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.
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.
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.