Using Analytics to Find NFL Counterparts for Draft-Eligible Receivers

With the NFL combine over and the NFL draft set to hit soon after the free agency uproar dies down, Vikings fans and the team itself will have some time on their hands to think about the upcoming draft prospects at wide receiver. With a class that’s deep and top-heavy, it will be more important than ever to discriminate between one second-tier receiver and another.

Sometimes, people will pore over data—combine scores, college production, etc.—in order to see which of those receivers will have the best NFL career. To an extent, this can be pretty valuable, and is one of the more well-known branches of the controversial sports movement best known as “analytics,” but that’s not all there is.

A lot of times when discussing analytics, people are concerned with answering the question “which player is better?” instead of the broader questions that might concern an organization. Many times, “analytics” aren’t designed to figure out which players are better, but any number of other things, like which successful strategies that coaches use that are underutilized, ways to redesign practices, ways to optimize athlete nutrition, even the best routes for an area scout to travel in order to maximize exposure to prospects and minimize scouting fatigue.

“Analytics” are used by every organization because all the word means is an effective use of information. So, while they may not embrace the use of numbers or stats to grade a player, they all have a way of prioritizing and organizing information—like scouting reports, players on the trade block and so on.

In this case, we can use analytics not to grade or rank players but to see what role may be their best fit in the NFL.

Because teams will often draft roles instead of positions, we may be able to see what roles some of the top receivers in the draft will play and whether or not that is something the Vikings want.

We could be limited to player comparisons made by an array of analysts, but that has limitations. I’ve seen 29 different player comparisons for Corey Coleman, with vastly different players like Tavon Austin and Anquan Boldin, or Reggie Bush and Odell Beckham.

Instead, let’s use data captured by Matt Harmon of Backyard Banter to see where a college player is strongest, and compare those strengths and weaknesses to established NFL players. He uses a metric called SRVC, or “success rate versus coverage” that measures how often a player gets open in certain situations.

This is basically just an organized scouting report, as it still requires Harmon to watch over 200 routes from each prospect and look at how often they win, why, and in what circumstances. He does the same for NFL players as he does NCAA players, and though they will have different levels of competition, seeing which routes they perform better running relative to themselves gives us a map for who they are.

In this case, we can determine which routes players succeed the most at, and against which types of coverage a player succeeds most often. By comparing their relative success rates—as well as their athletic workout measurements—to those of established NFL players, we can see what a good role for a player will be at the next level.

This can help guide a team’s decision, especially when they’re trying to figure out if a slower player should be used like Allen Robinson or like Jarvis Landry or if a smaller, faster player is as one-dimensional as Mike Wallace or multitalented like Brandin Cooks. Slow players can draw comparisons to both Robinson and Landry while smaller fast players can draw comparisons to Cooks and Wallace, despite all four receivers being completely different.

While the math behind the process can be a bit hairy, the end result is pretty easy to understand—a SimScore rating from 0 to 100 that compares a college receiver’s college performance profile to players in the NFL whose performance profile was similar—again, not that they played at the same level, but that their strengths and weaknesses were the same.

SimScores above 90 indicate good hits, while those below 90 tend to be more noise than anything.

So, let’s look at the players in the class that Harmon has data for and see if the players that match are the kind of player that will help the Vikings. These aren’t talent comps—some players on the comp spectrum are bad—just stylistic ones.

Of note, Harmon doesn’t have data on all NFL wide receivers, only 27. That means there will not be comps for some players and there will be repeats. A lot of Vikings fans want the next “Alshon Jeffery” or “Brandon Marshall,” but unfortunately, those players were not in the dataset. Still, this should be an immensely useful tool for visualizing players.

Laquon Treadwell, Ole Miss

Comps: Dez Bryant (SimScore: 92.7), Mike Evans (SimScore: 92.5), and Dwayne Bowe (SimScore: 91.0)

These showcase players who succeed on almost every route with an equal success rate, succeed against multiple types of coverage with equal success (zone, man, double coverage, and press coverage). These players tend to win contested catches. Despite that, they also have a somewhat high drop rate. They are all relatively slower players, though Treadwell doesn’t match their explosion metrics (though he didn’t run at the combine, we have a 40-yard dash estimate from—none of the 27 receivers are his height and weight and with similar jump scores.

