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 4 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 4 – Role Traits With Room to Grow into WR3-Plus Talents

Ladd McConkey | Georgia

  • WR Super Model: 53rd percentile
  • Age: 22.8
  • Height: 6-foot
  • Weight: 186


  • Program Quality Index: 65th percentile
  • NFL Mock Drafts: Pick 33, Round 2
  • 247 Recruit Player Rating: 3 of 5 stars

McConkey was not a super highly lauded recruit coming out of high school as a three-star WR, but he found his way into a sizable role at a strong program at Georgia. His expected draft capital has risen from Round 5 to a borderline Round 1 pick over the last nine months, making him one of the biggest climbers at the position.


  • Adjusted Career RYPTPA Index: 36th percentile
  • Career Total TDs Per Game Index: 42nd percentile
  • Career Targeted QB Rating Index: 79th percentile

McConkey redshirted as a freshman before earning playing time in his second year on the Georgia campus. He never earned a full-time role, and his highest route participation came as a redshirt sophomore, at 68%.

That part-time status took a toll on his Career RYPTPA Index — he finished his career in Athens with a 36th percentile score. We can’t dismiss McConkey’s inability to carve out a more significant role because earning playing time matters. The first step to earning targets and yards is getting on the field.

Of course, this is also where the QB Rating Index comes into play. It tells us how good a player was on a per-target basis, which helps balance out a stat like RYPTPA. The QB Rating Index doesn’t carry the same weight in the model because we know there is an overlapping signal with RYPTPA, but it also captures elements like TDs and INTs that we miss with RYPTPA.

That said, McConkey was the third-best prospect in the class in the QB Rating Index, with a 79th-percentile score. If McConkey can land in the right situation with access to more playing time, he could have sneaky upside.

It is easy to fall into the trap of labeling McConkey as an underneath slot WR — which still might be his best fit in the NFL — but his data profile offers more than that. He lined up wide on 69% of his snaps and registered an 11.6 aDOT.

McConkey’s depth-of-target profile includes a heavy dose of looks behind the line of scrimmage. His 26% was well above the 14% average for NCAA WRs but was also on par with the NCAA average in deep targets at 20%. Interestingly, the intermediate game (10 to 19 yards) was his most-targeted area of the field at 30% (above the NCAA average).

This data aligns with what the film analysts found. Lance Zierlein described McConkey as a player who can “uncover at all three levels,” and Matt Waldman graded him as one of four star-caliber route runners in the class. 

Waldman elaborated further on McConkey’s potential as a vertical threat at the next level, noting that while he doesn’t enter the league with the same contested-catch ability he saw from Tyler Lockett along the boundary, the speed to win similarly is there. 

McConkey proved equally adept against different coverages, earning a 23% TPRR against man and 25% against zone. His 3.05 career YPRR against zone looks is one of the best in the class — even stronger than Maserati Marv. 

While earning targets against man coverage is a more sticky stat, the NFL is a zone-dominated league, which could make McConkey a viable target more times than not if he is genuinely a zone virtuoso.

McConkey also added value on the ground, registering 216 rushing yards and four TDs in Athens. His four TDs tied Ainias Smith for the best mark in the class.

Ladd McConkey Fantasy Outlook: Hit Rates

  • Underdog ADP: WR56, Round 11
  • Rookie Dynasty ADP: WR8, Pick 12

McConkey is my preferred prospect in this range. Tier 4 contains many players who have questions about their game. With McConkey, the question is whether he can earn a more prominent role at the next level.

Some might argue that the ceiling of an Adonai Mitchell is higher thanks to his athletic profile, but athletic testing isn’t as strong as the production metrics in the model. I don’t want to say athleticism never matters. However, what is more important is how it translates to the field, and unearthing freak athletes who suddenly become high-end fantasy prospects is a low-probability game.

Give me the player who has been good when given the opportunity — especially one who played in the SEC and wasn’t a one-trick pony.

FF Hit Rates

Like his NFL Draft capital, McConkey’s fantasy ADP is rising. Since mid-February, he has climbed 33 spots on Underdog.

However, he is in a range where I am still willing to press the draft button.

Adonai Mitchell | Texas

  • WR Super Model: 55th percentile
  • Age: 21.9
  • Height: 6-foot-2
  • Weight: 205


  • Program Quality Index: 50th percentile
  • NFL Mock Drafts: Pick 28, Round 1
  • 247 Recruit Player Rating: 4 of 5 stars

Mitchell played his first two seasons at Georgia before transferring to Texas as a junior. Technically, he would grade out slightly higher if we gave him credit for being a Georgia recruit, but he was a three-star prospect.

Mitchell’s draft stock looked like it would dip, but he had a fantastic NFL combine, stabilizing his mock draft status as a late Round 1 pick.


  • Adjusted Career RYPTPA Index: 35th percentile
  • Career Total TDs Per Game Index: 47th percentile
  • Career Targeted QB Rating Index: 61st percentile

Mitchell might be an uber-athletic specimen, but his athleticism never showed up in the way you would expect in his production profile. If he goes in the first round of the NFL Draft, his 35th percentile Adjusted Career RYPTPA would be the lowest on record. The average first-round pick is at the 65th percentile since 2018.

Texas Longhorns wide receiver Adonai Mitchell (5) catches the ball for an first down against Kansas State Wildcats cornerback Jacob Parrish (10) in the. First quarter of an NCAA college football game, Saturday, November. 4, 2023, in Austin, Texas.

Mitchell has the fourth-highest career aDOT in the class (15.6), and his best fit is likely as a deep ball specialist early in his career. He saw 25% of his targets come on 20-plus yard throws, where he produced 519 yards and eight TDs over his career — which is helping his Career Targeted QB Rating Index.

While a deep threat with size can always find a niche role on a team, the league is littered with players of that archetype who never grew into more — think Marquez Valdes-Scantling or Miles Boykin. MVS had a 31st percentile Adjusted Career RYPTPA coming out of USF, and Boykin left Notre Dame at the 16th percentile. Per their Relative Athletic Score (RAS) comparison, Boykin was also a high-end athletic tester.


The average collegiate WR in Mitchell’s aDOT range — not even focusing on talents that made it to the NFL — earns 1.52 receiving yards per team pass attempt. Mitchell finished 14% below that at 1.30. MVS and Boykin were at 8% and 30% below expectations.

Even adjusting for passer rating, all these WRs were below expectation in RYPTPA. The bottom line is that Mitchell has to become more than a one-trick pony to pay off a Round 1 NFL pick.

Adonai Mitchell Fantasy Outlook: Hit Rates

  • Underdog ADP: WR47, Round 8
  • Rookie Dynasty ADP: WR7, Pick 9

No other player is being propped up more by their projected draft capital than Mitchell. His hit rates with draft capital are far higher than without, so he needs the first-round capital to have any value.

Hit Rates

Mitchell is an avoid for me in all draft formats unless he falls well past ADP. If things don't go his way, he could be one of the biggest fallers post-draft.

If he does secure first-round capital, we still shouldn’t expect an immediate breakout from this type of profile. However, he becomes draftable because there is still a shot he could come up with a usable fantasy season based on hit rates with draft capital.

Keon Coleman | Florida State


  • Program Quality Index: 30th percentile
  • NFL Mock Drafts: Pick 41, Round 2
  • 247 Recruit Player Rating: 4 of 5 stars

Coleman’s draft expected draft capital has declined since the NFL Combine. At his peak, mock drafts had Coleman as a mid-first-rounder, but after running a 4.61 40-yard dash, the market has cooled on the former No. 60 WR recruit from the 2021 class.


  • Adjusted Career RYPTPA Index: 45th percentile
  • Career Total TDs Per Game Index: 57th percentile
  • Career Targeted QB Rating Index: 57th percentile

Coleman showed promise as a 19-year-old sophomore at Michigan State with a 1.93 RYPTA but couldn’t build on that after transferring to Florida State for his junior season (1.78).

