With the NFL Draft quickly approaching, we are releasing our updated Fantasy Life Rookie Super Model. 

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 made before the 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.

So, without further ado. Let’s dive into the inputs used for the 2024 WR Super Model, and then dive into Tier 3 of the 2024 Rookie WR Super Model.

For the rest of the WR Super Model tiers, see below:

• Tiers 1-2
  • Rome Odunze | Washington
  • Malik Nabers | LSU
  • Marvin Harrison Jr. | Ohio State
• Tier 3
  • Brian Thomas Jr. | LSU
  • Xavier Worthy | Texas
  • Troy Franklin | Oregon
• Tier 4
  • Ladd McConkey | Georgia
  • Adonai Mitchell | Texas
  • Keon Coleman | Florida State
  • Jermaine Burton | Alabama
  • Roman Wilson | Michigan
• Tier 5
  • Ricky Pearsall | Florida
  • Ja'Lynn Polk | Washington
  • Malachi Corley | Western Kentucky
  • Jacob Cowing | Arizona
  • Devontez Walker | North Carolina
  • Jalen McMillan | Washington
  • Xavier Legette | South Carolina

WR Super Model Overview

The inputs are in order of their correlation to fantasy production in a WR’s first two years in the NFL.

• Projected draft capital (NFL Mock Draft Database)
• Collegiate program quality
• Adjusted career receiving yards per team pass attempt
• Career targeted QB rating
• Career TDs per game
• Age

Because the model includes advanced data that isn’t widely available before the 2018 class, our sample focuses on WRs with at least two years of play since then. So, our correlations to future performance currently derive from WR data from 2018 to 2022.

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

If you want all the details and reasoning behind the inputs and methodology, they are outlined in the WR Super Model Inputs & Methodology at the bottom of this page.

Tier 3 – Foundational Traits With WR2-Plus Upside

Brian Thomas Jr. | LSU

• WR Super Model: 70th percentile
• Age: 21.9
• Height: 6-foot-3
• Weight: 209

Pedigree

• Program Quality Index: 80th percentile
• NFL Mock Drafts: Pick 17, Round 1
• 247 Recruit Player Rating: 4 of 5 stars

No other WR expected to go in Round 1 of the NFL Draft climbed further. Thomas was considered a Round 2 or Round 3 prospect before the start of the 2023 season. However, only Troy Franklin (3) had a higher rank in the 2021 recruiting class. Thomas ranked as the No. 9 WR in the nation — ahead of Nabers (19) and Harrison Jr. (21).

Production

• Adjusted Career RYPTPA Index: 44th percentile
• Career Total TDs Per Game Index: 59th percentile
• Career Targeted QB Rating Index: 81st percentile

Thomas didn’t do much in his first two seasons at LSU but came to life in his final season as a junior with a 2.86 RYPTPA. While that wasn’t enough to push his Adjusted Career numbers into elite territory, it was enough to get his number to a respectable territory where his stronger marks in career TDs and targeted QB rating could help balance his production score. 

However, since 2018, WRs with junior-season breakouts after not doing much in their first two seasons don’t have the best track record. The sample size isn’t large, and some of these players have only been around two seasons, so we don’t want to overreact, but the list of players that went in the first three rounds of the NFL Draft is worth noting:

• Jameson Williams
• Jahan Dotson
• Terrace Marshall Jr.
• J.J. Arcega-Whiteside
• DK Metcalf

Of these players, only Metcalf has paid off big for fantasy managers. Marshall and Whiteside were busts, and Williams and Dotson still have a chance but aren’t trending positively after two seasons.

Oct 21, 2023; Baton Rouge, Louisiana, USA; LSU Tigers wide receiver Brian Thomas Jr. (11) celebrates after scoring a touchdown against Army Black Knights defensive back Cameron Jones (10) during the first quarter at Tiger Stadium. Mandatory Credit: Matthew Hinton-USA TODAY Sports

Like Metcalf and Marshall, Thomas played with some great teammates and struggled to consistently earn targets, with an 18% TPRR. Even when Thomas got man coverage looks, his TPRR remained at 18%, which isn’t something we typically see from alpha producers at the next level.

