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2022 NFL Draft Slot Projections: Tight Ends

Iowa City, Iowa, USA; Colorado State Rams tight end Trey McBride (85) runs with the ball after a catch as Iowa Hawkeyes linebacker Jestin Jacobs (5) goes to make the tackle during the second quarter at Kinnick Stadium. Mandatory Credit: Jeffrey Becker-USA TODAY Sports

In the run-up to the 2022 NFL Draft, we'll be predicting the draft order for every non-quarterback or specialist position based on their athleticism numbers and final-season college production. We've already done so for edge defenders, wide receivers and cornerbacks.

The goal of this series is to identify players with similar athleticism and production profiles to those who are historically drafted early. This gives us some indication of the type of player the NFL prefers to pick early. Additionally, separating production and athleticism numbers allows us to categorize players as producers or athletes, which may yield insight into their potential NFL success.

While the target variable of our modeling was pick number, we will only be reporting the rank relative to the player’s draft class — tight ends, in this case. This is because there are too many externalities for why a player is selected when they are, such as the relative strength of the draft class, individual team needs and team measurable thresholds, for the prediction to be well-calibrated. However, by looking at a player’s rank, we can get a better idea of how they stack up relative to their peers.

Athletic testing numbers come from the NFL Scouting Combine, and when possible, missing data was filled in with pro day measurements. If a player did not participate in more than three drills across the combine and their pro day, they were excluded from the dataset. For the 2021 tight end class, that threshold excludes Jeremy Ruckert and Cade Otton, among others.

If a player qualified for the dataset, their missing events were assumed to be the position average over the past five combines. These measurements and times were then thrown into a random forest model to handle potential nonlinearities and complex relationships between variables to predict draft position.

In a change from the previous entries in this series, the model does not put a large premium on straight-line speed or size for tight end prospects. The largest differentiator at the position in terms of athleticism appears to be performance in agility drills, including the three-cone and short shuttle. Most offenses expect their tight ends to operate in the underneath portion of the field where quickness is more important, and their size should be able to remedy any speed deficiencies if they are asked to win downfield. 

We additionally modeled draft position based strictly on production metrics charted by PFF, also in a random forest model to handle nonlinearities. The inputs included rate metrics, such as target rate and run-block win rate, as well as volume metrics, such as drops and receiving yards. We also added an indicator for whether the player was in a Power Five conference since strength of competition is meaningful for NFL evaluators.

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