mock draft roundup
Image courtesy of Vikings.com

I’ve gathered boards from 43 different rankers (Kiper was a late addition), 13 of them “Big Draft” media types and the other 30 as amateur evaluators. I’ve also updated the big boards from CBS, Matt Miller and NFL.com as well as a few others, but for the most part rankings haven’t changed since the last time I’ve posted them.

To go over the process again, you can click here. It describes how the board is built, and how we come up with the different categories, like “most polarizing player,” “biggest disagreements” and so on. As a quick summary, we’ve separated the rankings into two big boards: “forecasters” and “evaluators.”

Forecasters are primarily in the business of predicting how the draft will go (or, more accurately, their big boards tend to do that) in large part because of their access to information the NFL has, which can be good and bad—critical information like the nature of off-field issues and injuries are incorporated, but so are the lies teams feed the media.

Evaluators rely more on the data they can acquire from game tape (when available), the broadcast angle of games and publicly available quantitative information, like combine measureables and statistics. Many will rely on media reports, but recognize the limitations of that when applying injury or character information.

 


Biggest Disagreements

Last year, when forecasters disagreed with evaluators, the forecasters batted nearly 1.000 on figuring out who was going to be picked higher. Looking at these disagreements may also reveal information teams have revealed to forecasters, but has not leaked to media at-large, or it may incorporate biases from specific team evaluations, like prioritizing certain builds or traditional evaluation models.

Rank Player Eval Forecast Difference Score
162 Sean Mannion 187 115 1.69
15 La’el Collins 13 27 1.62
180 JaCorey Shepherd 203 128 1.59
138 Mitch Morse 169 106 1.50
49 Eric Rowe 42 72 1.40
34 Breshad Perriman 40 22 1.33
78 Donovan Smith 89 54 1.32
13 Trae Waynes 16 8 1.20
129 Shaquille Mason 121 181 1.17
188 Kyle Emanuel 205 142 1.04
17 Marcus Peters 14 25 1.04

La’el Collins shows up there before being called in to cooperate with the Baton Rouge police, though I think most people would drop him pretty uniformly.

Some character players show up, as do players with divergent skillsets (Breshad Perriman is really good at some things and bad at other things for example) and Sean Mannion, of course.

This sort of stuff is endlessly fascinating to me, and hopefully we’ll figure out some long-run inefficiencies from this.

Biggest Agreements

Not a particularly interesting set of things to look at, but it’s kind of cool when we see players evaluated the same way by forecasters as they are evaluators. It may mean a clean or well-known character and the dominance of obvious traits on film.

Rank Player Eval Forecast Difference Score
194 Bryce Hager 194 191 0.00
179 Durell Eskridge 179 179 0.00
79 Rashad Greene 80 80 0.00
67 T.J. Yeldon 68 68 0.00
52 Laken Tomlinson 51 51 0.00
39 Dorial Green-Beckham 39 39 0.00
18 Malcom Brown 18 18 0.00
9 Todd Gurley 9 9 0.00
5 Kevin White (WR) 5 5 0.00
2 Jameis Winston 2 2 0.00
1 Leonard Williams 1 1 0.00

I’m not really sure why Dorial Green-Beckham is there. The two megaboards generally agree on where he should be placed, but the individual boards (as you’ll see below) vary wildly.

 

Most Polarizing Players

These players drew the biggest disagreements, relative to their draft position, among the 43 big boards.

Rank Player School Polar Score
9 Todd Gurley Georgia 7.3
39 Dorial Green-Beckham Missouri 4.2
2 Jameis Winston Florida St 3.8
172 DeAndre Smelter Georgia Tech 3.0
12 Randy Gregory Nebraska 3.0
49 Eric Rowe Utah 2.8
166 Frank Clark Michigan 2.3
198 Ellis McCarthy UCLA 2.3
14 Danny Shelton Washington 2.2
19 Shane Ray Missouri 2.0
4 Marcus Mariota Oregon 2.0
26 Eddie Goldman Florida St 2.0
34 Breshad Perriman Central Florida 2.0
7 Vic Beasley Clemson 1.8
20 Andrus Peat Stanford 1.8
54 Cedric Ogbuehi Texas A&M 1.8
66 Damarious Randall Arizona State 1.7
30 Shaq Thompson Washington 1.7
71 Ty Sambrailo Colorado St 1.7
83 Tre’ Jackson Florida St 1.7
22 Arik Armstead Oregon 1.7
68 Mario Edwards Jr. Florida St 1.6
78 Donovan Smith Penn St 1.6

