Which Draft-Hopeful Wide Receivers Produced Like Studs in College?

By looking strictly at college production, which wide receivers look like they'll be strong fantasy football assets in the NFL?

Solving the NFL Draft is tough.

It's easy to sit on your couch and make fun of NFL teams when they miss on a draft selection, but figuring out which players will effectively translate from college to the pros is no easy task. We're dealing with humans here, after all.

This same idea trickles down into fantasy football. As fantasy managers, we'll never get things totally right. It's impossible to always be right. We just have to be more right than our leaguemates.

In order to do that, the way we think has to be smarter, deeper, and more innovative. Depending on the league you're in, sometimes that just means straight up caring about fantasy football the most. But sometimes it means totally nerding out and creating a prospect model that helps predict the future fantasy success of incoming rookie wide receivers.

I did that last thing -- I created a model that looks at a handful of factors and helps forecast how a wide receiver will do across his first three years in the league.

The purpose in doing so was to beat draft capital. It was to predict immediate fantasy performance better than the slot a player gets drafted in. And it does that almost twice as well.

To be clear, the model still needs to know where a player gets drafted. Because it matters. Earlier picks, for example, see more immediate opportunity, and volume is what we're looking for in fantasy football. Not only that, but utilizing draft capital adds an inherent team talent evaluation to things, as opposed to just looking at factors like a player's size and college production.

That shouldn't be a huge deal, though, because the majority of fantasy footballers are drafting after the NFL Draft occurs. That's why making draft capital a part of the equation made sense.

The Model

Let's get to the damn point. The model looks at three major statistical categories for wide receivers to go along with things like size and breakout age. Those three categories are final-season receptions per game, final-season receiving yards per team attempt, and final-season touchdown share.

Since the model's using historical player information to help find good prospects, that means the majority of top wide receivers in the NFL -- the top wide receivers in fantasy football -- were also productive when they were in college within these categories. Generally speaking, of course.

What does that mean for the potential success among wideouts in this year's class then?

The Process

The goal here is to look at a subset of successful NFL wide receivers, see how they performed within our three main statistical categories in college, and use that information to spot gems in this year's group. The problem is, how exactly do we define who's in that subset? What the hell is a "successful wide receiver"?

I figured keeping things simple would be the best approach with this question, so we'll be looking at wide receivers who've had multiple top-20 fantasy campaigns in PPR leagues since 2011. Taking this a step further, we'll be looking at players who've hit that mark while also playing Division I ball. And more recent guys are included -- Larry Fitzgerald does us little good because he played college football in the 1970's.

Doing all this unscientific work gives us a sample of 34 dudes.

Here's how that sample did, on average, in the three categories we're looking at today. (Note: if a player missed significant time during their final season, their next relevant season was used.)

Category NFL Studs
Receptions Per Game 6.56
Yards Per Team Attempt 2.97
Touchdown Share 41.36%

This may not mean a whole lot to you, so below is a side by side comparison of the information versus the average of all wideouts who were invited to the NFL combine this year.

Category NFL Studs 2020 Class
Receptions Per Game 6.56 4.68
Yards Per Team Attempt 2.97 1.93
Touchdown Share 41.36% 27.05%

Unsurprisingly, the NFL Studs category -- the players who have had multiple top-20 seasons over the last decade -- were far more productive in college.

Which players from this year's class came close?

The Results

Receptions Per Game

Of the 55 wide receivers who were invited to the combine, a solid 46 of them failed to reach the average receptions per game mark from our NFL Studs dataset. And 20 of them didn't hit the four receptions per game mark, which is well below the 6.56 NFL Studs average.

Player College Receptions/Game
Tee Higgins Clemson 3.93
KJ Osborn Miami (FL) 3.85
Juwan Johnson Oregon 3.75
Chris Finke Notre Dame 3.73
Darnell Mooney Tulane 3.69
John Hightower Boise State 3.64
Jalen Reagor Texas Christian 3.58
Henry Ruggs III Alabama 3.33
Kendrick Rogers Texas A&M 3.33
Freddie Swain Florida 3.17
Jeff Thomas Miami (FL) 3.10
Donovan Peoples-Jones Michigan 3.09
Binjimen Victor Ohio State 2.92
Stephen Guidry Mississippi State 2.73
Antonio Gibson Memphis 2.71
Austin Mack Ohio State 2.70
Marquez Callaway Tennessee 2.31
Lynn Bowden Kentucky 2.31
Tyrie Cleveland Florida 2.08
Malcolm Perry Navy 0.00

Two names really jump out on this list: Jalen Reagor and Henry Ruggs.

Let's start with Reagor. There's plenty to like about his profile -- he's got an elite breakout age, he left school as a junior, and he was a punt returner. Those are all things that matter. But everything that I've looked at to help the model's predictiveness shows that final-season numbers are most important. And Reagor, with fewer than four receptions per game, lacked production in this category during his final season.

Some will point out that Texas Christian's offense was abysmal this season, and that's a key reason Reagor's receptions per game rate was so low. Not only that, but they had a pretty run-heavy offense, ranking in the bottom half of college football in pass-to-rush attempt ratio.

That's all valid, but the bigger concern is that even within the context of his team -- even when you look at yards per team pass attempt, which captures market share -- he wasn't spectacular. In order for him to pop in the model, he'll need to have decent draft capital. That's the case for a lot of players, but it's especially true when they've got below-average marks in two of the three statistical categories.

With Ruggs, there's a competition issue. Jerry Jeudy also played with him at Alabama, and Jeudy's at the top of a lot of draft boards at wide receiver. There was also Devonta Smith, who decided to go back to Bama for his senior season.

