Nylon Calculus: Who are the best NBA stat-stuffers at their dimensions?

Check out an app that finds the best performers at different stats (points, rebounds, assists, etc.) by their dimensions (i.e. their height, weight, and age). Who does the most at their dimensions?

Ask 10 basketball fans to name the best pound-for-pound scorer in NBA history, and at least nine will respond with Allen Iverson. He’s as good an answer as any — the 6-foot superstar used a dizzying array of dribble moves to turn all-NBA defenders into traffic cones throughout the early-2000’s.

But what if we only considered players’ peaks? Would Iverson still be number one? Or would that spot go to someone a bit… tinier?

Nate “Tiny” Archibald, who was officially listed at 150 pounds (15 pounds lighter than Iverson), averaged 34.0 points per game in the 1972-73 NBA season. That mark stands one point per game higher than Iverson at his most prolific (33.0 points per game in 2005-06).

That’s all fine and well, but what if we want to know about the tallest dime-droppers? The oldest marksmen? The shortest glass-cleaners?

I wanted to answer to these questions and more, so I built an app to find the answers for me: ‘The Outer-Dimensional NBA app.’

The tool allows users to choose a dimension (height, weight, or age) and then select a statistic and a time period. From there, all users have to do is hit “Make Chart,” and the app will respond with a graph of the best players at a given statistic for a given dimension and era.

There are countless findings to explore, like the fact that the most liberal fouler in NBA history was named ‘Monk’, or that the 5-foot-9 Nate Robinson managed to pull down 1.3 offensive rebounds per game in a season (just 0.2 per game fewer than the 7-foot-4 Boban Marjanović gathered in 2019). And, given that these stats are on a per-game basis, users are sure to stumble upon Wilt Chamberlain at some point.

The app also has a tab called “Outer-Dimensional Stats,” where users can explore the relationship between a dimension and a stat as a whole. This also generates interesting findings.

For instance, in the visualization below, we can see how rebounding load has become more evenly-distributed across heights in the contemporary NBA, as teams put more trust in guards to grab boards and push the ball up the court in transition.

There are a few shortcomings that should be addressed in future versions of the app. Firstly, these are almost all per-game stats, rather than per-possession, meaning that this data unjustly skews towards players on more up-tempo teams and in more fast-paced eras. Secondly, this app only deals with basic relationships, which limits more layered analyses and masks the distinctions between height and weight (tall players are often among the heaviest as well).

All that said, this app is a lot of fun to play around with, and I encourage you to give it a whirl and see what interesting things you can come across on your own.

Finally, I want to give shout outs to Andrew Patton, whose Pareto app was the inspiration for this project, and Todd Whitehead, whose advice helped me make this app usable and whose work continues to set the standard for visualizing basketball information.