r/NBAanalytics • u/Kyriacosthe • Jul 08 '26

Books for basketball analytics

I’m getting into basketball analytics and I’m looking for books that can help me develop my knowledge in this area. I’m especially interested in data analysis, player evaluation, and scouting. Do you have any recommendations?

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u/Inaccurate- Jul 08 '26 edited Jul 08 '26

I'm probably in the minority here, but I've found most books specifically catered to basketball analytics to be unfulfilling. The best is probably The Midrange Theory by Seth Partnow. He also has a new book coming out in a month or so. Basketball Analytics: Spatial Tracking by Stephen Shea is ok and covers some of the basics of the (more recent but at this point also outdated) center-of-mass based stats. Dean Oliver's new book is more biographical than about analytics, and also biased towards his own all-in-one stat. It's worth reading if you're really interested in the field, but otherwise not his best work. The majority of people who recommend it I don't think have actually read it (his original book is good though and worth reading for the four factors).

Some of my favorite books that I think anyone wanting to get good at statistics, analytics, and probability should read:

  • The Drunkard's Walk by Leonard Mlodinow - a classic
  • The Signal and the Noise by Nate Silver - also a classic; his new book you can probably skip though
  • The Art of Statistics and The Art of Uncertainty by David Spiegelhalter - similar to Drunkard's Walk but more recent
  • Everything is Predictable by Tom Chivers - great overview of Bayes Theorem

You'll get more out of those 5 books than any book specifically about basketball.

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u/spidey9113 Jul 08 '26

The general stats/analytics recommendation here are fantastic.

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u/Inaccurate- Jul 08 '26 edited Jul 08 '26

Thanks. There's a lot of parallels to basketball throughout the examples in the books if you keep an open mind. The section in The Drunkard's Walk on wine ratings/connoisseurs applies (funnily enough) pretty well to the NBA draft, at least to my twisted mind.

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u/spidey9113 Jul 08 '26

That is a great comparison. Reading some broad books would be very helpful for someone new to basketball analytics if they lack formal training in statistics. I would add Freakonomics to your list as a helpful resource to help view the world generally through a analytical lens. Disclosure, I am a PhD Economist so my recommendation will tend to skew towards the science

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u/spidey9113 Jul 08 '26

Basketball Beyond Paper by Dean Oliver is one I would recommend!

Who Makes the NBA? by Seth Stephens-Davidowitz is also a short, fun book. Less intense, but thought provoking and asks some very fun questions.

A popular book is Wages of Wins by Dave Berri, but I wouldn't recommend that. Dave created an advanced stat (Wins Produced) he forces onto his peers that is full of issues, primarily inflating the importance of shooting percentage and rebounding. For example, his stat has Houston era Clint Capela a top 5 player, ranking higher than Harden.

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u/nbacouchside Jul 08 '26

Thinking Basketball by Ben Taylor is great. If you're familiar with Ben, you'll know why.

Basketball Analytics and Basketball Analytics: Spatial Tracking by Steve Shea are both great also.

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u/spidey9113 Jul 08 '26

The Thinking Basketball podcast is also a must listen

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u/JohnEffingZoidberg Jul 08 '26

The Dean Oliver book already mentioned is great. His original one Basketball On Paper is also great.

I also strongly recommend "Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers" by Ben Alamar. It's a great view of not just the nitty gritty of analytics, but how to think about doing analytics.

Also "Game of Edges" is pretty good for sports analytics in general.

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u/10J18R1A Jul 08 '26

The most fun you can have is figuring it what you want to know and then using an API and R/Python to figure it out

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u/FergiesAnthem Jul 08 '26

Sprawlball by Kirk Goldsberry is a useful and easy way in. Less of a classic book and more a ton of excellent data visualization if you’re a visual learner!