News & Analysis

C.J. Prosise set for early fantasy success

Earlier this week, I wrote about the methodology and historical results for the updated post-NFL combine running back prospect model. I didn’t update the wide receiver model with combine measurables, because I found – like others before me – that the combine doesn’t really matter for projecting NFL success for wide receivers. The most predictive wide receiver prospect variables are still age and on-field production. On the other hand, I found that the combine is critically important for running backs.

We used age, production, and combine measurables to train and test the updated running back model. After plugging more than a dozen different production and combine statistics into the model and slowly taking away – one-by-one – the least statistically significant, we were left with four (two combine, two production) that provide the most explanatory and predictive power (listed in order of statistical significance):

1. 40-yard dash

2. Weight

3. Final season rushing yards per game

4. Final season receiving yards per game

As you’d expect, the model favors faster, heavier prospects who had strong rushing and receiving production in their final college season. The 40-yard dash is by far the most influential statistic for predicting NFL success, followed by weight. Presumably skewed by the fact we’re using PPR scoring to measure success, receiving yards per game had a model coefficient nearly three times that of rushing yards per game. In order words, each additional receiving yard raises the prediction score by three times that of each additional rushing yard.

Here are the top-10 post-combine prediction scores for the 2016 running back class:

Name College Draft Year Weight Forty Rush Yds/Gm Rec Yds/Gm Predict
Derrick Henry Alabama 2016 247 4.54 147.9 6.1 0.44
Ezekiel Elliott Ohio State 2016 225 4.47 140.1 15.8 0.39
C.J. Prosise Notre Dame 2016 220 4.48 102.9 30.8 0.32
Tyler Ervin San Jose State 2016 192 4.41 123.2 25.7 0.18
Keith Marshall Georgia 2016 219 4.31 31.8 2.5 0.17
Kenneth Dixon Louisiana Tech 2016 215 4.58 97.5 42.2 0.17
Deandre Washington Texas Tech 2016 204 4.49 114.8 29.6 0.16
Wendell Smallwood West Virginia 2016 208 4.47 116.8 12.3 0.12
Josh Ferguson Illinois 2016 198 4.48 78.7 31.1 0.08
Paul Perkins UCLA 2016 208 4.54 103.3 18.6 0.07

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 model cannot generate prediction scores for running backs that did not participate in the 40-yard dash at the combine.

A couple of things to keep in mind to contextualize the prediction scores. The fact that no running back has a score above 0.50 isn’t an indictment of this class. The bar of a top-12 PPR season in the first three years is fairly high. The 10th-highest prediction score for the entire 2000-2013 data set was only 0.47 . So, scores like Derrick Henry’s 0.44 are very solid. In fact, the best score from the vaunted 2015 running back class belonged to Jay Ajayi at only 0.31 (Todd Gurley did not participate in the combine).

The model loves Henry’s extraordinary size/speed combination. Henry also had strong rushing production, but couldn’t achieve a truly elite score with such low receiving production. If Henry has pass-catching ability that simply wasn’t utilized at Alabama, his potential for NFL success is as great as any running back we’ve seen in the last 15 years.

Trailing Henry narrowly is the consensus top prospect of the 2016 class, Ezekiel Elliott. The stud national champion did nothing to hurt his draft stock at the combine, running a blazing 4.47 40-yard dash at 225 pounds. It’s been reported that there’s a real possibility that Elliott goes in the first four picks of the NFL draft, which would drastically boost his projection in any model accounting for draft position.

C.J. Prosise is the top projected back who could also be a substantial value in dynasty rookie drafts. NFL Draft Scout has Prosise as its 10th-highest-ranked running back prospect, projecting him to be taken in the third-to-fourth round. Prosise is athletically similar to Elliott, has good rushing production, and also brings to the table stronger receiving production than the consensus top-two backs.

Like Prosise, Tyler Ervin isn’t a top consensus prospect. In fact, he’s even lower down NFL Draft Scout’s running back rankings at 14th, projected for the fifth round. Ervin’s 4.41 forty was the second best at the combine, and he has strong rushing and receiving production, but his relatively small size (192 pounds) lowers his prediction score to 0.18, materially lower than that of the model's top-three backs. While Ervin might not match Henry, Elliott or Prosise in likelihood of NFL success, his upside should still vastly outweigh minimal cost in dynasty rookie drafts.

The fact that Keith Marshall has a top-five prediction score shows you the power of speed in the model. Marshall had no meaningful production in his last three years at Georgia, but his 4.31 forty (the third fastest time in the last 10 years) at 219 pounds is mind-blowing. I’m skeptical of “workout warriors” generally, but Marshall has some legitimate excuses for his lack of on-field production. He had a promising start to his collegiate career, racking up 759 rushing yards and eight touchdowns as a freshman competing for touches with Todd Gurley. Then, Marshall tore his ACL his sophomore year, and only played three games the following year. It’s not surprising that the speedster got buried on the depth chart after that. Marshall surely isn’t a lock for NFL success, but poor on-field production could understate his true talent.

One prospect that didn’t make the top 10 but deserves mentioning is Daniel Lasco. Lasco had one of the more impressive combine performances, posting a 4.46 forty, 41.5 vertical (third-best ever for a running back), and a record-breaking 135-inch broad. Lasco’s final year on-field production was poor due to injury, but he had a solid junior year, averaging 92.9 rushing yards and 29.7 receiving yards per game. If you plug those numbers into the model with Lasco’s weight (209 pounds) and forty, his prediction score is 0.20, or fourth highest of all 2016 running backs. Despite his strong combine, Lasco hasn’t moved up draft boards much yet, as he's still projected to go in the fourth or fifth round.

You can peruse this sortable table of the 2016 running back prospects with the relevant variables and prediction scores:

NameCollegeDraft YearWeightFortyRush Yds/GmRec Yds/GmPredict
Derrick HenryAlabama20162474.54147.96.10.44
Ezekiel ElliottOhio State20162254.47140.115.80.39
C.J. ProsiseNotre Dame20162204.48102.930.80.32
Tyler ErvinSan Jose State20161924.41123.225.70.18
Keith MarshallGeorgia20162194.3131.82.50.17
Kenneth DixonLouisiana Tech20162154.5897.542.20.17
Deandre WashingtonTexas Tech20162044.49114.829.60.16
Wendell SmallwoodWest Virginia20162084.47116.812.30.12
Josh FergusonIllinois20161984.4878.731.10.08
Paul PerkinsUCLA20162084.54103.318.60.07
Brandon WildsSouth Carolina20162204.546315.80.06
Dan VitaleNorthwestern20162394.6029.60.06
Kenyan DrakeAlabama20162104.4531.421.20.06
Alex CollinsArkansas20162174.59121.37.30.05
Peyton BarberAuburn20162284.6478.28.60.03
Daniel LascoCalifornia20162094.4636.82.70.02
Glenn GronkowskiKansas State20162394.7158.40.01
Kelvin TaylorFlorida20162074.673.910.70.01
Soma VainukuSouthern Cal20162464.680.400.01
Andy JanovichNebraska20162384.8124.15.30
Devon JohnsonMarshall2016238NA84.79NA
Devontae BookerUtah2016219NA126.131.6NA
Jonathan WilliamsArkansasNA220NANANANA
Jordan HowardIndiana2016230NA134.811.8NA
Marshaun CoprichIllinois StateNA2074.47NANANA
Quayvon HicksGeorgia2016259NA1.86.8NA
Shad ThorntonNorth Carolina St.NA2174.75NANANA
Tra CarsonTexas A&M2016227NA89.614.1NA
Tre MaddenSouthern Cal2016223NA50.214.8NA

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

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