmons, and Luka Doncic are also all very gifted passers and their impact is shown in these numbers.
But there are certainly some peculiar results. Nikola Jokic is widely considered the best passing big man in NBA history, yet his teammates’ eFG% apparently dips 1.44% when they shoot off of his passes. What could be the explanation for this bizarre result? I’m not sure. I have a shoddy theory, though. He touches the ball more than any other player in the league — the entire offense revolves around him. Maybe the plays where his teammates receive passes from other players occur when they’re already in great scoring situations, so the team opts not to wait for Jokic to open things up (like fastbreaks). But that probably doesn’t explain it completely. Those situations aren’t exclusive to the Denver Nuggets.
So, this metric is probably not an immaculate measure of playmaking ability. It has clear flaws and, as one may expect, some of the results just don’t match the eye test at all. But hey, the numbers are pretty interesting. Just don’t use them to reach any big conclusions.