nfl draft

Podcast Appearance: The Football Analytics Show

I made an appearance on Ed Feng’s Football Analytics Show, which is associated with his fantastic content over at The Power Rank

We discussed my research, how to find undervalued running back and wide receiver prospects, and my bold prediction for the 2017 NFL draft (that ended up coming true): A team would trade into the top-11 picks and select Patrick Mahomes before DeShaun Watson.

To listen on iTunes, click here.

Or visit Ed’s site for embedded audio.

Deshaun Watson, Mitchell Trubisky and the Importance of Sample Size

I tend to ignore the specifics of most NFL mock drafts, but I was happy to see Rotoworld’s Josh Norris recognize the importance of sample size while recently predicting that the analytical wizards with the Cleveland Browns will prefer Deshaun Watson to Mitchell Trubisky come draft day.

(We should mention that everything written below regarding Watson’s larger sample also applies to Patrick Mahomes, who had more pass attempts than Watson at a similar yards per attempt.)

To those who are familiar with statistics generally, the concept that the Browns would want a larger sample for their potential franchise quarterback isn’t tremendously difficult to grasp. But it was still a pleasant surprise to see someone in the larger draft community weave this thinking into his analysis, especially when some mock drafts still forecast the Browns to make terrible strategic decisions from an analytical perspective, including taking a running back in the middle of the first round.

We know that bigger is better when it comes to sample size, and various models, including Football Outsiders’ QBASE – developed by now Browns’ senior strategist for player personnel Andrew Healy –  have shown that quarterbacks with longer college careers are more likely to be successful in the NFL. But is there a way we can truly peel apart the analysis and see the nuts and bolts of why this is so? (more…)