Injury, character and inconsistent play dominate this board. There are also a fair amount of “raw” players and system concerns. Also, Vic Beasley, I guess.

 

Least Polarizing Players

These players were astoundingly consistent across all the individual boards, even at odd places like rank 134 or what have you.

Rank Player School Polar Score
193 Bryce Hager Baylor 0.2
162 Mike Hull Penn State 0.2
190 Wes Saxton South Alabama 0.2
171 Blake Bell Oklahoma 0.3
138 Christian Covington Rice 0.3
148 Jake Ryan Michigan 0.3
197 Ladarius Gunter Miami FL 0.3
154 Vince Mayle Washington State 0.3
194 Derrick Lott Chattanooga 0.3
200 Jalston Fowler Alabama 0.3
132 Jamil Douglas Arizona St 0.3
50 Devin Smith Ohio State 0.3
192 Xavier Williams Northern Iowa 0.3
186 Karlos Williams Florida St 0.3
73 Tyler Lockett Kansas St 0.4
189 Malcolm Brown Texas 0.4
143 Antwan Goodley Baylor 0.4
133 Kurtis Drummond Michigan St 0.4
164 Dezmin Lewis Central Arkansas 0.4
174 Matt Jones Florida 0.4
167 Quandre Diggs Texas 0.4
57 Ameer Abdullah Nebraska 0.4
69 Clive Walford Miami (FL) 0.4
79 Rashad Greene Florida St 0.4
173 Sean Hickey Syracuse 0.4
146 Kenny Bell Nebraska 0.4
118 Tony Lippett Michigan State 0.4
163 Lorenzo Doss Tulane 0.4
160 Taiwan Jones Michigan State 0.4
176 Titus Davis Central Michigan 0.4
114 Jamison Crowder Duke 0.4
144 Josh Harper Fresno State 0.4
178 Durell Eskridge Syracuse 0.4
62 Nate Orchard Utah 0.4
177 Darren Waller Georgia Tech 0.4

 

The Board

We have the board below, but first a fantastic probabilistic model based on the boards for determining the likelihood that a player survives to a particular pick. This was sent to me by @combinePhysics who used the boards to (to the best of my abilities, this is my guess) look at aggregate rank and distribution of ranks for each player to see how likely it would be that they’d be at a particular spot.

BB42_EstimatedDraftDistribution

 

It did a great job last year, so it should be fun to track this year.

Below, we have players sorted by overall rank, but you can also see the most likely “role” they’ll play, like 5-technique, flanker, etc. and the position they’ll likely play, like wide receiver. So if you wanted to see the slot receivers compared against each other, you can click on the “SWR” tab at the bottom, or if you just want to see how all receivers compare to each other, the “WR” tab. As a review:

Position-Role Cheat Sheet

Alternate Boards

There are alternate boards, too.

Here’s the board for when you simply average all the ranks.

Here’s the relatively flawed board when you simply take the median of all the ranks.

This is what happens when you combine the two above approaches, and simply take the average rank for players when excluding ranks in the top and bottom quartiles. It gets rid of outliers while preserving the idea of averaging ranks instead of my normal method, which gives more of a bonus for a high rank than a penalty for a low on.

Here’s what happens when you weight forecasters and evaluators equally, instead of weighting each individual set of ranks equally.

UPDATE: As Cshedahi points out in the comments below, I didn’t include links to the final forecaster and evaluator boards! Big mistake, as those are pretty interesting. Embedded below. First, the evaluator board:

Next the forecaster board: