Which tight ends in the 2024 NFL Draft have the best chance of producing strongly at the next level?

With the NFL Draft quickly approaching, we are releasing our updated Fantasy Life Rookie Super Model to help answer that question! Last week, we released the WRs, and now we move on to the TE position — where the model has made significant strides since last season.

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 — and this offseason was a SUPER one for tight ends. I don’t want to go overboard with praise for the model, but let's just say I am very encouraged, given how well it performed against important benchmarks.

Correlations to future fantasy performance:

  • Raw NFL Draft Pick: 0.58
  • NFL Draft Capital Value: 0.66
  • Super Model without Draft Capital: 0.65
  • Super Model with Draft Capital: 0.71 

So, let’s examine the inputs used for the 2024 TE Super Model and then see how the 2024 NFL Draft prospects fared.

Find Tier 3 of the TE Super Model here. 

TE Super Model Overview

The inputs below 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)
  • Adjusted career receiving yards per team pass attempt
  • Collegiate program quality
  • Speed score
  • Career targeted QB rating
  • Age

If you have read the WR Super Model, you will notice that the order is slightly different, and Speed Score replaces career TDs per game.

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

Tier 1 – Superstar Traits With High-End TE1 Upside

Brock Bowers | Georgia

Pedigree

  • Program Quality Index: 55th percentile
  • NFL Mock Drafts: Pick 10, Round 1
  • 247 Recruit Player Rating: 4 of 5 stars

Bowers has slipped a few spots in mock drafts over the past month, but he has a chance to become the third TE taken inside the top-10 picks since the 2018 NFL Draft. This would put him in the company of Kyle Pitts and T.J. Hockenson. 

He was a top-59 recruit in 2021 and the No. 2 TE in the nation, with offers from 22 schools, including LSU, Clemson, Michigan and Notre Dame. Per Chad Reuter of NFL.com, Bowers won the Shaun Alexander Award (nation’s top freshman) and followed that up with John Mackey Awards as the nation’s top TE in his sophomore and junior campaigns.

Production and Athleticism

  • Adjusted Career RYPTPA Index: 100th percentile
  • Career Targeted QB Rating Index: 92nd percentile
  • Speed Score Index: 44th percentile*

Adjusted Career receiving yards per team pass attempt (RYPTPA) is the strongest predictor in the model, with a 0.51 correlation to fantasy points per game over a TE’s first two seasons — only behind NFL Draft pick at 0.58. Rarely do we find a collegiate production metric so powerful, and Bowers has the top prospect score in our database at the 100th percentile.

The biggest driver for Bowers is how quickly he acclimated to the college game. The position can take many prospects as many as two seasons to earn playing time. However, Bowers erupted as a true freshman with a 2.16 RYPTPA — the highest mark on record. He followed that up in each of the next two seasons to earn an age-adjusted RYPTPA of 2.03 (96th percentile).

But that wasn’t all. Bowers delivered a top-three all-time finish in every component of Adjusted Career RYPTPA:

  • Career RYPTPA Over Expected for aDOT: +1.21
  • Career RYPTPA Over Expected for team passer rating: +1.18
  • Career RYPTPA Over Expected for Teammate Competition: +1.18

So, in the context of his team, Bowers was an absolute alpha TE compared to his peers from 2018 to 2023. Of course, he was also elite on an opportunity basis. 

When his QBs looked his way, Bowers rewarded them with a 141.9 QB rating — the second-best mark in our database. He averaged an astounding 3.1 yards after contact (YAC) over expectation per reception. Oh yeah, Georgia — a program known for RBs — got Bowers involved on the ground as well, with 183 yards and five rushing TDs.

Georgia Bulldogs tight end Brock Bowers (19) runs after a catch during the third quarter as Auburn Tigers take on Georgia Bulldogs at Jordan-Hare Stadium in Auburn, Ala., on Saturday, Sept. 30, 2023.

Bowers was equally impactful against man and zone coverages, outpacing his collegiate peers with at least 250 routes in all categories:

  • Man TPRR: 20% (+3 percentage points versus peers)
  • Man YPRR: 1.90 (+0.58 versus peers)
  • Zone TPRR: 25% (+10)
  • Zone YPRR: 2.92 (+1.60)

The Bulldogs prioritized Bowers as a playmaker, looking to get the ball into his hands any way possible, with 31% of his targets coming behind the line of scrimmage. That was 20 percentage points higher than the average TE in college football. 