While he never became a high-end target earner, he was steady against both man and zone coverage with 25% and 23% TPRRs. However, he ranked third-worst in the class in contested target rate. Adjusting for aDOT and alignment, his 26% contested rate was six percentage points above expectation. Lance Zierlein noted that Coleman “could struggle to find separation to avoid excessive contested catches” in his scouting report.

Still, Coleman has the size and ball skills to unlock more in the proper role. Matt Waldman was less concerned about Coleman’s separation issues, noting he projects well on underneath and intermediate routes in the pro game, but that landing spot would be essential.

“If the design of the RSP’s evaluations were to determine how likely a player would earn his best-case team fit, Coleman would have a lower grade because to unlock his complete game, he’ll need a quarterback with excellent anticipation and confidence throwing into tight windows.”

Coleman’s data profile isn’t great, but it is still in the range where unearthing a top-24 WR is possible. While much of this comes back to the player, landing in the right spot can still be huge for unlocking players with a strong but limited set of skills.

Keon Coleman Fantasy Outlook: Hit Rates

  • Underdog ADP: WR60, Round 12
  • Rookie Dynasty ADP: WR9, Pick 14

Coleman’s profile with expected draft capital remains in the same band as the Tier 3 players. However, his hit rates are lower in his profile without draft capital, which creates a tier break.

Hit Rates

While Coleman’s outlook isn’t great, he is still in the range where Lloyd Christmas would tell us, “So you’re telling me there’s a chance.” That makes him a viable pick at his current ADP, priced below profiles with similar hit rates.

Jermaine Burton | Alabama

  • WR Super Model: 47th percentile
  • Age: 23.2
  • Height: 6-foot
  • Weight: 196


  • Program Quality Index: 100th percentile
  • NFL Mock Drafts: Pick 53, Round 2
  • 247 Recruit Player Rating: 4 of 5 stars

Georgia initially recruited Burton, where he played for two seasons before transferring to Alabama. Technically, his Program Quality Index might need to come down, and we will look at it in the model next season for transfers.


  • Adjusted Career RYPTPA Index: 45th percentile
  • Career Total TDs Per Game Index: 46th percentile
  • Career Targeted QB Rating Index: 63rd percentile

Burton’s profile is intriguing because while he never exploded in the RYPTPA category, he was relatively strong each step of the way, based on his age. He carved out a 48% route participation as a true freshman at Georgia and registered a 1.32 RYPTPA. He finished his career with a 45th percentile RYPTPA — better than any other WR in this tier.

We don’t want to overstate the meaning of RYPTPA since the model weights each component based on signal strength for future fantasy points. However, Burton grades out as the best player in the tier when excluding projected draft capital because Adjusted Career RYPTPA carries the most weight in the production department.

It is encouraging that Burton graded out at the 63rd percentile in the QB Rating Index, which tells us he was a plus player on a per-target basis versus his peers. When we add it all up, we have a solid player in light of the team but showed upside when using his opportunities as the basis for evaluation.

Nov 25, 2023; Auburn, Alabama, USA; Alabama Crimson Tide wide receiver Jermaine Burton (3) carries for a touchdown during the second quarter against the Auburn Tigers at Jordan-Hare Stadium. Mandatory Credit: John Reed-USA TODAY Sports

Nothing guarantees future success, but George Pickens and Nico Collins possessed this interesting low-key combination and hailed from quality programs. Both have gone on to become factors at the NFL level despite neither securing elite draft capital as Round 2 and Round 3 selections.

Burton was a deep threat in college, with 28% of his targets coming 20-plus yards downfield. Unlike many of the other field stretchers in the tier, Burton exceeded expectations by 8% after adjusting for his aDOT of 16.9 yards.

But Burton wasn’t just a deep merchant. He garnered 40% of his targets on intermediate routes between 10 and 19 yards downfield. Burton graded out as a star-caliber route technician in the Rookie Scouting Portfolio. Matt Waldman described Burton as a “route technician who can play all three positions” and “a candidate for the designation of having a better NFL career than college career.”

