12 months of big boards and mock drafts all gone in 48 hours. What can be done now to squeeze more just an ounce more of content out of all that hard work? I’m sure some 2020 mock drafts have already been posted, but that does require new scouting. Let’s not ring in a new draft calendar just yet. It is now draft grades season! You may have read ESPN’s big board, but now football bloggers can remind you which teams did and did not agree with them. Waiting for picks to see how draft picks turn out takes at least three years if we’re going to give players a fair shake, and that’s just too long! Nowadays teams have scouting and analytics departments. They have private interviews with prospects and their coaches no one else will ever see. They know their team fits and plans for the future better than anyone else. Nonetheless, knowing all of this is not as reassuring as seeing a beat writer put an A- by your favorite team. So I’ll stop testing your patience and let you see the grades I gave to every team, just let me explain how I used a computer model to grade every team’s draft performance given the picks they entered the draft with.
In all honesty, I don’t work for an NFL FO. I could watch film and claim I know how each team did, but that would take a long time and I still wouldn’t be as good as NFL front offices, so I automated the process. Here’s how I used the R language to create a computer model that graded all 32 teams’ drafts. I generated 32 random numbers. We’ll follow a normal distribution and use an 82% B- average and a standard deviation of 8 to get some interesting scores while still making it likely no one gets an F or exceeds 100. I ran one simulation to output 32 normally distributed numbers and assigned the numbers to all 32 teams based off alphabetical order. So the first number was assigned to Arizona and the last was assigned to Washington. Here’s the source code for my computer model if you are inspired to run a similar simulation.
draftgrades <- rnorm(32, 82, 8)
So without further ado, here are the draft grades produced by my model:
Looks like the jury is still out there on Kyler Murray. Sure is frustrating even computers have to wait and see how it works out.
My empirical model must have thought Atlanta was not getting great value for trading up to draft to first round linemen.
The model was not high on the Ravens’ selections. Time to start over for next year.
My model seems to confirm that the Bills were able to draft players who slid for no reason at all.
The Panthers received a raw score of 76.1 in my model. Sorry Panthers, it was not your draft.
Bears seemed to have found average value given their lack of draft capital.
With a raw score of 87.8, it appears the Bengals are trending upward in the first year of their regime change.
This model must not like cornerbacks who don’t tackle.
With a raw score of 82.8, this is right on par with the Cowboys 8-8 status quo.
Congratulations Broncos! Drew Locke was a smash hit and I can assure you Noah Fant was the correct value pick. With a 96.7 raw score, you had the second best draft.
The duality of life. We now reach our lowest score of 68.2. Drafting a TE in the top 10 just seems to be objectively wrong.
GREEN BAY PACKERS
This will not please Aaron Rodgers.
When the Texans front office professionals drafted Tytus Howard, it's possible they knew something Mel Kiper didn’t after 5 minutes of tape.
Sometimes you can’t avoid regression after a great year.
Josh Allen and Jawaan Taylor were no-brainers. How could 5,000 mock drafters be wrong?
KANSAS CITY CHIEFS
With a raw score of 83.4 the Chiefs were quite forgettable. I’m sure they’re fine with that for now.
LOS ANGELES CHARGERS
I can’t remember who they drafted.
LOS ANGELES RAMS
Too much trading annoyed me.
The Dolphins hedged their bets with the Josh Rosen trade. The model recognizes that.
Just part of the Vikings long march back to mediocre.
NEW ENGLAND PATRIOTS
I’ll have to fix the model for next year and remind it Bill Belichick has never made a mistake as a general manager. Never.
NEW ORLEANS SAINTS
Did they pick anyone beside Erik McCoy? Model seems to have not liked him.
NEW YORK GIANTS
Okay, so the point of this was that teams know their needs and the players they were interested better than anyone else, and no one really knows how good a player is going to be. I even defended the OBJ trade as not the worst thing ever for the Giants. But Daniel Jones blows. D- draft.
NEW YORK JETS
With a raw score of 71.0 my model is reminding the world that there is no such thing as a perfect prospect. Don’t fly too close to the sun Quinnen.
Just doing an adequate job with all those picks should be enough to help turn the Raiders around. Good job Jon.
The received an 87.7 raw score. I’m not going to pretend like I know much about Andre Dillard.
No Michigan bias here. With a 98.6 raw score, the Steelers found the most value in the draft.
SAN FRANCISCO 49ERS
I swear this model does not favor Michigan players and discriminate against Ohio State players. Although I don’t know. I didn’t program the rnorm function.
D.K. Metcalf will never work out. How did the Seahawks fall for him?
TAMPA BAY BUCCANEERS
Devin White is as advertised.
Hometown advantage likely played a large role in the Titans' success.
See, I gave Buckeyes a chance.
Before this report card and all of the others are exposed in hindsight, just remember, there is a universe where these grades are exactly right.