Still, Thomas was highly effective when targeted. According to PFF data, LSU passers enjoyed a 132.8 passer rating when targeting Thomas. That is the second-best mark in the class behind Franklin.

Some think of Thomas as more of a deep threat thanks to his 4.33 40-yard dash at the NFL Combine, but his deep target rate was in line with the NCAA average of 20%. The soon-to-be 22-year-old saw 50% of his targets come between 0 and 9 yards from the line of scrimmage while operating on the outside on 88% of snaps. Thomas could be a more versatile weapon than we believe — Matt Waldman graded him as one of the top route runners in the class.

Brian Thomas Jr. Fantasy Outlook: Hit Rates

• Underdog ADP: WR37, Round 6
• Rookie Dynasty ADP: WR4, Pick 5

When we include draft capital in the Super Model, the dropoff from Harrison Jr., Nabers, and Odunze to Thomas is significant. The average fantasy points for Years 1 and 2 dip from 14.1 to 10.0, and the hit rate on top-24 finishes falls from 67% to 27%.

The floor also dips with this profile, but over a third of prospects secure a top-36 finish while offering a shot at the top-12 upside — which is critical for fantasy purposes.

Thomas is coming off the board as a low-end WR3 in early bestball drafts. If Odunze goes almost 10 picks ahead of him in the NFL Draft, that is too close unless Thomas lands in the perfect spot with a path to immediate routes. Still, the overall sentiment of the market isn’t wrong. Thomas’ profile screams boom-bust WR4.

Xavier Worthy | Texas

• WR Super Model: 59th percentile
• Age: 21.4
• Height: 5-foot-11
• Weight: 165

Pedigree

• Program Quality Index: 50th percentile
• NFL Mock Drafts: Pick 32, Round 1
• 247 Recruit Player Rating: 4 of 5 stars

The University of Texas is a well-known Power 5 program, but they haven’t produced a first- or second-round WR since 2014. Devin Duvernay was the closest in 2020 as a Round 3 selection, but that could change this year. Worthy and Adonai Mitchell are projected as borderline Round 1 picks in the NFL Draft in mocks.

In the 2021 recruiting class, Worthy ranked as the No. 13 WR prospect — ahead of Harrison, Nabers and Thomas. Recruiting data doesn’t go into the model now, but we will test it in the future. For now, it is just good context to know.

Production

• Adjusted Career RYPTPA Index: 69th percentile
• Career Total TDs Per Game Index: 62nd percentile
• Career Targeted QB Rating Index: 41st percentile

After breaking John Ross’ record in the 40-yard dash with a time of 4.21, it opened the door for people to view them as similar prospects. However, nothing could be further from the truth on a production front. Ross didn’t post a significant RYPTPA until age 22.

Worthy exploded out of the starting gate as a freshman with 2.81 RYPTPA — the top mark in the class. While Worthy never reached those heights again, he still posted respectable marks of 1.86 and 2.14 in his sophomore and junior campaigns. His 69th percentile Adjusted Career RYPTA ranks fourth out of all the Power 5 WRs in the 2024 class.

The Longhorn product is a truly versatile weapon. While WRs that rely too heavily on gadget looks around the line of scrimmage or just deep throws don’t always translate well to the NFL, Worthy was a threat in both areas. He saw 25% of his targets come behind the line of scrimmage while also garnering 28% of his looks 20-plus yards downfield. 

His 13.7 aDOT is in that sweet spot where we know he isn’t too dependent on underneath looks. Furthermore, gadget players often struggle against man coverage, which wasn’t the case with Worthy. He dominated with a 32% TPRR against man and led the team versus zone at 28%.

If you single him up, he can win deep, but if you play off in zone, he can beat you underneath after the catch. Worthy registered the highest YAC over expected of all the Power 5 WRs in the class, at plus-2.2 yards per catch. 

