2024 RB Rookie Super Model: Tier 1 Running Back Rookie Rankings

With the NFL Draft quickly approaching, we are releasing our updated Fantasy Life Rookie Super Model and rookie rankings to help answer that question! We have already released WRs and TEs and are now moving on to the RB position.

Our goal is to identify the top rookie prospects based on data points that correlate most with future NFL production. I have been working on NFL rookie models for the last three years, and over that time, I have studied and measured hundreds of predraft variables against future NFL production.

This is a great time to clarify that I am not a mathematician or a coder. Yes, I have a background in data and analytics, but I am self-taught. I didn’t take a course or go to a university to study these topics. I simply love data, understanding why things work the way they do, and football.

OK, back to the point I was about to make before my disclaimer.

The truth is that most variables don’t carry a strong signal, or they overlap too much with an existing variable to make it into a model. Even once you define a list of relatively strong inputs, it is hard to accurately predict which college athletes will be the best NFL players.

Football is a sport with countless dependencies played by notoriously unpredictable creatures known as human beings. When you add in plain old variance, you can see how this activity can become challenging.

But that is what makes it so interesting, and it fuels me to test new ideas every offseason. At the RB position, our model has performed very well.

Correlations to future fantasy performance:

Raw NFL Draft Pick: 0.58
NFL Draft Capital Value: 0.68
Super Model without Draft Capital: 0.68
Super Model with Draft Capital: 0.72

So, let’s examine the inputs used for the 2024 RB Super Model and then see how the 2024 NFL Draft prospects fared. You can find the other Rookie Running Back tiers here:

RB Super Model Inputs & Methodology Overview

The inputs below are in order of their correlation to fantasy production in an RB’s first two years in the NFL.

Projected draft capital (NFL Mock Draft Database)
Collegiate program quality
Adjusted career yards per team attempt (rushing and receiving)
Career composite PFF grades (rushing and receiving)
NFL.com prospect grades
Speed Score
Career TDs per game
Age

Because the model includes advanced data that wasn’t widely available before the 2017 class, our sample focuses on prospects with at least two years of play since then. So, our correlations to future performance currently derive from RB data from 2017 to 2022.

For all production stats, the data comes from the game log level rather than the season.

Super Model Note: the only RBs included from the 2017 class left for the NFL after three years because we don’t have data for the 2013 season to cover four-year starters from the class.

Draft Capital Value

The model uses Chase Stuart’s Draft Value Chart for draft capital, essentially a better version of what many know as the Jimmy Johnson trade chart. The value of a draft pick isn’t linear, and this methodology helps us capture that. The dropoff in value is steeper in the first round and becomes much flatter around the end of the second round. Draft capital value is the most weighted input in the Super Model.

Super Model Note: Because draft capital isn’t linear, the most significant deterioration occurs in the first two rounds. As a byproduct of that, the scores in the model drop off quickly and then begin to flatten toward the end of Round 2. That means the 45th percentile isn’t a bad score – it ranks 20th out of 215 prospects in the database. In fact, 177 out of 215 prospects are below the 35th percentile.

Program Quality Index

Program quality is a pedigree metric that uses draft capital value to determine the total value each collegiate program has contributed to the NFL Draft since 2015. The model uses a composite score derived from two inputs.

Program draft capital at the RB position
Program draft capital at the RB, WR and TE positions

Those scores are then indexed to form the Program Quality Index.

Prospects who come from more robust programs score better. Program quality has been a factor in the model before, but this is a better way of quantifying it. Additionally, this metric helps offset lower production numbers from prospects with more competition.

The weighting for pedigree is intentionally lower than the correlation to future production suggests because program quality creates a double-counting effect for draft capital. While we want prospects from schools that churn out high draft picks, that particular player’s draft capital is included in program quality when we look back at the model. This is also why we use a program quality score focused on RB, WR and TE in the RB model.

I want to shout out to Billy Elder, who spawned this idea.

Adjusted Career Yards Per Team Attempt Index

Adjusted career YPTA is a production metric that allows us to normalize yards based on the team environment, which is essential because team volume varies from one situation to the next. A prospect averaging 75 yards per game in a low-volume offense might be better than another averaging 100 yards on a high-volume squad per-team-attempt basis.

