Kenneth Dixon and Paul Perkins project for fantasy success

Kevin Cole applies his running back prospect model to the 2016 draft class to see who is most likely to have early fantasy success at the pro level.

| 1 year ago
(AP Photo/Brynn Anderson)

(AP Photo/Brynn Anderson)

Kenneth Dixon and Paul Perkins project for fantasy success

In the past couple weeks, we walked through the historical results and methodology for our wide receiver prospect model, and then applied it to the 2016 class. Now that we’ve built the equivalent for running backs entering the NFL, let’s take a look at the 2016 running back prospects under the microscope.

First, we need to revisit the methodology briefly to make sure we understand what’s driving the model. We trained and tested the model to find the most predictive variables for early NFL success, which I’ve defined as at least one top-12 (PPR scoring), or RB1 season in the first three years of a running back’s career. Simplicity is always a priority when developing a model, and our running back success model provides strong accuracy with only three independent variables:

1. Draft age

2. Final season rushing touchdowns

3. Final season receiving yards

Age was also shown to have significance for wide receivers, with younger prospects preferred. It seems a bit strange to not have rushing yards in the formula, but touchdowns have more predictive power. Perhaps this is due to the fact that touchdowns are more correlated with weight, which I plan to add to the model after the NFL combine. The value of receiving yards is certainly buoyed by our choice to uses PPR scoring for our measurement of success.

Here are the results for the 2016 class.

Name College Year Age Rush TDs Rec Yds Predict
Ezekiel Elliott Ohio State 2016 20.9 23 206 0.70
Derrick Henry Alabama 2016 22.0 28 91 0.54
Kenneth Dixon Louisiana Tech 2016 22.4 19 464 0.53
Alex Collins Arkansas 2016 21.8 20 95 0.31
Paul Perkins UCLA 2016 21.6 14 242 0.30
CJ Prosise Notre Dame 2016 22.1 11 308 0.19
Tyler Ervin San Jose State 2016 22.7 13 334 0.18
Deandre Washington Texas Tech 2016 23.4 14 385 0.15
Darius Jackson Eastern Michigan 2016 22.6 14 201 0.13
Peyton Barber Auburn 2016 22.0 13 112 0.13

Hat tip to Jon Moore for collecting the draft ages for the 2016 prospects. Prospect age is calculated as of mid-year 2016. The “Predict” column gives the model score (between 1 and 0) for each prospect indicating the likelihood of a top-12 PPR season in a prospect’s first three years.

The top of the board should lay to rest any concerns about the model’s simplicity: four of the top five prospects match between our model and NFL Draft Scout’s rankings.

Ezekiel Elliott looks just as dominating in the model as he does on the field. Elliott provides the perfect storm for running back success: young, lots of touchdowns, and proficiency in the passing game. Elliott’s 0.70 success score would have him tied for seventh place against the entire 2000-2013 database.

Derrick Henry is the 2016 leader in rushing touchdowns, but is a clear notch down from Elliott due to his older age, and more importantly the lack of passing game production. The fact that hefty Henry wasn’t heavily involved in the passing game in college doesn’t mean he won’t be in the pros, but it leaves that question uncertain at best.

Kenneth Dixon is the flip side of Henry: fewer rushing touchdowns but strong passing game chops. Dixon is currently seen as a second or third round pick, but his 0.53 nearly matches that of the much more highly rated Henry. Dixon is much smaller than many of the top prospects, so I doubt that even a strong combine will propel him into the first round of the NFL draft.

Alex Collins, like Henry, didn’t top 100 passing yards in his final season. But unlike Henry, Collins’ smaller build profiles more like that of a pass-catcher. Collins could end a value in dynasty rookie drafts if he shows pass-catching ability during combine drills.

Paul Perkins is a surprise top 5 success score. Perkins is seen as a third or fourth round pick by analysts, but our model is fond of his younger age and good receiving numbers. It will be interesting to see if the model and prospect experts’ opinions of Perkins converge as get closer to the draft.

Name College Year Age Rush TDs Rec Yds Predict
Devontae Booker Utah 2016 24.1 11 316 0.04

A name conspicuously absent from the model’s top 10 is Devontae Booker, who is currently viewed as a top 5 prospect. Booker has good receiving yards, but the model doesn’t look kindly on his older age and lack of rushing touchdowns. Utah’s 6-7, 233-pound quarterback Travis Wilson scored seven touchdown last year, holding down Booker’s total. Booker could certainly blow up the combine and look much better in the updated model, but I doubt his model projection will ever match his draft position.

Below is a sortable table of the 2016 running back class with the relevant variables and prediction scores.

Kevin Cole is a Lead Writer for PFF Fantasy. You can follow him on Twitter at @Cole_Kev

NameCollegeYearAgeRush TDsRec YdsPredict
Ezekiel ElliottOhio State201620.9232060.70
Derrick HenryAlabama201622.028910.54
Kenneth DixonLouisiana Tech201622.4194640.53
Alex CollinsArkansas201621.820950.31
Paul PerkinsUCLA201621.6142420.30
CJ ProsiseNotre Dame201622.1113080.19
Tyler ErvinSan Jose State201622.7133340.18
Deandre WashingtonTexas Tech201623.4143850.15
Darius JacksonEastern Michigan201622.6142010.13
Peyton BarberAuburn201622.0131120.13
Jordan HowardIndiana201621.791060.10
Brandon BurksTroy201622.773040.08
Dwayne WashingtonWashington201622.243150.08
Kelvin TaylorFlorida201622.8131500.08
Elijhaa PennyIdaho201622.9102390.07
Wendell SmallwoodWest Virginia201622.491600.07
Jordan CanzeriIowa201623.4122080.06
Robert LoweTexas State201622.271450.06
Travis GreeneBowling Green201624.1152370.06
Jordan ParkerMiddle Tennessee201621.96680.05
Devontae BookerUtah201624.1113160.04
Jaylen WaltonOle Miss201622.451640.04
Kenneth FarrowHouston201623.3121190.04
Kenyan DrakeAlabama201622.412760.04
Michael GordonArkansas State201622.79810.04
Shaquille PowellDuke201622.532120.04
Aaron GreenTCU201623.7111170.03
Chris HairstonEast Carolina201623.781930.03
Christian PowellColorado201622.34640.03
Don JacksonNevada201622.88770.03
Josh FergusonIllinois201623.132800.03
Marteze WallerFresno State201622.951510.03
Tre MaddenSouthern California201622.951330.03
Brandon WildsSouth Carolina201622.931420.02
Devon JohnsonMarshall201622.95630.02
Keith MarshallGeorgia201622.43280.02
Paul JamesRutgers201623.05400.02
Rodrick Williams JrMinnesota201622.31580.02
Tra CarsonTexas A&M201623.771830.02
Anthon SamuelFlorida International201623.31160.01
Daniel LascoCalifornia201623.73240.01
Jc ColemanVirginia Tech201622.51210.01
Jhurell PressleyNew Mexico201624.11120.01
Johnathan GrayTexas201623.23840.01
Jonathan WilliamsArkansas201622.4000.01
Justice HayesSouthern Mississippi201623.62490.01
Leon AllenWestern Kentucky201623.32720.01
Shaun WickWyoming201623.01490.01
Zac BrooksClemson201623.431050.01

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