Treadwell compares most strongly to Dez Bryant and in particular, both have the same physical profile with height, weight and 40 times, as well as success rates on hook routes, nine routes and post routes.

For the most part, this presents the picture of a versatile player who can play a variety of roles, but succeeds most in a possession role, winning contested catches.

Corey Coleman, Baylor

Comps: None.

The closest comparison to Coleman is Percy Harvin (84.8), and that comes in part because both had incredible workout numbers (Coleman, who did not do some workouts at the NFL combine, has his numbers from last year’s Baylor pro day, where he participated)—though Coleman put up better explosion numbers.

Coleman’s relative struggles against man coverage and double teams are well-represented by Percy Harvin, but Coleman succeeded much more often on deep routes and hooks, though was more likely to drop the ball than Harvin.

Almost as close as Harvin was Odell Beckham, who matched Coleman on each route more or less, but dropped the ball less often, and had worse strength and explosion scores.

Michael Thomas, Ohio State

Comps: None.

Michael Thomas had no true comparison, but he was closest to Stevie Johnson (88.4). This is again a good reminder that these comps are purely stylistic and not talent, but you can’t help but be a little deflated seeing that name. Johnson is a fantastic player, but perhaps not the most exciting or a true #1. Thomas as a player matches Johnson on hook routes like curls and comebacks and square routes, like digs and outs.

He does much better than Johnson in deep routes (Johnson struggles quite a bit at those) but overall carries a similar profile—his relative rates of success against man, zone, and press coverage are similar (they struggle—relatively—against man coverage), he wins contested catches as often, drops the ball as often, and has a similar build.

Josh Doctson, TCU

Comps: Odell Beckham (90.7)

Before getting too excited, SRVC comps to Odell Beckham can be common because Odell is talented at a wide variety of routes and tends to succeed in most of them. Doctson’s comparison to Beckham reflects an even distribution of route success—winning nearly every route to the same degree, like Beckham.

Doctson in fact places in the top seven of every single individual route score, getting open more often on every single route than the vast majority of his college peers, and as a result has an even score throughout the process (with the second best SRVC overall, behind Sterling Shepard).

Doctson was slightly better on contested catches (winning 85 percent to lead his peers) and slightly worse with drops (dropping a still-low 3.4 percent of his catchable passes) but for the most part matches Beckham in this category.

At first glance, he doesn’t seem to compare well to Beckham, but despite being taller and heavier, tends to have the same distribution of weight and same relative scores in agility drills over speed drills (or, as Mayock would say, “quicker than fast”). They also have the same short-area burst scores, with very similar 10-yard splits.

Other Comps

Harmon has 22 NCAA receivers in his list, and instead of going over the strengths, weaknesses and meaning of each comp, it may simply be more efficient to list them all.

  • Will Fuller: Mike Wallace (91.1)
  • Braxton Miller: None
  • Tyler Boyd: None (Close—Stevie Johnson at 88.9)
  • Pharoh Cooper: None
  • Rashard Higgins: None
  • Sterling Shepard: Randall Cobb (96.8)
  • Leonte Carroo: None (Close—Allen Robinson at 82.9)
  • Kenny Lawler: Stevie Johnson (94.0), Michael Crabtree (90.8)
  • De’Runnya Wilson: None
  • Michael Thomas, Southern Miss:  None (Close—Marqise Lee at 84.1)
  • Keyarris Garrett: None (Close—Donte Moncrief at 84.1)
  • Aaron Burbridge: None
  • Roger Lewis: None (Close—Stevie Johnson at 84.8)
  • Malcolm Mitchell: Randall Cobb (99.9)
  • Tajae Sharpe: None (Close—Antonio Brown at 87.3)
  • Charone Peake: None (Close—Dwayne Bowe at 82.5)
  • DeMarcus Robinson: None
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