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Predicting Post-NFL Draft Rookie RB Success (2019 Fantasy Football)

Predicting Post-NFL Draft Rookie RB Success (2019 Fantasy Football)

What makes a successful fantasy RB? More importantly, how do we know after the draft which rookie RBs will perform the best, especially for dynasty leagues? There are hundreds of predictions from all kinds of fantasy websites. Most of them will agree that it boils down to two things: opportunity and talent. How we measure that is uniquely different. Now while most fantasy analysts claim to have their fix on who will be great, they often fall short.

This model below has accuracy second to none and has one thing others do not — accountability. If that’s hard to believe, let’s first compare it against some of the popular fantasy projection sites out there of years past:

2015 | 2016 | 2017 | 2018

Their models include Melvin Gordon and David Johnson as the fourth- and eighth-best RBs of their class, Kenyan Drake as 10th, Christian McCaffrey and Aaron Jones as third and 17th, and Ronald Jones as third, all respectively. These are just to name a few that are far from accurate. Compare them to the z-score predictions for each of their respective years in the charts found at the end of this article. Which is more accurate?

Here is an overall plot of what I call a players’ “Zach score” percentage, z-score for short, in correlation to their average fantasy production per year in a PPR league for their first three years in the NFL. This list includes the majority of RBs drafted in Rounds 1-4 since 2015, along with a few later selections to test the model (minus two outliers).

This is absolutely wonderful. The line of best fit through the data shows a direct and highly positive correlation. This means, in general, the higher a player’s z-score %, the greater his fantasy production.

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So where do these numbers come from? Fantasy forecasters like to use certain metrics to predict success. These range from BackCAST, 40 times, team philosophies, college stat combinations, etc. This model takes a historical approach; it proves itself by using concrete measurables that have translated to success in the past.

The factors that are being taken into account have either been proven by third-party independent research or are self-proven inside the model. These factors aim to measure the opportunity and talent as referenced before, and include the following:

  1. Draft position
  2. Speed score
  3. Harvard Combine Metric (HCM)
  4. Agility threshold
  5. Prior year’s offensive production of the NFL team they are drafted by
  6. If they are a top-15 selection
  7. Collegiate production
  8. Disqualifying thresholds

Going a little further into this, how does each factor translate to success? Draft position relates to both opportunity and talent, better players typically go higher and the higher a draft pick, the more investment from the NFL club selecting them, leading to more opportunities (ref. 1). Speed score takes into account a 40-time relative to weight and is also indicative of success (ref. 2). The Harvard Combine Metric tries to predict both when someone will be drafted followed by their three-year average of success (ref. 3). Agility threshold is similar to an elusiveness rating (three-cone drill is part of this) and translates to a better receiving back more often than not.

Being drafted into a high powered offense also benefits RBs. If you’re on a team that scores more points and gains more yards than average, you are already setup for greater early success simply by being on that team. A top-15 pick goes without saying, as everyone thinks you are great and you likely are great. Higher collegiate production translates to higher NFL production, although not as strongly as most would think. Finally, there are “disqualifying” thresholds, which consist of five categories that prove as red flags and hurt your future value (e.g. a 40 time of 4.68). Combine all these together with varying weights and conditions, and you have your fantasy z-score.

As with all statistics, there were a few outliers, of which two were removed in the overall graph for clarity. They are Kareem Hunt (1% z-score, 263 FP/yr) and Rashaad Penny (97% z-score, 70 FP/yr). What happened here? In short, opportunity trumps all.

Would have Hunt had the success he had without Spencer Ware falling to injury just before the season started? Or how about Andy Reid’s tendency to use a bell-cow receiving back? My model sees him being a bust down the road. Then there’s Penny. Both being hurt before the season started and Carroll’s desire to use RB by committee, limited his opportunities, even though he led the team in YPC. There is still time left in their three-year averages to better follow the model and they are likely to do so.

2019 Dynasty RB Predictions

Looking at the rookie RB class of 2019, what stands out most is that there are no scores above 60%, which sadly breaks trend from each the past five years from a fantasy perspective. This means it is severely lacking in the top-end talent we usually see, although it has decent mid-round depth. In addition, the top-four rated backs are unusually all within four percent of each other, meaning that you should take this top-four ranking order with a grain of salt. The model tells us there might not be much of a difference as these top four are very likely to hit between 100-200 fantasy points with their three-year averages. This is all indicative of the relatively weaker RB class this year.

Below you can find where each RB ranks with regard to their z-score and respective fantasy-point projections. Keep in mind these projections are based on a historical record of their average fantasy production over a three-year period, not solely their rookie reason. Please reference the overall chart above or yearly charts at the end of the article to find players from the past that have scored similarly.

1. Damien Harris (NE): 57.4% z-score, 147 FP/ year
This is a peculiar landing spot for Harris, as the Patriots drafted Sony Michel in the first round just a year ago. They love to use their RBs regardless of draft position (see LeGarrette Blount, James White, Dion Lewis, Rex Burkhead — all selected in the fourth round or later) and love to cycle through them equally as much in trades, free agency, or the draft. Even so, it’s hard envisioning Harris cutting out a role early unless they change up their backfield roster yet again. Registering a better speed score, Harvard Combine Metric, collegiate YPA, and a better offensive unit than fellow ex-teammate Jacobs, he was still able to score higher despite the much weaker draft position.

