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What is the best advanced statistic for basketball? NBA executives weigh in

·3 min read

Modern basketball debates often include one of the parties citing advanced analytics to prove their point. But are those metrics any good?

While some may shy away from numbers when talking about athleticism, others have embraced the statistical revolution. We were curious, though, which of those numbers we should reference in our player evaluations. Is there a catch-all, all-in-one composite metric that has the best reputation and has the most accurate assessment of a player’s impact on winning?

We wanted to find out so, for this story, we surveyed some of the most trusted thinkers in the basketball community.

HoopsHype received answers from nearly 30 participants, including various media members as well as individuals who have a combined experience with more than half of the teams in the NBA. Answers came from folks at every level within an organization, including those who work on a coaching staff as well as several different directors of analytics departments.

Most who answered spoke on the condition of anonymity because they are currently employed for NBA teams and felt that speaking publicly could reveal proprietary information about their teams.

But some like Cory Jez, who was the head of analytics for the Utah Jazz, were kind enough to walk us through the key principles that a team analytics staffer would want to incorporate in their analysis.

More representative all-in-one metrics need to capture the impact players can have when they don’t log a box score event (see: basically every Rudy Gobert possession ever),” said Jez. “It’s much harder to see the impact a player like De’Anthony Melton has compared to a different substitute like Lou Williams.”

Jez told us his criteria for a good formula would include possession-based data featuring more than just box-score metrics; methods that are Bayesian in nature that consider the individual player contribution and not just possession results; inputs that include tracking data; statistics that properly handle the sample size problem of single-season data by including historical information.

While we received overwhelming interest in this project from individuals like Jez, others declined participation. Some felt that as a whole, catch-all stats are flawed and do not very accurately measure talent or performance.

“I don’t really use any,” said one executive, who is the president of basketball operations for a team in the Eastern Conference. “They are all pretty bad.”

Others were less critical but felt that while all-in-one composite metrics are constantly getting better, the future of analytics is headed away from these measurements altogether.

“If I could add a wrinkle to your story, it would be that all-in-one stats are overused – that the next phase of basketball analytics is all about context-dependent numbers,” said another front office member from the Western Conference. “That would be the most honest quote I could give.”

This executive feels that analytics will move away from ridge-regression-based stats and instead attempt to answer questions about forecast future performance based on roles the player had for their team (e.g. and have metrics on lineup spacing, playmaking value and defensive versatility).

However, the most common feedback to the survey we received was that most teams focus on their own custom-developed systems when evaluating players.

But those measurements aren’t available to the public or to the media and can’t be readily cited. Ultimately, the goal of this project is to provide the most updated feedback on the evaluation tools that you can actually use.

Based on conversations with some of the most trusted names in basketball, here is what we learned about the state-of-the-art, publicly available metrics. The following rankings included below are sorted from least trusted to most trusted.

Main Image: Coley Cleary / USA TODAY Sports Media Group

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