If that were the only way Bowers won, it would be a red flag, but the young TE was three percentage points above average on targets 20-plus yards downfield (12%) and plus-eight percentage points in the 10-to-19 yard range (32%).

In his Rookie Scouting Portfolio, Matt Waldman, who has a stringent and comprehensive film-based grading process, spoke glowingly about Bowers.

 “Brock Bowers has the highest grade I’ve given a tight end since I updated the route-running criteria for the position 5-7 years ago. Bowers isn’t the most complete tight end ever graded — not even close. He doesn’t need to be. He’s the most talented combination of athlete, route runner, receiver, and ballcarrier that I’ve watched with this iteration of the RSP’s grading system and reminds me of talents like Vernon Davis and Aaron Hernandez.”

Bowers' Speed Score uses an average for the position because he didn’t participate in the 40-yard dash at the NFL Combine or his pro day due to a hamstring injury. He could run a workout for teams on April 10th. Bowers reportedly ran a 4.50 in 2019 at the Nike Sparq camp. That time would boost his Speed Score Index to the 80th percentile and his overall Super Model score to the 87th percentile.

With or without an updated 40-yard dash time, Bowers is an elite production prospect whose game can be applied to all areas of the field against a variety of coverages and he adds value after the catch. It doesn’t get any better as a prospect.

Brock Bowers Fantasy Outlook: Hit Rates

  • Underdog ADP: TE9, Round 7
  • Rookie Dynasty ADP: TE1, Pick 4

Hitting on an early-career breakout from the TE position isn’t easy. However, if Bowers lands on a roster without much competition, I wouldn’t bet against a top-six season out of the gate. The sample size of TEs at his grade is small, but 100% of them have at least secured a top-12 finish in one of their first two seasons — that is a reasonable floor, given the type of upside we saw from Sam LaPorta last year.

Bowers is a player I am excited about targeting in all formats this season at current ADP. In non-super flex dynasty leagues, once Marvin Harrison Jr. and Malik Nabers are off the board, Bowers is in the conversation with Rome Odunze. Bowers is the rarer profile of the two, but the ceiling for WRs is typically higher unless Bowers is the next Travis Kelce. In TE-premium formats, Bowers would be my selection with the third pick.

Tier 2 – Role Traits With Room to Grow Into A Low-End TE1

Ja’Tavion Sanders | Texas

  • TE Super Model: 48th percentile
  • Age: 21.4
  • Height: 6-foot-4
  • Weight: 245

Pedigree

  • Program Quality Index: 0th percentile
  • NFL Mock Drafts: Pick 40, Round 2
  • 247 Recruit Player Rating: 5 of 5 stars

Sanders was a wide receiver in high school but at 235 pounds was classified as an athlete (ATH) prospect. In 2021, he was the No. 30 prospect overall and the No. 1 ATH prospect in the country. He had offers from most of the prolific programs in the country, including Alabama, LSU, Ohio State and Georgia.

Texas ranks in the 55th percentile in program quality since 2014 when looking across RB, WR and TE. However, they haven’t had much success producing NFL Draft selections at the TE position, leading to a 0 percentile score. However, Sanders isn’t like most other Texas TEs — he ranks first all-time in career receptions and second in receiving yards for the position.

Production and Athleticism

  • Adjusted Career RYPTPA Index: 62nd percentile
  • Career Targeted QB Rating Index: 69th percentile
  • Speed Score Index: 40th percentile

Despite a non-existent role as a freshman, Sanders scored above average in the Super Model production model, where Years 2 and 3 are the most important RYPTPA years for TEs. Here are the correlations to future fantasy points for each year:

  • Year 1: 0.18
  • Year 2: 0.40
  • Year 3: 0.43
  • Year 4: 0.13
  • Year 5: -0.04

How well the prospect scores in age-weighted RYPTPA accounts for almost half of their Adjusted Career RYPTPA.

Sanders gets a bump from the Super Model in his production profile for playing with Xavier Worthy and Adonai Mitchell. He only gets credit for one season with Mitchell (transferred from Georgia), but both WRs could go in Round 1 of the NFL Draft. His career teammate-adjusted RYPTPA was 0.57 over expectation — the second-best mark in the 2024 TE draft class.

