Rookie Tight End Model

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!

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.
 

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, let’s examine the inputs used for the 2024 TE Super Model.

TE Super Model Inputs & Methodology 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.

Because the model includes advanced data that wasn’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 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, 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 okay. 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 WR model as it used to because we already account for age in adjusted career RYPTPA.

Additional notes

You will notice that the dominator rating, which combines a player’s percentage of yards and TDs, is no longer in the model. Adjusted Career RYPTPA is stronger than yards share and has significant overlap, so it didn’t make sense to keep dominator.

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.