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Into Basketball And Beyond: Review Of ‘The Midrange Theory’

The Athletic’s Seth Partnow illustrates the development of basketball analytics, which reaches far beyond the sport.

Editor’s note: to my knowledge, this is Brew Hoop’s first (only?) book review, and it is not a sponsored post. The relevance of the author’s experience and first-hand knowledge of the Bucks’ basketball operation brought the idea of a review to the surface, and I think the contents of Seth’s book are worth the time and effort. There, disclaimer delivered. –MM

The game of basketball is complex enough. While the dimensions of the court and height of the basket might remain static, the game itself is unpredictable (to an extent) because it is played by people, who tend to complicate matters whenever they’re involved. After all, knowing that a guard is running a pick-and-roll on the right wing and that the defense is trying to impede their progress is already a lot consider...but what is done pales in comparison to who is involved, in terms of intricacy.

Seth Partnow, current NBA analyst for The Athletic and former Director of Basketball Research with the Milwaukee Bucks, goes to great lengths to illustrate not just the what, but the who behind modern basketball, both on the court and off, in his new book titled The Midrange Theory.

In reading through the book, I found myself learning far more about people than about basketball. That isn’t to say that I didn’t learn something about the game (I learned more than a few things!), but that there is so much that the human element of sports affects and influences that most fans fail to consider, even those who are proponents of “analytics” in the NBA. Moreover, the details of how the human element impacts things translates to all walks of life, not just professional sports. It just so happens that professional sports is an environment richer in measurables and metrics than other parts of society.

One of the major themes present in The Midrange Theory is the importance of language, and by extension, communication, as a bridge between data and an idea. Chapter 2 goes into detail about the relationship between numbers and words, and the necessity of using the former to support the latter in building a case and influencing a decision. Partnow describes the lengthy period between player tracking data being recorded and actually being utilized, starting with a question from a notable NBA figure.

“Of what possible use is that information?” was the reaction of long-time NBA head coach Stan Van Gundy at the 2014 Sloan Sports Analytics Conference upon learning that then-Pacers forward Paul George had run almost 130 miles to that point in the season, the highest such figure in the league. Of course, Van Gundy was correct. Perhaps if George were competing in a cross-country meet, miles traveled would be a useful data point. But basketball players aren’t measured in miles, they are measured in buckets. And, as it turns out, plenty of other things.

Enter the data people, including but not limited to Second Spectrum.

“When people talk about their problems, they talk about it in words,” [Rajiv] Maheswaran says. “Words are how they describe strategy or understand the game or help to develop a player. People use words to describe what they want to know and what they want to happen.”

What Van Gundy and other coaches wanted to know were basketball strategic questions, such as, how well do certain teams and players defend the pick-and-roll, and if they are successful in defending the play, how did they do so?

Now, seven years after Van Gundy’s incredulous comment, we know so much more about the sport because the right people have been able to link data and ideas using words. In the excerpt above, I wish that the quote from Rajiv Maheswaran (CEO of Second Spectrum) had continued on to say, “People use words to describe what they want to know and what they want to happen...and people use data to prove whether the option they chose worked.” (emphasis/additional words mine). That link is crucial, and alludes to a dynamic that applies universally, especially outside of basketball.

If you’re looking for a successful example to apply this story to, look no further than the Bucks’ acquisition of Brook Lopez, something that Partnow was directly involved with and has clearly worked out. We already know the results of that process, that Lopez came to Milwaukee and became a stalwart defender of the paint whose efforts boxing out allowed Giannis Antetokounmpo to maximize his talents, eventually culminating in the 2021 NBA championship. But when it comes to the process itself, Partnow is able to give us an idea of what thought went into the decision, and along the way we get a lesson in the difference between individual and team statistics, and how indicators can be missed by the “popular consensus.”