At only 165 pounds, Worthy wouldn’t have had a chance to go in Round 1 five years ago. However, the NFL is changing, and lighter WRs like Tank Dell (165 pounds) and DeVonta Smith (170 pounds) have opened the eyes of NFL GMs who are starving for playmakers who can create horizontally and vertically.

Matt Waldman compares Worthy to two other lighter WRs of yesteryear in his Rookie Scouting Portfolio:

“If Worthy plays to his potential, he’s not far from developing along the lines of (Isaac) Bruce and (DeSean) Jackson, two of the great primary options with dangerous deep games in the past 25-30 years. If he doesn’t reach that tier of value, he still has the skills to realize what the emotional trouble Titus Young flashed all too briefly or what many thought K.J. Hamler had promise to become.”

Xavier Worthy Fantasy Outlook: Hit Rates

• Underdog ADP: WR50, Round 9
• Rookie Dynasty ADP: WR6, Pick 8

The Super Model prefers Thomas over Worthy when including projected draft capital, but the two are much closer without it. Worthy has proven more on the field, but Thomas meets the prototypical pedigree and size requirements that teams prefer.

If Worthy lands on a team with an open-minded coach willing to give him 70% to 80% route participation while maximizing his versatility, the young playmaker could shine. He is a boom-bust WR4, similar to Thomas, but you can get him three rounds later than Thomas.

I would be willing to trade down in dynasty drafts and pass on Thomas if I can still get Worthy and pick up extra draft capital. The two offer similar hit-rate profiles, but Worthy goes three picks later.

Troy Franklin | Oregon

• WR Super Model: 56th percentile
• Age: 21.6
• Height: 6-foot-2
• Weight: 176

Pedigree

• Program Quality Index: 20th percentile
• NFL Mock Drafts: Pick 34, Round 2
• 247 Recruit Player Rating: 4 of 5 stars

Oregon scores poorly in the Program Quality Index, pulling down Franklin’s score. However, he was the No. 3 WR recruit in the 2021 class, and based on the latest mock draft data, Franklin looks like a borderline Round 1 NFL Draft pick.

Production

• Adjusted Career RYPTPA Index: 59th percentile
• Career Total TDs Per Game Index: 58th percentile
• Career Targeted QB Rating Index: 86th percentile

Franklin played a limited role as a freshman, with only a 31% route participation, but moved into a full-time role in his sophomore and junior years. In those years, he posted 2.07 and 2.95 RYPTPA marks, helping boost his Adjusted Career RYPTPA Index to the sixth-highest score in the class at the 59th percentile.

When targeting Franklin, Oregon QBs enjoyed a 137.6 passer rating, the highest career finish for any WR in the 2024 class. His Career Targeted QB Rating Index of 86% ranks fourth-best in the database since 2018.

Nov 24, 2023; Eugene, Oregon, USA; Oregon Ducks wide receiver Troy Franklin (11) catches a pass for a touchdown during the first half against the Oregon State Beavers at Autzen Stadium. Mandatory Credit: Troy Wayrynen-USA TODAY Sports

Franklin was a strong performer against man coverage with a 28% TPRR. While we expect a WR’s TPRR to fall against zone, Franklin’s gap was abnormally wide, dipping to 21%. It could be nothing, but it is worth noting because NFL defenses utilize zone coverage over 70% of plays.

Film analysts like Franklin’s ability after the catch, but after his YAC over expected wasn’t special at 0.2 yards per catch. Franklin didn’t get as many looks behind the line of scrimmage (10%) to help pad those stats, but based on adjusting for aDOT and alignment, he was well behind Xavier Worthy (2.2).

Troy Franklin Fantasy Outlook: Hit Rates

• Underdog ADP: WR54, Round 10
• Rookie Dynasty ADP: WR5, Pick 6

Like Worthy, Franklin lags behind Thomas when including projected NFL Draft Capital, but the hit rates aren’t that different, and Franklin grades out much closer without capital.