2. Josh Jacobs (OAK): 56.3% z-score, 145 FP/year
Being the top back off the board should greatly open up his opportunities, although his stats don’t particularly stand out as he scores around or below average in nearly every z-score metric. Only one player with a z-score above 55% has averaged less than 120 FP/yr (Rashaad Penny – time will tell), giving Jacobs a decent fantasy floor with upside potential. However, he has the lowest z-score of any first-round pick in over five years by a longshot (next closest is Sony Michel at 86%)! This tells us he was likely overdrafted, but since he has such strong club investment from the Raiders, he could very well score higher than this rating indicates, especially early in his career. His limited competition will be Doug Martin, who they signed to replace Isaiah Crowell (torn Achilles), along with pass-catching specialist Jalen Richard, who could possibly eat into his workload.

3. Miles Sanders (PHI): 54.7% z-score, 142 FP/year
Boasting the best three-cone time of the rookie RB crop and ranking above average in nearly every metric, Sanders is a very nice all-around prospect. He should compete with recently acquired Jordan Howard, among others, for carries in the Eagles’ backfield.

4. Darrell Henderson (LAR): 53.8% z-score, 141 FP/year
His collegiate YPC (8.2) is phenomenal. If you are a firm believer in college production translating to the NFL, Henderson is your man. Perhaps he will outperform his z-score rating here based on that alone. Going to a fantastic offensive team in the Rams and a very mysterious health situation surrounding Gurley, he could be sitting on the verge of fantasy glory.

*Note* Scoring below a 50% z-score has only produced two 200+ FP/year players over the last five years, a success rate of less than seven percent for that threshold. Keep this fantasy ceiling probability in mind for the remaining players.

5. Justice Hill (BAL): 48.7% z-score, 132 FP/year
Hill had the fastest 40-time among RBs and a respectable Harvard Combine Metric of 210.5 which typically translates well. His somewhat smaller size is a concern, but hopefully he can carve out at least a receiving back role. Similar statistical past prospects include Cohen, Tyler Ervin, Nyheim Hines, and Ito Smith, but Hill outdoes all of the aforementioned with his higher z-score. Could this possibly be a precursor to his greater comparable success? Having Mark Ingram and Gus Edwards may indeed limit his window to receiving downs with Javorius Allen.

6. *Bryce Love (WAS): 42.3% z-score, 123 FP/year
Buyer beware as he is coming off a recent ACL tear, but his recovery team points to him being healthy either before or during the 2019 season as of this article’s publishing. Even with no official 40-time, his speed sets him apart. This combined with his college production stand out the most. He possesses the skills to, at a minimum, be in an RBBC or a third-down option with potential for even more usage, if he stays healthy. The Redskins now have two high-potential RBs coming off of ACL tears in Love and Derrius Guice.

*Did not participate at combine or pro day, affecting his z-score

7. Tony Pollard (DAL): 39.2% z-score, 118 FP/year
He’s an intriguing prospect with a hard ranking to live up to, as he is not likely to see this point total with feature workhorse Ezekiel Elliott commanding the majority of snaps. If they decide to lighten the load on Zeke, this could become more realistic as he is sure to serve as his primary backup. He has some nice credentials as a quality sleeper and his metrics point to more work as a third-down back with his receiving and returning background.

8. Ryquell Armstead (JAC): 23% z-score, 94 FP/year
The speed score winner of the combine (112.2) has some nice potential upside because of it. The only players with speed scores above 110 over the model’s range are Elliott, Derrick Henry, Leonard Fournette, Joe Mixon, D’Onta Foreman, Saquon Barkley, Penny, Guice, and Kalen Ballage. That is not bad company. His college production hurts, as does his draft stock, however, giving him the lowest z-score of his fellow speed score company. He could be a decent sleeper worth a late flyer if Fournette’s lack of love from the Jags’ brass continues.

9. Alexander Mattison (MIN): -0.9% z-score, 52 FP/year
The heir apparent to Dalvin Cook’s injury-riddled throne will need yet another Cook injury to see any significant playing time. He is quite slow (4.67 40-time and 7.13 3-cone), so even if Cook does go down, this backfield is looking to shape into a committee.

10. David Montgomery (CHI): -33% z-score, 23 FP/year
If this model had a kryptonite, it would be projecting Bears RBs. It has significantly under forecasted the last two meaningful RBs they selected in Howard (26%, 203 FP/year) and Cohen (10%, 189 FP/year). Yet again, the model predicts Montgomery as an even harder bust candidate at -33%. His lack of decent combine metrics paired with his mediocre YPA in college both spell bad news for his future. However, his only competition for early-down work is seemingly Mike Davis, with Cohen handling receiving downs. He could see decent playing time as a result of this.

11. Devin Singletary (BUF): -40% z-score, ~0 FP/year
As the z-score loser of the whole group, there are red flags everywhere with Singletary, ranging from his atrocious speed score, to the worst Harvard Combine Metric seen in five years, to his smaller stature…yikes. He would completely shatter statistical history if he performed well mid-to-long term in the NFL.

The remaining prospects were taken in the fourth round or later and/or scored under a 10% z-score. They are being left off this list for the apparent lack of fantasy relevance.

Previous Years’ Z-Scores

To complete the remaining data, here are the z-score charts for each year during 2015-2018 for the majority of the RB class taken through Rounds 1-4 and their fantasy pts/year average over their first three years in the NFL.



2017 (Hunt’s and Conner’s successes came from injuries or holdouts from players above the depth chart to them; data has one year left in the model):

2018 (data has two years left in the model):

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David Zach is a featured writer at FantasyPros. For more from David, check out his archive and follow him @DavidZach16.

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