2024 WR Rookie Super Model: Tier 5 - the Best Wide Receivers in the NFL Draft

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 5 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 5 – The Best 2024 Rookie WRs Who Need Draft Capital to Move Up

For this tier, I will provide high-level information and context for each category the model uses and then summarize my thoughts on each prospect under player summary. As we move further down the board in mock drafts, player expectations are more fluid and often less accurate.

We will revisit many of these in my post-draft rookie rankings because draft capital will drastically change some scores. I will also cover any prospects not listed who receive unexpected draft capital in more detail at that time.

Refer to the Rookie Model page for the model scores and data on all 2024 prospects. It will be updated with draft capital during the draft.

Ricky Pearsall | Florida

• WR Super Model: 42nd percentile
• Age: 24.0
• Height: 6-foot-1
• Weight: 189
• Ricky Pearsall Scouting Report via Chris Allen

Pedigree

• Program Quality Index: 55th percentile
• NFL Mock Drafts: Pick 47, Round 2
• 247 Recruit Player Rating: 3 of 5 stars

Production

• Adjusted Career RYPTPA Index: 42nd percentile
• Career Total TDs Per Game Index: 30th percentile
• Career Targeted QB Rating Index: 56th percentile

Player Summary

Pearsall climbed 50 spots in expected draft capital after a strong performance at the NFL Combine. The Florida product is a fifth-year senior who played his first three years at Arizona State before transferring. He didn’t secure a significant role until his third season at age 21.

Ultimately, his best years came in his final two seasons (ages 22 and 23), which doesn’t bode well for future fantasy production. When researching factors for the model, I found that Year 4 and Year 5 production correlated negatively to future fantasy production. To achieve a high Adjusted RYPTA score you must produce in the first three years.

Nov 11, 2023; Baton Rouge, Louisiana, USA; Florida Gators wide receiver Ricky Pearsall (1) catches a pass against the LSU Tigers during the first half at Tiger Stadium. Mandatory Credit: Stephen Lew-USA TODAY Sports

Much of what I just outlined about Pearsall’s profile is also true about names in Tier 4, but they either have higher expected draft capital or a better score in one of the other two production categories.

Ricky Pearsall Fantasy Outlook: Hit Rates

• Underdog ADP: WR72, Round 14
• Rookie Dynasty ADP: WR13, Pick 23

If you are a big Pearsall fan, you can consider him a Tier 4 prospect — it is a close call and choosing the cutoff points isn’t easy in this range. The hit rates are the same.

Ja’Lynn Polk | Washington

• WR Super Model: 41st percentile
• Age: 22.4
• Height: 6-foot-1
• Weight: 203

Pedigree

• Program Quality Index: 70th percentile
• NFL Mock Drafts: Pick 64, Round 2
• 247 Recruit Player Rating: 3 of 5 stars

Production

• Adjusted Career RYPTPA Index: 34th percentile
• Career Total TDs Per Game Index: 42nd percentile
• Career Targeted QB Rating Index: 60th percentile

Player Summary

Polk played at Texas Tech as a freshman before transferring to Washington for his final three seasons. While he never registered a powerful RYPTPA performance in his first three years, he enters the NFL as a young prospect for a four-year player.

The model accounts for target competition based on teammate RYPTPA, and Polk gets a boost thanks to his Program Quality Index (70th percentile), but this is an extreme case where the model is still underestimating him. We could see three Huskies taken in the first three rounds, including a potential top-10 pick in Odunze.

Polk’s Targeted QB Rating Index score at the 60th percentile tells us he is above average versus when he gets his opportunities. Pairing that score with his target competition is enough where. If you squint, you could weave a narrative that Polk scores too lowly in the model.

Ja'Lynn Polk Fantasy Outlook: Hit Rates

• Underdog ADP: WR77, Round 15
• Rookie Dynasty ADP: WR15, Pick 27

Malachi Corley | Western Kentucky

• WR Super Model: 41st percentile
• Age: 22.5
• Height: 5-foot-11
• Weight: 215

Pedigree

• Program Quality Index: 25th percentile
• NFL Mock Drafts: Pick 59, Round 2
• 247 Recruit Player Rating: 2 of 5 stars

Production

• Adjusted Career RYPTPA Index: 44th percentile
• Career Total TDs Per Game Index: 60th percentile
• Career Targeted QB Rating Index: 57th percentile

Player Summary

The Super Model doesn’t like Corley’s pedigree, but his production profile has positives, which means he has room to move up if his draft capital surprises. He didn’t break out until his third season, which hurts his Adjusted Career RYPTPA Index, but his above-average marks in TDs and Targeted QB Rating buckets help pull up his production score. 

At 5-foot-11 and 215 pounds, he is built like Deebo Samuel and played a similar role. Corley saw a jaw-dropping 37% of his targets come behind the line of scrimmage from schemed looks and lined up wide on only 8% of snaps. His YAC over-expected — which accounts for his 6.3 aDOT — was a tantalizing plus-2.4 yards per reception.

