A Graphical History of Moreyball

A Graphical History of Moreyball

One of the most prominent narratives of the 2018-19 NBA season is about the dominance of Moreyball across the league – an overriding aversion to low-efficiency mid-range shots, in favor of Morey-approved looks at the rim and from three. What does this shift look like? Is the typical three-pointer really that much more efficient than the typical 18-footer? How radically has league-wide shot selection really changed? And can this change be traced to Morey’s Rockets?
Draft Combine Measures & Defense, Part Three - Using Neural Networks to Predict Blocked Shot Rates

Draft Combine Measures & Defense, Part Three - Using Neural Networks to Predict Blocked Shot Rates

Intro In previous iterations of my draft combine and defensive performance posts, I’ve assessed the basic distribution of combine measures like height, wingspan, vertical jump, and sprint speed; described how these measures correspond to basic measures of defensive and rebounding prowess; and fit linear models to the combine and performance data, estimating the influence of each combine measure in the presence of all the others and providing for a baseline predictive model.
Coaching Trees and Coaching Graphs

Coaching Trees and Coaching Graphs

The 2018-19 NBA season has started, Spoelstra apprentice David Fizdale has taken charge of the Knicks, Pop acolyte Mike Budenholzer is in Milwaukee, and journeryman assistant Igor Kokostov is leading the maybe-not-terrible Suns. In other words, it’s an ideal moment to gaze at coaching trees. As quickly becomes apparent from the data, however, full NBA coaching trees are better thought of as gnarled bushes, with head coaches and assistants bouncing around the league, forming multiple, inter-woven connections.
Draft Combine Measures & Defense, Part Two - Full Inferential Models

Draft Combine Measures & Defense, Part Two - Full Inferential Models

In Part One of my look at draft combine measures and defensive performance, I graphed out some basic relationships among the combine measures (e.g. how height and wingspan tend to relate to each other), as well as some basic relationships between these measures and future defensive performance. To summarise briefly: there’s a very regular, linear relationshp between height and wingspan, but when looking at either measure in a vacuum, wingspan tends to do a bit better in predicting future performance.
How Much Does Thibs Love His Former Players?

How Much Does Thibs Love His Former Players?

Tom Thibodeau loves his former players. Since taking over as coach of the TWolves, Thibs (who also serves of president of basketball operations) has acquired the services of no fewer than six players he coached on the Bulls: Jimmy Butler, Taj Gibson, Derrick Rose, Aaron Brooks, John Lucas III, and most recently Luol Deng. Tom Thibodeau loves his former players. But how extraordinary is this love? Do other coaches surround themselves with their former guys when they move to new teams?
Manu Ginobili: A Shot Chart Commemoration

Manu Ginobili: A Shot Chart Commemoration

Manu Ginobili retired last week after a 16-year, 4-championship career that will likely see him land in the HOF and was undoubtably great. When a player has a run as long and as durably impactful as Manu’s, there’s a natural tendancy to remember him as you last watched him play and to forget the explosive, luxuriously maned 25-year-old rookie. To remind myself of the player that Manu was been throughout his career, and to get a better understanding of his trajectory as a scorer, I’ve put together a statistical summary of where on the court Manu got his shots, how efficient he was from different spots, and how this changed over his career.
Draft Combine Measures & Defense, Part One - Basic Relationships

Draft Combine Measures & Defense, Part One - Basic Relationships

When we talk about the defensive potential of incoming NBA players, we’re usually referring to a set of physical attributes – height, length, physical strength, footspeed, etc. – that can may be improved upon at the margins, but essentially exist as raw athletic foundation upon which a defensive stalwart can be built. But to what extent, and in what combination, do these building blocks actually predict future defensive performance? This post is the first in a series that aims at answering that question.
Clustering High Scorers by Shot Type

Clustering High Scorers by Shot Type

People like arguing about the relative greatness of great basketball players. This desire often sees itself expressed in…less than optimally informed ways. One way to help sharpen such arguments is to get a better feel for which players are more or less similar to each other, and thus what specific player comparisons are most salient fodder for Twitter spats. Such is my task in this post: to arrange the best scorers of the past several decades into distinct flavors, determined by where an the court they get their looks.
Where Guys Are From, Part One - Heat Maps

Where Guys Are From, Part One - Heat Maps

I’ve at some point described every place I’ve ever lived as “a good town/city/region/state for basketball”. As clearly as this judgment is tainted by a sort of home-spun narcissism rootless tribalism goodhearted loyalty, it’s also clear that some places are better than others. Which are those places? To begin answering this question, I pick from Basketball Reference’s extraordinary stock of information, scraping their Birth Places page to get the country/state and city of birth for every player in NBA history.