That score makes sense in the context of his targeted QB rating. Sanders capitalized when his opportunities came, delivering a 111.0 Career Targeted QB Rating (69th percentile).

Nov 11, 2023; Fort Worth, Texas, USA; Texas Longhorns tight end Ja'Tavion Sanders (0) in action during the game between the TCU Horned Frogs and the Texas Longhorns at Amon G. Carter Stadium. Mandatory Credit: Jerome Miron-USA TODAY Sports

A potential red flag in Sanders’ profile is how he played against man coverage. His 15% TPRR was two percentage points lower than the average collegiate TE, and his 1.26 YPRR was also average. However, some of this also goes back to target competition. Worthy was an absolute dawg against man, demanding a 32% TPRR. Press coverage can often come when facing man defense, and Matt Waldman noted that Sanders must improve in this area to transform into a primary option at the NFL level.

Some thought Sanders might post a faster time at the NFL Combine, but his 4.69 40-yard dash produced a Speed Score of 101.3 (40th percentile). That time could push his draft stock down further than expected. Some teams considering a receiving-first TE might not think Sanders offers the differentiating play-making ability if they aren’t factoring in his targeted QB rating and Speed Score.

Sanders is a big step down from Bowers, but almost any TE prospect since 2018 can fit that description. He isn’t a lock to succeed at the next level, but he has the traits that could allow him to develop into a primary option — especially on a pass-first team that values the receiving aspect of a TE game over blocking in the run game. 

Ja’Tavion Sanders Fantasy Outlook: Hit Rates

  • Underdog ADP: TE27, Final Rounds
  • Rookie Dynasty ADP: TE2, Pick 25

The most significant risk for Sanders is finding an offense that won’t pigeonhole him in a part-time role. The league is littered with passing-down TEs that suffer massive weekly variance based on game script. However, in the right spot, Sanders could develop into a No. 2 or 3 passing-game option and offer fantasy value.

The Super Model likes Sanders much more with his draft capital than without, so Sanders needs to stay in the Round 2 range of the NFL Draft.

TE Super Model Inputs & Methodology Overview

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

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 because how much college teams pass the ball varies drastically from one situation to the next. A prospect averaging 75 yards per game on a run-first offense might be better than another averaging 100 yards on a run-first squad on a per-team pass attempt basis.

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)

Tight ends who performed well in RYPTPA in Years 2 and 3 enjoyed more robust hit rates in their first two years in the pros. While it is more common for TEs to take longer to develop than WRs, many of the best prospects are doing it in a big way by Year 3. 

While WRs don’t get much credit for their fourth season, TEs can still impact their score in the model with a strong performance as seniors. However, the fifth year is much more common for TEs, but the correlation to future production was negative.

The model accounts for these nuances by weighting the years in this order: Year 2, Year 3, Year 1, Year 4. It is important to note that the model expects Year 1 to be at age 18 or 19. If the player doesn’t play in those two years, it counts as a redshirt season, and the player gets a zero RYPTPA. 

So, years are not always perfectly aligned with a player’s class, but that is OK. We want a measure that allows us to capture the spirit of age and time on campus, and this methodology accomplishes that goal without massive additional data mining.

This measurement also allows us to move away from breakout thresholds, which have a nasty habit of barely missing prospects, a little 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 prospect’s aDOT, the higher their expected RYPTPA. 
  • The higher a team’s passer rating, the higher their expected RYPTPA. 
  • The stronger the teammate competition, the lower the player’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).

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 TE position since 2014. Those scores are then indexed to form the Program Quality Index.

Prospects 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 prospects with more target competition.

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

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 TE, Speed Score garnered a 0.34 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 Targeted QB Rating Index

This metric tells us the passer rating when a prospect 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 prospect performed when targeted. That critical distinction allows these two metrics to work well together.

Note: YPRR correlated more strongly to future performance than targeted QB rating and was in consideration. However, its overlap with RYPTA was high. The correlation was 0.82, while the targeted QB rating was 0.38. That made targeted QB rating a superior option for the opportunity-context data point in the model (RYPTPA is a team context stat). The lower correlation between the two makes sense because targeted QB rating accounts for completions, incompletions, TDs and INTs, adding breadth to our view of the prospect.

I want to shout out to Peter Howard, the first person I noticed using targeted QB rating in their model.

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 TE 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.