Why the Eagles Should Take a CB in Round 1

Greg Hart
6 min readMar 21, 2021

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It’s hard to find an Eagles mock draft that does not have them taking Ja’Marr Chase, Jaylen Waddle, Devonta Smith, Kyle Pitts, or one of the QBs with their 6th pick in the 2021 draft. Very, very few have them taking one of the top CBs at 6 with various reasons, but all with the theme that WR is a more valuable position than CB:

“You have to take a QB or WR — elite positions — at 6”

“WR is the Eagles biggest need and we missed taking Justin Jefferson in 2020”

“6 is too high for a CB — you can get CBs later in the draft”

The view that picks in the top 10 are too high to take a CB is the one that bugged me the most and got me started looking for data sets to see what the analytics say. Before getting into the analysis, the short of it is:

Cornerback is a more valuable use of a 1st round pick than Wide Receiver. Period.

Before getting into the data, just a caveat that I am not a scout, this is not a scouting report, and could Chase be such a generational talent and so much more valuable than Caleb Farley or Patrick Surtain? Sure, maybe. Now on to the data…

(Note: The following uses the last 10 years of draft data and ProFootballReference’s Approximate Value valuation metric. A fuller explanation of the data set is available here — “An NFL draft AV data set”)

Positional value by draft round

These first tables show both the average total career Anticipated Value (AV) and the average AV per game by position and draft round. This shows the average or expected AV for a position when drafted and there aren’t really surprises here, with QB, IDL, Edge, and OL generally showing the highest expected AV in early rounds.

Drop-off in expected player value by round

Then I calculated the percentage drop-off in AV by round for each position in these next tables. This will show which positions degrade quickest in value and which positions present relatively more value later in the draft.

Few key points here:

  • Cornerback has the largest drop-off from round 1 to 2 by Career AV (46% decrease in expected career AV from round 1 to 2) and the third greatest drop-off in AV per game (30% decrease from round 1 to 2), behind only QB, Edge, and IDL.
  • Cornerback continues the degradation in round 3 behind only IDL, Edge, Tackle, and LB in percentage decrease in expected career AV and the largest positional decrease by AV per game.
  • Wide receiver is one of the best positions of value, especially through the first three rounds, losing 30% of its expected career AV in round 2 and only 16% of its expected AV per game.
  • One note on QB as most would expect that show the largest decrease in value over rounds. The reason its career AV decrease is one of the smallest is because the bust rate on round 1 QBs is much higher than most positions, which weighs the average career AV down.

Visualizing Cornerback and Wide Receiver Round Value

These next two charts visualize the above data to show the percentage likelihood of drafting elite (90th percentile), above average (60th to 90th percentile), or league average (40th to 60th percentile) WRs and CBs by round.

You can see the steeper drop-off in expected value for CBs in round 2 and beyond and the relative value that WRs maintain largely through the first 100 picks:

  • For CBs, teams have a 38% chance of drafting an elite CB and a 46% chance of drafting an above average CB in the first 10 picks. In picks 11–20, the chance of an elite CB is 31% and an above average one is 52%.
  • For WRs, there is an 18% chance of drafting an elite WR in the first 10 picks and a 54% chance of an above average WR. In picks 11–20, the elite chance is 20% and above average is 60%.
  • The steep drop-off in CB value occurs in the mid second round, with the chance of an above average CB below 50% in total. The chance of an elite CB drops to zero in the 41–50 pick range and chance of an above average CB drops to 37% (falling from 72% in picks 31–40).
  • WRs continue to show relative value through round 2 and much of round 3. Chance of an elite WR is between 6% and 13% from picks 31 to 70 and chance of an above average WR stays between 40% and 53% in the same pick ranges.

Two final charts to visualize the data which show a scatter of CB and WR picks by round vs. the percentile value of each pick. The box plots at the top of each chart show the 25th percentile, median, and 75th percentile pick location for each player class (elite, above average, league average, and poor)

Again, this shows the concentration at the top of the draft for elite and above average CBs and the relative value of WRs deeper into rounds 2 and 3:

  • Elite CBs had a median draft pick location of 18, with the top quartile of elite CBs at pick 10 and bottom quartile at pick 33, at the very top of the second round.
  • Elite WRs had a median draft pick location of 36, with the top quartile at pick 23 and bottom quartile at pick 70.
  • Above average player classes normalize a bit, with CBs having a median draft pick location of 51 vs. WRs at 61. The spread on above average CBs is greater than WRs, with WR quartiles ranging from pick location 31 to 88 vs. 27 to 102 for CBs.

Summary

This is an analysis of what the past 10 years of draft data empirically shows on player value data and why the “you shouldn’t take a CB that high” view is just wrong. It obviously is not a scouting analysis of the top WRs and CBs available in this year’s draft and many will say why Ja’Marr Chase or Jaylen Waddle are such generational talents and can’t be skipped. But WR is a position that has shown more value at pick 37 or beyond, including Michael Thomas (picked at 47), A.J. Brown (51), Davante Adams (53), DK Metcalf (63), Kennan Allen (76), Cooper Kupp (69), Stefon Diggs (146), Antonio Brown (195), and of course Tyreek Hill (165). The list of top CBs that have been available at 37 or later is a much shorter list with Xavien Howard (38), Tyrann Mathieu (69), Kam Chancellor (133), and Richard Sherman (154).

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Greg Hart
Greg Hart

Written by Greg Hart

Background in economics, statistics, and computer science and work in tech building healthcare products.

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