Franklin is a WR worth targeting in best ball formats because his profile grades out similarly to the other two players in the tier who go earlier in drafts. In dynasty formats, his average draft position (ADP) is slightly above Worthy’s, which is fair, but if I were forced to choose, I would lean toward Worthy.

WR Super Model Inputs & Methodology

The inputs are in order of their correlation to fantasy production in a WR’s first two years in the NFL.

• Projected draft capital (NFL Mock Draft Database)
• Collegiate program quality
• Adjusted career receiving yards per team pass attempt
• Career targeted QB rating
• Career TDs per game
• Age

Because the model includes advanced data that isn’t widely available before the 2018 class, our sample focuses on WRs with at least two years of play since then. So, our correlations to future performance currently derive from WR data from 2018 to 2022.

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

Draft Capital Value

The model uses Chase Stuart’s Draft Value Chart for draft capital, which is 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.

Program Quality Index

Program quality uses the draft capital value to determine the total value each collegiate program has contributed to the NFL Draft at the WR position since 2014. Those scores are then indexed to form the Program Quality Index.

WRs who come from stronger 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 WRs with more target competition.

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

Adjusted Career Receiving Yards Per Team Pass Attempt Index

Yeah, that is a mouthful, huh? To help, we will shorten receiving yards per team pass attempt to RYPTPA, an acronym you will see throughout this piece. If you have a cooler name for us to use, don’t hesitate to DM me on X.

RYPTPA helps us normalize receiving yards based on the team environment, which is very important since how much a team throws can vary drastically.

The adjusted version of Career RYPTPA accounts for four critical variables that showed to impact performance:

• Age and class (i.e., first-year, second-year student, etc.)
• Average depth of target (aDOT) and alignment
• Team passer rating
• Teammate score (competition for targets)

Receivers who performed well in RYPTPA earlier in their careers enjoyed much stronger hit rates in their first two years. In fact, WRs who didn’t perform well until Year 4 and Year 5 correlated negatively with NFL success. To account for this, the model assigns heavier weights to the first three years.

This measurement also allows us to move away from breakout thresholds, which have a nasty habit of barely missing prospects, barely too low or high — everything is now on a scale.

The other three variables quantify an expected RYPTPA based on game-level data since 2014. Then, we can perform an over-expected calculation. 

The higher a WR’s aDOT, the higher their expected RYPTPA. The higher a team’s passer rating, the higher a WR’s expected RYPTPA. The stronger the teammate competition, the lower the WR’s expected RYPTPA.

These four factors are then weighted and combined into one data point and indexed (placed on a scale from zero to one).

Career Targeted QB Rating Index

This metric tells us the passer rating when a WR was targeted. There is an inherent overlap between targeted QB rating and RYPTPA data points because both use yards. 

However, RYPTPA tells us how a player performed in the context of their team, while targeted QB rating tells us how well a WR performed when targeted. That critical distinction allows these two metrics to work well together. 

I want to shout out to Peter Howard, the first person I noticed using this data point in their model.

Career Total TDs Per Game Index

The data showed that using a normalized metric like RYPTPA 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 WRs who are strong in RYPTPA score a lot of TDs.

Intuitively, this makes sense because we expect WRs who can score long TDs and provide value inside the 10-yard line to have an advantage over a small slot WR with a ton of targets.

Additionally, we account for the broadness of a WR’s utility by including rushing TDs in the career total.

Age Index

A player’s age derives from how old they will be at the beginning of the upcoming NFL season. It doesn’t carry as much weight in the WR model as it used to because we already account for age in adjusted career RYPTPA.

You will notice that the dominator rating, which combines a player’s percentage of yards and TDs, is no longer in the model. Career total TDs and RYPTPA offered stronger correlations to future production, and the dominator rating was duplicative, so it didn’t make sense to keep it in the model moving forward.

You might also be wondering why target share wasn’t included — especially considering how important it is at the NFL level. The answer is twofold: 1) RYPTPA is stronger, and the two correlate strongly. 2) The targeted QB rating was stronger and offered a holistic view of efficiency that we can’t get from target share, which made it a better pairing with RYPTPA.