Sep 17, 2022; Bloomington, Indiana, USA; Western Kentucky Hilltoppers wide receiver Malachi Corley (11) evades tackle from Indiana Hoosiers linebacker Cam Jones (4) during the first quarter at Memorial Stadium. Mandatory Credit: Marc Lebryk-USA TODAY Sports

If Corley lands in a scheme that can maximize his talent and continues to develop, he has a chance to morph from a former two-star recruit into an NFL producer. However, the historical hit rate on players with a high percentage of manufactured touches (low aDOT and low wide alignment rate) is full of landmines.

• Kadarius Toney, Pick 20, 2021
• Rondale Moore, Pick 49, 2021
• Parris Campbell, Pick 59, 2019
• Amari Rodgers, Pick 86, 2021

Samuel had a 9.1 aDOT and played out wide 84% of the time, so while Corley has similarities in play style, the two players didn’t have identical collegiate roles and played against different levels of competition.

Malachi Corley Fantasy Outlook: Hit Rates

• Underdog ADP: WR78, Round 16
• Rookie Dynasty ADP: WR12, Pick 22

Jacob Cowing | Arizona

• WR Super Model: 41st percentile
• Age: 23.6
• Height: 5-foot-8
• Weight: 168

Pedigree

• Program Quality Index: 5th percentile
• NFL Mock Drafts: Pick 60, Round 2
• 247 Recruit Player Rating: 2 of 5 stars

Production

• Adjusted Career RYPTPA Index: 83rd percentile
• Career Total TDs Per Game Index: 54th percentile
• Career Targeted QB Rating Index: 50th percentile

Player Summary

Cowing is a fifth-year senior who started his collegiate career at UTEP before transferring to Arizona for his final two seasons. He earned playing time immediately as a freshman with a 65% route participation and hit 85% or better every year after.

Playing primarily from the slot (24% wide rate), Cowing registered a 28% career target share, the second-best mark behind Worthy for FBS WRs. His 83rd percentile Adjusted Career RYPTPA was the top score for an FBS receiver. While he is a five-year player, he did a ton of damage over his first three seasons, which keeps his production score high in the model.

However, Cowing gets balanced out because age (23.6) still carries weight in the model on its own to make sure we catch cases like these. And while the model doesn’t know this, it is worth pointing out that his production took a hit upon arriving at Arizona, with 2.25 and 1.86 outings.

Like many players in the final two tiers, landing spot could be everything for Cowing. He was productive enough that we shouldn’t dismiss his chances of becoming a successful slot WR on the right offense. That means landing on a pass-first team that loves 11 personnel and needs competition at the slot position.

Jacob Cowing Fantasy Outlook: Hit Rates

• Underdog ADP: WR108, Final Rounds
• Rookie Dynasty ADP: WR21, Pick 39

DeVontez Walker | North Carolina

• WR Super Model: 40th percentile
• Age: 23.2
• Height: 6-foot-1
• Weight: 193

Pedigree

• Program Quality Index: 45th percentile
• NFL Mock Drafts: Round 3
• 247 Recruit Player Rating: 3 of 5 stars

Production

• Adjusted Career RYPTPA Index: 50th percentile
• Career Total TDs Per Game Index: 67th percentile
• Career Targeted QB Rating Index: 68th percentile

Player Summary

Walker was initially set to play for North Carolina Central in 2020, but the team didn’t play any games due to COVID-19. He transferred to Kent State, where he played for the next two years before finishing his career in North Carolina.

Walker doesn’t have an excellent pedigree profile but worked his way up the college ranks. His production profile lacks in the Adjusted Career RYPTPA department due to his age when he finally posted decent numbers.

His 67th percentile and 68th percentile marks in the TD and Targeted QB Rating buckets are notable. Still, he also benefited from playing against small-school competition before his age-22 senior season at North Carolina.

That season, he posted a 2.32 RYPTPA, a 114.4 targeted QB rating and scored seven TDs in eight contests (.88 per game). For his career, Walker accumulated 32% of his targets 20-plus yards downfield, and he posted a 4.36 forty-yard dash along with a 40.5 vertical at the NFL Combine. 

Oct 14, 2023; Chapel Hill, North Carolina, USA; North Carolina Tar Heels wide receiver Devontez Walker (9) scores a touchdown against the Miami Hurricanes in the first half at Kenan Memorial Stadium. Mandatory Credit: Nell Redmond-USA TODAY Sports

The Super Model doesn’t incorporate NFL Combine drills due to low signal, but they are good context for a profile like Walker’s. Ultimately, if a team believes his 40-time and deep-threat production align, it will present as elevated draft capital. If you are wondering, the vertical has the strongest correlation to future success at the position.

His 60th percentile score without draft capital is on par with Tier 4 players like Coleman and Burton, but his expected draft capital has fallen about 30 spots since February. 