Receiving yards are worth twice as much as rushing yards in this equation. This gives us a better approximation of value versus half and full-PPR formats.

Equation: (rushing yards + receiving yards*2) / (team rushing attempts + passing attempts)

Career Composite PFF Grade Index

This qualitative metric is based on a player’s career PFF Rushing Grade and PFF Receiving Grade. If you wonder how PFF Grades work, I recommend reading Steve Palazzolo’s breakdown. But below are two excerpts that can get you by if you just want the basics.

“Credit is given for each move the running back makes to add value to the play, whether forcing a missed tackle, using speed to gain the edge or creating yards through contact.”

“Our goal is to isolate the running back’s contribution to that production, and the runners with the highest grades are those who produce above expectation and outside what the run blocking or scheme allows.”

PFF Grades account for context we otherwise can’t capture at such a massive scale. Because of this, it isn’t surprising that grades correlate more strongly to future production than individual statistics, such as missed tackles forced, yards after contact, and explosive plays. Plus, it allows us to concisely present that information in one data point.

Super Model Note: We are calculating the career grades based on season grades weighted by rushing attempts and passing targets.

NFL.com Prospect Grade Index

This is another qualitative metric based on Lance Zierlien’s prospect grades on NFL.com. His prospect scores have a 0.59 correlation to Year 1 and Year 2 RB fantasy production since 2017, which was strong enough to add a film element to the Super Model.

The grades are indexed on a scale of 0 to 1.

Speed Score

I tested all NFL Combine and pro day data, including RAS (relative athletic scores) for all positions. While most athletic tests show some signal, they aren’t strong enough to make it into the model. However, for RB, Speed Score garnered a 0.31 correlation to future production and offered relatively low overlap with the other data points in the model.

Speed Score combines a player’s weight with 40-yard dash time (weight*200)/(40-time^4), offering a significantly stronger signal over 40 times alone. Bill Barnwell of ESPN created Speed Score.

Career Total TDs Per Game

This is another production metric. The data showed that using a normalized metric like YPTA was superior for receiving yards, but that wasn’t true for TDs. Instead, per-game data demonstrated a stronger correlation than share, per-team attempt, and other options.

There is a correlation between yards and TDs, so once again, there is some overlap in signal between our metrics—however, not all RBs who are strong in YPTA score a lot of TDs.

Age Index

A player’s age derives from how old they will be at the beginning of the upcoming NFL season. Historically, younger players and early-declares carry a stronger signal than older prospects.

The 2024 NFL Draft is underway! Read about all of the 2024 NFL Draft winners and losers for fantasy football as the draft unfolds!

Tier 1 – Every-Down Traits With Top-12 Upside

Jonathon Brooks | Texas

RB Super Model: 45th percentile
Age: 21.1
Height: 6-foot
Weight: 216
Jonathon Brooks Scouting Report via Jonathan Fuller

Prospect Summary

Brooks is the most complete back in the draft, which gives him a ton of outs. He is coming off a late-season ACL injury, so patience might be required from fantasy managers in Year 1. Still, historically, this type of profile has a great hit rate. He is the hands-down RB1 of the 2024 class for dynasty and rookie drafts.

Pedigree

Program Quality Index: 58th percentile
NFL Mock Drafts: Pick 47, Round 2
247 Recruit Player Rating: 4 of 5 stars (354 overall, RB24)
Speed Score: N/A

While the Longhorns haven’t been an overall skill position factory, they have produced three NFL-Draft-worthy RB prospects since 2017. Bijan Robinson is the obvious headliner, but he was joined by Roschon Johnson (Round 4) last year, and D’Onta Foreman was a Round 3 pick in 2017. Of course, Texas could add up to three more Day 1 or Day 2 picks in Xavier Worthy, Adonai Mitchell and Ja’Tavion Sanders in the 2024 NFL Draft. Those don’t count for the Program Quality Index, but things are looking good in Austin.

Despite tearing his ACL last November, Brooks remains the top RB in the majority of mock drafts. Some of that is due to the lack of high-end prospects this year, but don’t let that lull you to sleep on Brooks — he grades out in a range of the RB Super Model where we have seen far more RBs provide fantasy value than disappoint.

