Which Combine Drills Really Matter For Running Backs?

The NFL combine workouts start Friday in Indianapolis, and football fans across the nation will dissect every result. We can gather that being bigger, stronger, faster, quicker, and having jumping ability are desirable traits. But what do they mean for NFL success? The concept that faster players are better is easy to grasp, but how do we evaluate the totality of drills against each other? Is a fast and less agile running back better than the opposite? Does the broad jump matter more for taller players? And how do all the drills work together to forecast NFL success? I had been having trouble answering those questions for myself, and adding multiple measurables to linear or logistic regressions can muddle the results due to multicollinearity. I decided to build a different type of model, a regression tree, to look only at NFL combine results and categorize results by NFL success. In this post, we’re going to look at running backs, and I’m defining success for a prospect as having at least one top-12, or RB1 season (PPR scoring) in his first three years. Here’s how to read the regression tree nodes. The “yval”, or predicted value, in this case is the likelihood of success (from 0 to 1). The darker the node, the higher the yval. tree I plugged into the regression equation the following NFL combine measurements for running back prospects from 2000-2013: height, weight, 40-yard dash, short shuttle, three cone, vertical, broad, and bench. The regression tree does the work of figuring out which variables are most important, and how we can classify different athletic profiles by their chance of NFL success.

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By Kevin Cole | @Cole_Kev | Archive

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  1. This is outstanding! What program is producing the graphics for your decision tree?

  2. This was my favorite article of the past year. That's not hyperbole. And I've ready every single article. (That's not hyperbole either.)

  3. Every time I read one of these pieces I think about this

    But seriously, awesome stuff Kev, love this kind of analysis

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