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2022 NFL Draft: Using text analytics to evaluate the 2022 cornerbacks

Seattle, Washington, USA; Washington Huskies defensive back Kyler Gordon (2) participates in pregame warmups against the Washington State Cougars at Alaska Airlines Field at Husky Stadium. Mandatory Credit: Joe Nicholson-USA TODAY Sports

We are back with another positional writeup of draft prospects, this time focusing on the cornerback. We previously dove into the quarterbacks, running backs, wide receivers, tight endsoffensive linemen and interior defensive linemen, edge defenders and linebackers.

Thanks to math and feature engineering, we can use natural language processing to compare prospects to their contemporaries and those from the past before tying in advanced descriptive stats that we have built previously to gauge how well a prospect fits within a certain mold performed in the NFL.

For this analysis, we took prospect write-ups from The Athletic's Dane Brugler, one of the best football film analysts out there, over the past eight seasons (including 2022) and used latent semantic analysis (LSA) to derive similarity scores between the text in prospects’ scouting reports.

After building our dataset to span eight seasons, we can create a prospect's score in a number of ways. We decided to use a weighted average of similar players’ WAR (wins above replacement), using the similarity score derived above as the weights. For example, if a player has a 0.60 similarity score with a player who has earned 7.0 WAR since being drafted and a -0.3 similarity score with someone who has earned 4.0 WAR, his overall score would be +3.

Using the analyses above, we can look at 2022 prospects in a couple of ways. First, we can examine player comparisons for notable prospects. Second, we can rank the players in each position group by the score derived above. These scores have correlated well with draft position and future WAR generated at the NFL level, although a more robust analysis using additional seasons and data sources is beyond the scope of this article.

Let’s start by looking at the most successful NFL cornerbacks' text comparisons so that we can then see what it means for prospects in the 2022 class.

SUCCESSFUL TEXT ANALYTIC TRAITS

BAD TEXT ANALYTIC TRAITS

PLAYERS EXCEEDING THEIR DRAFT PEDIGREE

KALON BARNES, BAYLOR

The clear winner from the NFL combine, Barnes’ amended 4.23-second 40-yard dash was the fastest in Indianapolis. Barnes isn’t listed as an option on DraftKings to be one of the three cornerbacks off the board, and PFF’s big board ranks him 139th, so he should hear his name called sometime on Saturday, which is too late based on this text analytics performance. 

Brugler notes that “Barnes is speed-reliant and needs to improve his technique and feel down the field.” If his technique improves, Barnes has the intangibles to be a lockdown corner, but his speed alone holds value at the NFL level. None of Barnes' top-10 comparisons produced negative WAR, as A.J. Terrell was an obvious hit as his eighth closest. His speed offers enticing upside but also a reasonable floor for a projected fifth-round pick. 

Use promo code DRAFT50 for 50% off PFF’s ELITE Annual subscription and get a free year of PFF ELITE added to your subscription if a running back gets selected in Round 1.

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