Super Model Note: Because draft capital isn’t linear, the most significant deterioration occurs in the first two rounds. As a byproduct of that, the scores in the model drop off quickly and then begin to flatten toward the end of Round 2. That means the 45th percentile isn’t a bad score — it ranks 20th out of 215 prospects in the database.

Production and Film

Adjusted Career YPTA Index: 50th percentile
Career Total TDs Per Game Index: 48th percentile
Career Composite PFF Grade Index: 80th percentile
NFL.com Prospect Grade: 54th percentile

Brooks played sparingly during his first two years in Austin, thanks to Robinson's presence. However, he delivered a 2.22 YPTA as a redshirt sophomore — the second-best age-20 mark of any Power 5 back in the class. Brooks accounted for 56% of the Longhorns’ designed rushing yards (1,135) and punched in 10 scores in 11 games in 2023.

Brooks wasn’t a high-end target earner in the passing game, but his 1.50 career YPRR ranked as the class's No. 3 Power 5 RB. Like most backs, he was a behind-the-line-of-scrimmage (BLOS) option with a -1.4 aDOT. However, Brooks was an explosive playmaker, averaging 4.7 yards over expected after the catch (YAC), the best mark for any Power 5 RB in the Super Model database.

In the Rookie Scouting Portfolio, Matt Waldman noted Brooks is the best pass protector in the class — which could help him find his way into an every-down role sooner rather than later once the ACL heals. Waldman also believes Brooks has room to grow as a receiver.

“Brooks’s route game won’t remind you of Austin Ekeler at this stage of his career, but he’s an effective receiver from the backfield who should become a reliable component of screens, swing passes and wide routes who could deliver more if called upon. Brooks catches the ball effortlessly and has strong hand-eye coordination.”

While Brooks’ career production profile was hampered by playing time, he delivered when he got his opportunity. He registered an 84th percentile PFF Run Grade and a 71st percentile receiving grade for his career.

His Career Composite PFF Grade Index, which combines rushing and passing grades, falls in the 80th percentile — the 10th-best mark in the Super Model's history. Across 183 prospects analyzed since the 2017 NFL Draft, the composite score correlated more strongly to future fantasy success than Adjusted Career YPTA — one of our industry’s favorite production metrics for RBs.

Texas Longhorns running back Jonathon Brooks (24) runs for the first down against TCU Horned Frogs in the first quarter of an NCAA college football game, Saturday, November. 11, 2023, at Amon G. Carter Stadium in Fort Worth, Texas.

If you wonder how PFF Run Grades work, I recommend reading Steve Palazzolo’s breakdown. But below are two excerpts that can get you by if you just want the basics.

“Credit is given for each move the running back makes to add value to the play, whether forcing a missed tackle, using speed to gain the edge or creating yards through contact.”

PFF Grades account for context we otherwise can’t capture at such a massive scale. Because of this, it isn’t surprising that grades correlate more strongly to future production than individual statistics, such as missed tackles forced, yards after contact and explosive plays. Plus, it allows us to concisely present that information in one data point.

Of course, others are also high on Brooks’ ability to add value. Lance Zierlein of NFL.com compared Brooks to another former Texas back — Jamaal Charles, calling out Brooks’ “NFL-caliber accelerator” and ability to stack cuts and moves as strengths. Brooks received a 6.38 prospect grade as the top option in the 2024 class.

Zierlein recently commented that he was at his best when grading RBs, and his track record speaks for itself. Since 2017, his RB prospect grades have a 0.597 correlation to Year 1 and 2 fantasy production, making them a valuable input into the RB Super Model.

Jonathon Brooks Fantasy Outlook: Hit Rates

Underdog ADP: RB37, Round 10
Rookie Dynasty ADP: RB2, Pick 13

Whether you look at Brooks’ hit rates with or without draft capital (he is the No. 20 prospect in the database), he offers the type of profile we should invest in as fantasy managers. Nothing is ever a lock, but Brooks has an excellent chance to deliver top-24 results by year two, and a visit to the top 12 is within reach. His outlook requires patience, especially in Year 1, thanks to a November ACL injury, but he could still provide fireworks by the time the fantasy playoffs arrive.

Travis Etienne, Javonte Williams and Samaje Perine are the closest comps to Brooks in the Super Model. To unlock a top-six ceiling, Brooks must grow as a receiver or land on an elite offense with little competition for early-down work.