2019 Wide Receiver Draft Model

Our Wide Receiver draft model incorporates player and team level college statistics and NFL Combine metrics to generate predicted probabilities of possible outcomes for each player’s NFL career. We focus on each player’s likelihood of making a Probowl, becoming a starter, becoming a role player (3rd – 5th wide receiver), or not making it in the NFL. Our model is trained on a sample of all player’s who have attended the NFL combine since 2000. Below are our results for the 2019 draft class.

2019 Draft Class

playerPro BowlStarterRole PlayerNon NFL
Parris Campbell0.260.530.140.07
JJ Arcega Whiteside0.20.230.330.24
Emanuel Hall0.150.340.350.16
Andy Isabella0.140.520.230.1
Marquise Brown0.140.470.270.13
AJ Brown0.120.430.140.31
Dillon Mitchell0.110.370.380.14
Deebo Samuel0.10.320.170.41
Gary Jennings0.10.330.320.25
Miles Boykin0.10.450.250.2
Anthony Johnson0.090.380.190.35
David Sills0.090.310.360.24
Tyre Brady0.090.210.450.25
Stanley Morgan0.
Darius Slayton0.070.340.310.29
Hakeem Butler0.070.280.330.32
Antoine Wesley0.060.350.290.3
Greg Dortch0.060.40.320.22
NKeal Harry0.
Terry Godwin0.060.390.050.5
Jamal Custis0.050.180.310.46
Johnnie Dixon0.050.460.280.21
Jovon Durante0.050.320.070.57
Kelvin Harmon0.
Riley Ridley0.
DK Metcalf0.040.30.370.29
Jamarius Way0.
Mecole Hardman0.040.480.290.19
Terry McLaurin0.040.390.370.21
Cody Thompson0.
Diontae Johnson0.
Lil'Jordan Humphrey0.
Jalen Hurd0.
Travis Fulgham0.
Jaylen Smith0.
Ryan Davis0.010.310.160.52
Hunter Renfrow00.120.150.72
Jakobi Meyers00.110.250.64


  • Variables involving touchdowns scored in college are strongest predictors of NFL success. Specifically, our models incorporate touchdown rate, touchdown market share, touchdowns per game and total touchdowns. I believe these metrics are important because touchdowns can encapsulate a player’s big play ability, contested catch ability, and the willingness of the player’s coach and quarterback to go to that player in key situations like plays in the red zone. The following table shows the average values of some of our player-level variables for each response group.
  • According to our model, Paris Campbell has the strongest chance of becoming a Probowl wide receiver in the NFL. Campbell didn’t necessarily light the NCAA on fire but he checks a lot of boxes that suggest he could be a good NFL wide receiver. He was a highly ranked recruit coming out of high school, he ran a blazing 4.31 40 at the NFL Combine, he led his team in receiving in his final season with 1,063 yards and he scored 12 touchdowns, and he did all of this while playing for a very good team – Ohio State had an SRS of 17.7 in his final season.
  • DK Metcalf is a player who has drawn a ton of attention due to his rare combination of size and speed. Our draft models are not nearly as high on him as other mock drafts and projections. This is mostly due to the fact that he only played 7 games in his final college season, and when he did play he was not as effective as most top prospects tend to be. Metcalf averaged 64 yards per game and 0.63 touchdowns per game over his career at Ole Miss. It also hurts Metcalf considerably that he was never his team’s top receiver for a complete season. Despite this pessimistic projection, Metcalf clearly has upside that is not completely captured by his college statistics. This case is a good example of why scouting and film study are still crucial in player evaluation.
  • Andy Isabella is an interesting prospect that our models areĀ  higher on than most in the NFL Draft community. Isabella was a very effective receiver at UMass averaging 97 yards and 0.83 touchdowns per game over his college career, and he led his team in receiving yards twice. On top of that he ran a blazing fast 4.31 in the 40 yard sprint at the NFL Combine. Some downsides of Isabella is that he has a small frame at 5’9, 188 lbs and he played for a weak team in college (SRS: -11.06). It will be interesting to see where Isabella gets picked on draft day!

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