We believed the perception of him as a player around the league was overly negative, more about his previous contract than his current contributions. More importantly, we had reasons to believe that his defensive shortcomings were overstated. His poor reputation in that area was largely due to both his poor individual rebounding totals and his inability to function in the aggressive defensive schemes in vogue around the league for much of his career.

Do you want your center to get the rebound, or do you want someone on your team to get the rebound? Considering the question now, the answer seems obvious, almost rudimentary! Of course you would rather Giannis or Khris Middleton or Eric Bledsoe or Jrue Holiday to end up with the board and launch into transition, rather than Lopez, who is going to need to pass the ball over to another player in pursuit of an early opportunity or else need to hold things up and eat away at precious shot clock seconds. But for years – decades, really – defensive rebounds per game was the metric that mattered, because it showed that a big man could handle collecting missed shots to regain possession. That by itself has utility...but it’s also the rationale that leads to players like Andre Drummond getting contracts that their teams will never get the most out of.

These are the types of questions that people ask every day, but so many of us are intimidated by the idea of analyzing data, even to the point of avoiding spreadsheets unless absolutely necessary. Again, this is not a basketball problem, it’s a human one, and one that rears its head anywhere and everywhere that people exist. So, y’know, everywhere. This is why it’s so crucial to tie data and words together, so that we can at least have a chance of making better decisions and experiencing better outcomes. Seth goes into specifics of how this is applied in the basketball world, but the lessons resonate far beyond the sport. That’s the mark of something special and worth celebrating.

As with anything, the book isn’t perfect, but the critiques I can muster are largely cosmetic and likely a matter of preference. First, there are a multitude of footnotes in each chapter, occasionally taking up as much space on the page as the writing itself. The footnotes themselves are worthwhile (my personal favorite concerns Partnow’s unauthorized pseudonym for his personal high score), but the experience of reading through the book, finding a footnote, reading the footnote, and then returning to the book is jarring. Second, the book jumps from topic to topic between chapters, making it feel almost like a collection of essays; this is very much a stylistic gripe. The story of The Midrange Theory is told perfectly well as-is, but part of me wonders if it could have been better served if structured with more of a natural flow from section to section, taking the reader on a journey through a story rather than a guided tour through different aspects of professional basketball analysis. Then again, when a writer abandons brevity and attempts to weave a tapestry for the audience, often enough the writer is simply writing for themselves, getting in their own way and providing a worse experience when really they could just convey what needs conveying and get on with it and…oh, no, I’m doing it again. Let me move on. Last, but certainly not least, I read Chapter 7 and my immediate reaction was “hey, I did that first!” so, not cool, man.

(This is the part where I abruptly claim that I did a thing first, ignoring that my attempt was amateurish and merely scratched the surface of the topic, whereas this work is literally a professional product that goes so, so much farther and into more detail than I could have managed, and avoiding at all costs admitting that I’m simply jealous that Seth did it better, because he’s really good at this stuff.)

(But seriously, Seth is really good at this stuff.)

My absolute favorite part of the book is the final full chapter, which I won’t spoil here, but I will share that it concerns the future of NBA analytics and what opportunities exist for the next generation of basketball aficionados and data nerds. It is an ending about the concept of “not ending,” which makes sense once you read it. Which you should.

There is so, so much more contained within The Midrange Theory, and much of it pertains to basketball. For that reason alone, I could not recommend the book enough. But if you also have interest in other topics, like change management or organizational growth (check out Chapter 12 for that!), or the process of identifying bias and noise (so that you can account for it) in decision-making, or the relationship between human performance and incentives (Goodhart’s Law, Chapter 4!), or how to differentiate between “activity” and “achievement,” then this book is a must-read. I didn’t bother creating a rating system for this review until now, so I’ll give The Midrange Theory a score of 5/5 Giannis Mean Mugs.

Go read Seth’s book. If you already have, discuss the book here in the comments! Ekpe Udoh doesn’t have a monopoly on book clubs! If you haven’t, find a way to get a copy of The Midrange Theory, you’ll be glad that you did.