In the world of basketball, a revolution has taken place, one that has shifted the sport's intellectual landscape. The analytics revolution, a movement that challenged traditional intuition, has left an indelible mark on the game. However, as with any paradigm shift, it has also created new challenges and blind spots.
The Power of Perspective
Don Nelson, a legendary NBA coach, understood the essence of basketball long before the era of analytics. He recognized that a team's success hinges on what players enable for each other, not just their individual prowess. Nelson's teams thrived because he built around this principle, creating a dynamic where every change affected everyone else.
The Analytics Revolution: A Double-Edged Sword
The analytics revolution provided basketball with a language to describe Nelson's instincts. It corrected historical misconceptions, highlighting the importance of 3-pointers over midrange shots and exposing the limitations of traditional box-score stats. But, as Goodhart's Law suggests, when a measure becomes a target, it can distort behavior.
In basketball, this meant that optimizing for individual metrics led to a neglect of the collective value that Nelson's teams exemplified. The focus on individual output created a myopic view, overlooking the limitations that can become strengths in a well-functioning team.
The Golden State Warriors: A Case Study
The Golden State Warriors dynasty is a prime example of this phenomenon. Klay Thompson, an all-time great shooter, understood his role perfectly. He shot when open and passed when not, never dominating the ball. This created space and opportunities for others, a team strength that individual efficiency metrics couldn't capture.
Similarly, Draymond Green, a playmaking forward, thrived because he couldn't shoot. This limitation forced him to pass first, making him an invaluable asset in the pick-and-roll. The circularity of their strengths and weaknesses elevated the entire system, a concept that first-generation models struggled to quantify.
San Antonio Spurs: A Different Perspective
The 2014 San Antonio Spurs offer another perspective. Their championship team, known for its ball movement, had a unique roster. While they lacked prototypical players in certain areas, their collective fit was exceptional. They ranked first in offensive efficiency despite being below average in free-throw attempts and offensive rebounds.
The Spurs' second unit, with players like Marco Belinelli, Patty Mills, and Boris Diaw, was a perfect example of collective strength. Individually, they might not have stood out, but together, they were a force. This highlights the need for metrics that capture collective value, beyond individual output.
The Future of Basketball Analytics
The first analytics revolution challenged inherited thinking about individual players. The next step, as Nelson understood, is to measure the whole sentence, to capture the collective value that makes basketball such a dynamic sport. This means developing metrics that account for screen assists, gravity scores, and lineup-adjusted individual value.
As we move forward, the challenge for basketball analysts and teams is to build an infrastructure that values collective strength over individual output. It's a complex task, but one that will ultimately lead to a deeper understanding of the game and, hopefully, more successful teams.
