revolutionary tools.  groundbreaking articles.  proven results.

The 2017 RotoViz Scouting Index – Composite Rankings – Final Version

The 2017 NFL draft is just a couple weeks away. Through the combine and pro days, we’ve tracked and aggregated scouting evaluations from around the web. Here then is the final look at the draft stock of this year’s prospects.

The RotoViz Scouting Index (RSI) methodology allows us to gather evaluations from many different scouting sites and score them in such a way that we can track changes in a player’s stock, not only over time, but across positions. Instead of providing position-by-position analysis in this final pre-draft iteration, I’ll provide composite rankings and links to our best positional analysis. That way, you can just enjoy the rankings, or dive deeper to see which players might be ranked too high or too low.

As a reminder, these aren’t RotoViz rankings. These are rankings compiled from seven different traditional scouting web sites. These rankings are useful for informing your awareness of how the larger football community values these prospects.

RSI Composite Pre-Draft Rankings

The table shows each player’s overall rank and standardized score,1 followed by their positional ranking.

WRCorey Davis12.051
WRMike Williams22.032
RBLeonard Fournette31.911
WRJohn Ross41.873
TEO.J. Howard51.821
RBDalvin Cook61.812
RBChristian McCaffrey71.593
TEDavid Njoku81.452
QBDeshaun Watson91.391
QBMitchell Trubisky101.342
TEEvan Engram111.263
RBAlvin Kamara121.244
WRZay Jones131.184
WRJuJu Smith-Schuster141.165
QBDeShone Kizer151.163
WRCooper Kupp161.066
WRCarlos Henderson170.907
TEBucky Hodges180.854
QBPatrick Mahomes190.834
RBD’Onta Foreman200.795
RBJoe Mixon210.795
WRChris Godwin220.788
RBWayne Gallman230.747
WRCurtis Samuel240.689
WRIsaiah Ford250.6410
RBKareem Hunt260.648
RBSamaje Perine270.648
WRArDarius Stewart280.6211
TEJake Butt290.555
WRTaywan Taylor300.4412
WRAmara Darboh310.4213
RBMarlon Mack320.4210
TEGerald Everett330.376
TEJordan Leggett340.376
WRMalachi Dupre350.3014
WRChad Hansen360.2615
RBBrian Hill370.1911
WRDede Westbrook380.1716
WRK.D. Cannon390.1517
QBBrad Kaaya400.145
RBJeremy McNichols410.1412
TEAdam Shaheen420.118
QBDavis Webb430.056
WRJosh Reynolds440.0318
RBJamaal Williams450.0213
RBJames Conner46-0.0114
QBNathan Peterman47-0.047
WRRyan Switzer48-0.3519
RBCorey Clement49-0.3615
RBDonnel Pumphrey50-0.3615
WRJehu Chesson51-0.3920
WRStacy Coley52-0.4121
WRNoah Brown53-0.4522
TEJeremy Sprinkle54-0.459
TEMichael Roberts55-0.459
WRArtavis Scott56-0.5123
WRTravis Rudolph57-0.5324
QBJoshua Dobbs58-0.608
QBJerod Evans59-0.608
TEJonnu Smith60-0.6011
RBMatthew Dayes61-0.6117
WRKenny Golladay62-0.6325
TEGeorge Kittle63-0.7112
RBT.J. Logan64-0.7618
RBElijah McGuire65-0.8319
TEEric Saubert66-0.9013
RBElijah Hood67-0.9120
WRFred Ross68-0.9226
WRMack Hollins69-0.9226
RBAaron Jones70-0.9321
TECole Hikutini71-0.9314
WRTravin Dural72-0.9628
WRJosh Malone73-0.9829
TERicky Seals-Jones74-1.0430
RBDe'Veon Smith75-1.1122
QBChad Kelly76-1.1510
RBJoe Williams77-1.1823
RBTarean Folston78-1.1823
QBC.J. Beathard79-1.1911
WRTrent Taylor80-1.2031
WRAmba Etta-Tawo81-1.2031
RBDare Ogunbowale82-1.3025
WRDamoreea Stringfellow83-1.3233
QBSeth Russell84-1.3312
TEBlake Jarwin85-1.3415
RBJahad Thomas86-1.3826
TEDarrell Daniels87-1.3816
WRGabe Marks88-1.4234
WRRodney Adams89-1.4835

Additional Research

QuaterbacksThe QB Prospect Model – RotoDoc uses a statistical feature selection process to build a QB projection model. It provides a likelihood of success. In testing, this process mis-classified QBs just seven percent of the time. RotoDoc provides success odds for the incoming rookies, as well as 2016 draftees, and the complete data set for all QBs going back to 2007.

Running BacksThe RB Success Model – Using age, production, and combine measurables, Cole builds on earlier regression tree analysis to build a model that predicts success within the first three years of a player’s NFL career. Odds of success are given for 28 RBs from the 2017 NFL draft class, along with commentary on the more prominent names.

Wide ReceiversThe Phenom Index – Jon Moore combines age and market share of receiving yards into a single number. Historical success rates are provided, and scores for the 2017 draft class can be compared to those from previous years. Jim Kloet provides context, graphing WR college market shares by age.

Tight EndsTight End Prospecting – Phil Watkins built a TE success model that is exceptionally good at identifying busts. It’s also useful for finding players that are likely to have NFL success. Watkins analyzes the top 14 TE prospects in this year’s draft and provides odds of success for each.



  1. Each player’s positional RSI score standardized to be on the same scale.  (back)

recent and related...

in case you missed it...

Best Ball Win Rates: 2019 Awards

Shawn Siegele uses the Best Ball Win Rates tool to hand out awards to the top performances in six categories.  Each week I’ve been using our Best Ball tools to evaluate a different fantasy position. We’ve already begun grading the Best Ball Workshop, a series of how-to strategy articles built on

Read More

16 Stats to Know for DFS in Week 17

Utilizing RotoViz’s suite of creative tools, metrics, and filterable stats (all of which just keep getting better!), I unearthed 16 key stats to help you crush your Week 17 NFL DFS lineups. Vegas lines for Week 17 Reported lines are current as of December 26, 2019.1 CLE @ CIN: The

Read More

Week 17 DraftKings Targets: The News Cycle Is Key

As is the case with Week 17 historically, teams locked into the playoffs with little or nothing to gain from a Week 17 win will often be cautious with their key players. This is already reported to be the case with the Bills this week. We could also see the

Read More

Sign-up today for our free Premium Email subscription!

© 2019 RotoViz. All rights Reserved.