Jermaine Burton Fantasy Outlook: Hit Rates

  • Underdog ADP: WR84, Final Rounds
  • Rookie Dynasty ADP: WR18, Pick 34

Burton grades out in the same tier as the rest of Tier 4 when baking in expected draft capital. However, he separates himself from the group thanks to the top grade without draft capital. 

Hit Rates

Burton is free in best ball and dynasty formats, possibly making him the most important name in this tier. His post-draft grade in the WR Super Model is the least dependent of the group on his draft capital, and we have seen some players with his makeup find their way to success in the NFL, like Pickens and Collins. 

When you combine all of that into one sentence, it reads Draft Jermaine Burton!

Roman Wilson | Michigan


  • Program Quality Index: 50th percentile
  • NFL Mock Drafts: Pick 46, Round 2
  • 247 Recruit Player Rating: 4 of 5 stars

Wilson comes from a well-established Power 5 program that has produced six drafted WRs since 2014, including Devin Funchess, a second-round pick, and Nico Collins and Amara Darboh, third-round picks.


  • Adjusted Career RYPTPA Index: 35th percentile
  • Career Total TDs Per Game Index: 43rd percentile
  • Career Targeted QB Rating Index: 75th percentile

Wilson was a part-time player until registering 72% route participation in his senior season at age 22. That significantly impacted his Career RYPTPA Index, which is at the 36th percentile — pulling his overall grade in the Super Model down.

However, Wilson is similar to McConkey in that on a per-target basis, he flashed, as we can see with his 75th-percentile Career Targeted QB Rating Index. Michigan passers enjoyed a 127.5 rating when locking onto Wilson.

Wilson saw 29% of his targets come on throws of 20-plus yards, nine percentage points above the NCAA WR average. He finished his career with a 14.1 aDOT, and similar to Adonai Mitchell, he performed below expectations in RYPTPA after accounting for target depth (-9%).

Lance Zierlien summarized Wilson as an explosive athlete who can push the vertical boundary but needs refinement in his routes to unlock his potential. Matt Waldman noted Wilson’s “lack of sharpness and snap in the intermediate range of the field” as a deficiency that could hinder his career, given how vital those routes are for big-time WRs.

Wilson was average against man and zone coverage, with TPRRs of 23% and 20%.

There is a chance Wilson could develop into more, but the model didn’t have much to hang its hat on in this production profile. Even after digging into the secondary data and scouting profiles, I find arguing with his final percentile score challenging.

Roman Wilson Fantasy Outlook: Hit Rates

  • Underdog ADP: WR70, Round 13
  • Rookie Dynasty ADP: WR11, Pick 21

Historical data suggests that someone from this tier will surprise us and make noise in fantasy football. This profile has produced a top-24 finish in their first two seasons 25% of the time. However, figuring out which player it will be is the hard part.

FF Hit Rates

The best part about Wilson is that he is priced the second-lowest out of the WRs in this tier, which makes him someone we can throw a dart at in drafts. You can get Wilson five rounds later than Mitchell, which is the type of arbitrage we want to exploit. 

One of the best ways to make this research payoff is to acknowledge that it is a numbers game and that we shouldn’t fall in love with any player in the tier. It is OK to have a favorite for sound reasons (i.e., McConkey), but that doesn’t mean we shouldn’t draft Wilson. Thanks to ADP, we can get both if we want.

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.

Dwain McFarland
Dwain McFarland
Dwain is the Lead Fantasy Analyst and Director of Analytics of Fantasy Life. He is best known for the Utilization Report, which led to his first full-time role in the industry at Pro Football Focus. Dwain’s experience and background have helped him craft a unique voice in the fantasy football community. He has placed highly in multiple national season-long contests, including three top-five finishes at the FFPC. Before beginning his fantasy career in 2018, Dwain led product strategy and data and analytics teams for one of the largest healthcare improvement companies in the nation.