Updated on 4/23/2019.

The goal of this is to predict NFL success as accurately as possible solely using analytics. Specifically, I’m predicting Pro Football Reference’s Approximate Value Per Game (AV/G) statistic using models that have college production, athletic testing, and consensus big board data.

After analyzing AV/G, I found that the statistic isn’t representative of positional spending and consensus positional value, so I’ve made slight adjustments in an attempt to have my top 300 big board reflect positional value, something that seems to be underrated during the draft. Adjusting for positional value will certainly make some people #MadOnline because the quarterbacks will be ranked higher than you are used to and the running backs and safeties will be ranked lower than you are used to. If you have a problem with that, ignore the overall ranking and focus on where they are ranked within their position.

Another thing that needs to be made abundantly clear is that these rankings include each prospect’s projected draft pick. There is a huge difference between ranking prospects with their draft capital in mind (that’s what I’m doing in the NFL Draft Analytics Top 300) and ranking prospects as if they were all drafted with the same overall pick. For example, projected top-15 pick Daniel Jones and projected Day 2 pick Will Grier basically have the same projection, but if Grier and Jones were both projected to be the 10th overall pick, Grier would be ranked much, much higher than Jones.

Lastly, one improvement I’ve had in modeling for the NFL Draft is to separate positions into different archetypes. Like Josh Norris has noted on the Rotoworld Football Podcast -- subscribe and leave a review -- the weight difference between receivers Marquise Brown and D.K. Metcalf is 62 pounds, so it’d be silly to use the same model for each receiver. For that reason, I’ve split running backs, wide receivers, defensive tackles, and edge rushers into groups using weight or height as the separator. Doing so helped increase the out of sample r-squares for each model.

NFL Draft Analytics Top 300

Just wanted to remind you that the models include projected draft capital and are adjusted for positional value. That's why Daniel Jones is ranked highly when he's absolutely not an analytics quarterback.

Continue to Page 2 to view the variables in the models and some nuggets I picked up while studying the recent history of the NFL Draft.