Competition matters to some degree, but it shouldn't be overstated. It does help form a narrative as to why Ruggs' production isn't everything but, the fact is, good players see good numbers. If they're good, they'll get the ball, no matter the competition. We saw that with AJ Brown and DK Metcalf (when he was healthy) during their final seasons last year.

From a modeling perspective, Ruggs' draft capital will be even more meaningful than Reagor's, since Reagor has historical production (a strong breakout age) while also being a returner. Ruggs is someone I'll likely be lower on than the consensus entering the draft, even if he shows off his blazing speed at the combine. It wouldn't surprise me if he ends up being a better real football player than a fantasy one.

Yards Per Team Attempt

The model favors yards per team attempt most of the three metrics. Receptions per game can be skewed a bit by how an offense is run, but it does make some sense that it works in conjunction with yards per team attempt, because the latter does seem to favor big-play threats. If a big-play receiver is also averaging a lot of receptions per game, then he's probably pretty good. That, at least, is the logic I've used to explain why the model seemed to care about receptions per game.

When looking at the 55 combine invites, just 5 were able to reach the NFL Stud sample's final-season yards per team attempt average. There were 19 who couldn't get to 1.50 yards per team attempt, a number far off from the NFL Stud's average of 2.97.

Player College Yards Per Team Attempt
Binjimen Victor Ohio State 1.41
Van Jefferson Florida 1.41
Quartney Davis Texas A&M 1.36
Lynn Bowden Kentucky 1.35
Kalija Lipscomb Vanderbilt 1.34
Joe Reed Virginia 1.31
KJ Osborn Miami (FL) 1.25
Stephen Guidry Mississippi State 1.23
Lawrence Cager Georgia 1.15
Freddie Swain Florida 1.11
Chris Finke Notre Dame 1.10
Dezmon Patmon Washington State 1.07
Donovan Peoples-Jones Michigan 1.07
Juwan Johnson Oregon 1.05
Austin Mack Ohio State 0.89
Jeff Thomas Miami (FL) 0.87
Kendrick Rogers Texas A&M 0.78
Tyrie Cleveland Florida 0.75
Malcolm Perry Navy 0.00

For the record, you'll keep seeing Malcolm Perry at the bottom of these lists because Perry played quarterback at Navy. He was invited to the combine as a wide receiver.

Van Jefferson is liked by a lot of analysts, but he doesn't really rank well in the model. Not only did he play a full four years at the collegiate level, but he broke out late, too. And his 1.41 yards per team attempt this past season was pretty mediocre. In fact, among our NFL Studs subset, no player had that low of a mark. Jefferson's going to have to either have far better draft capital than expected, or he'll have to just be a plain old outlier if he ends up hitting for fantasy purposes at the next level.

Touchdown Share

Final-season touchdown share added a little bit of juice to the overall predictiveness of the model, but it's not close to the most important factor. Keep that in mind when you look at the statistic's list of underachievers, which includes combine invites who failed to reach a 20% share during their last season in college.

Player College Touchdown Share
KJ Osborn Miami (FL) 18.52%
Van Jefferson Florida 18.18%
Quartney Davis Texas A&M 18.18%
Dezmon Patmon Washington State 16.00%
Lawrence Cager Georgia 15.38%
Henry Ruggs III Alabama 14.29%
Binjimen Victor Ohio State 12.50%
Juwan Johnson Oregon 11.43%
Lynn Bowden Kentucky 11.11%
Jeff Thomas Miami (FL) 11.11%
Chris Finke Notre Dame 10.81%
Kendrick Rogers Texas A&M 9.09%
Austin Mack Ohio State 6.25%
Tyrie Cleveland Florida 3.03%
Malcolm Perry Navy 0.00%

There's no need to spend a lot of time on this, since the major players who were bad at scoring touchdowns during their final college campaign have already been talked about. There's additional variance to a statistic like this considering touchdowns can be fairly random, but it did help the model, so it's factored in.

The Studs

When using the NFL Stud sample's averages as filters on the dataset of this year's class, two names emerge.

PlayerCollegeRec/GameYards/Team AttemptTD Share
Omar BaylessArkansas State7.153.5245.95%
Tyler JohnsonMinnesota6.624.0841.94%

Your first thought after seeing this table might be, "Who?"

We're not staring at CeeDee Lamb or Jerry Jeudy. Instead, it's Omar Bayless and Tyler Johnson.

Since the two players met the final-season criteria listed above, it doesn't mean they're bust-proof. At all. Bayless has plenty of red flags: he didn't do a ton until his final season, he played in a bad conference, and he's not expected to have draft capital to increase his rate of hitting at the NFL level. It wouldn't make sense to make him a priority.

Tyler Johnson, however, is crazy interesting. The downside to Johnson's profile is that he didn't declare early for the NFL Draft, and he could go later in the draft than we -- than I -- would like. But his numbers are great. They're elite. When looking strictly at his statistical production score, Johnson ranks in the 98th percentile among wide receivers who've been invited the combine since 2006.

In the end, Johnson likely won't be the best wide receiver in the model this year. He may not even end up in the top-five depending on where he's drafted. Like I said, there are more factors to the calculation than the three statistics that were laid out above. CeeDee Lamb, a projected first-rounder, actually has the third-best stat profile in the class. Jeudy, who was mentioned earlier, ranks in the top-10. When teams back them with early-round capital, they're likely going to look great in the model.

Until then, we can only speculate.