Typically, draft capital doesn’t worry me as much for players with a strong production score from a Power 5 program. However, Walker’s profile is a bit inflated because the Program Quality Index is currently based on final school.

Devontez Walker Fantasy Outlook: Hit Rates

• Underdog ADP: WR81, Final Rounds
• Rookie Dynasty ADP: WR14, Pick 24

Suppose you believe in Walker’s profile despite his boost from showing North Carolina pedigree (senior-year transfer). In that case, he sticks out as one of the better prospects in this range due to his score in the model without draft capital. If that were North Carolina Central (FCS) or Kent State, he would still outscore many options in this tier, but it would be closer.

Jalen McMillan | Washington

• WR Super Model: 37th percentile
• Age: 22.8
• Height: 6-foot-1
• Weight: 197

Pedigree

• Program Quality Index: 70th percentile
• NFL Mock Drafts: Round 3
• 247 Recruit Player Rating: 4 of 5 stars

Production

• Adjusted Career RYPTPA Index: 42nd percentile
• Career Total TDs Per Game Index: 43rd percentile
• Career Targeted QB Rating Index: 47th percentile

Player Summary

McMillan was a highly recruited player who turned down offers from Alabama and Notre Dame to play at Washington. His role was limited as a freshman, with a 35% route participation, but he stepped into a more significant role over the next three seasons.

The former four-star recruit never blossomed into a high-end RYPTPA performer, finishing with an adjusted career mark in the 42nd percentile. However, similar to my thoughts on Polk, I wonder if the model is doing enough to account for McMillan’s level of competition. 

The Super Model weights teammate production, and the Program Quality Index also helps here, but this is a pretty extreme case with the potential for three Huskies to go in the first three rounds of the NFL Draft.

Unfortunately, McMillan also didn’t score highly in the Career Targeted QB Rating Index, which measures how well you play when given an opportunity. This stat is a good balance against a team-driven stat like Adjusted Career RYPTPA.

On a positive note, McMillan posted a 27% TPRR against man coverage, which was second on the team behind Odunze at 29%. McMillan only played outside on 33% of snaps but wasn’t limited underneath targets with a healthy 12.1 aDOT. His 24% deep target rate was 4% above the NCAA average for WRs.

McMillan offers a versatile skillset, and his underlying pedigree and immense target competition could be the type of cocktail that makes us wonder why we didn’t have him higher in our dynasty rankings. 

Still, the model accounts for each of those variables, so I am inclined not to try to force my shares, but if he secures better-than-expected draft capital, it will have my attention.

Jalen McMillan Fantasy Outlook: Hit Rates

• Underdog ADP: WR92, Final Rounds
• Rookie Dynasty ADP: WR17, Pick 32

Xavier Legette | South Carolina

• WR Super Model: 36th percentile
• Age: 23.6
• Height: 6-foot-1
• Weight: 221

Pedigree

• Program Quality Index: 45th percentile
• NFL Mock Drafts: Pick 51, Round 2
• 247 Recruit Player Rating: 4 of 5 stars

Production

• Adjusted Career RYPTPA Index: 20th percentile
• Career Total TDs Per Game Index: 19th percentile
• Career Targeted QB Rating Index: 46th percentile

Player Summary

The only thing carrying Legette in the model is his expected draft capital. The good news for Legette lovers is that draft capital is the most important thing in the model. 

We are dealing with a fifth-year senior who never sniffed a strong RYPTPA until his age-23 season. Legette’s Adjusted Career RYPTPA was minus 0.07, which led to a 20th percentile score.

You might be wondering how a player achieves a negative score. The metric has four components, three based on the prospect’s actual RYPTPA versus expected RYPTPA. Those three areas isolate on aDOT and alignment, team passer rating and teammate competition.

With a low Total TDs Per Game Index score and slightly below average Career Targeted QB Rating Index score, we don’t have anywhere to hang our hat with this profile other than draft capital.

Even though draft capital is king, second- or third-round capital won’t make me feel warm and fuzzy about this profile. Legette wasn’t considered a Day 2 pick before the 2023 season, and if I were running an NFL franchise, I would not spend that sort of capital on this profile.

I am rooting for Legette because we want all players to succeed. Unfortunately, the track record for players with an Adjusted Career RYPTPA below the 30th percentile as fifth-year seniors who still managed to secure draft capital in the first three rounds hasn’t been good.

• Terry McLaurin
• Van Jefferson
• Miles Boykin
• Velus Jones Jr.

Terry McLaurin was the only hit from this group and came from a 90th-percentile Program Quality Index program. If Legette turns into a hit, I will re-label this tier of the Super Model as the Rudy Index because Ian Hartitz loves that movie, and it would be a great comeback story.

Xavier Legette Fantasy Outlook: Hit Rates

• Underdog ADP: WR71, Round 14
• Rookie Dynasty ADP: WR10, Pick 19

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.