In the first wave of free agency, the Commanders invested heavily in patching numerous holes in the defensive roster. There were a few key additions on offense, but the team could still benefit from an infusion of youth and talent at all offensive skill positions.
Wide receiver is particularly thin, with Terry McLaurin hoping to bounce back from a disappointing, injury-plagued season at age 31. It is currently unclear who will start the season opposite McLaurin at WR2, with the depth pipeline posing
more questions than answers.
This article will take a look at WRs in the 2026 draft class through the lens of advanced productivity metrics. Productivity metrics measure receiving production per unit of playing time. They allow us to see who were the best players when they got opportunities, rather than just focusing on the players who got the most playing time. This approach has demonstrated the ability to uncover hidden gems lurking down the bottom of draft boards. For example, last year’s edition turned up rookie All-Pro Chimere Dike and Isaac TeSlaa, who was drafted by the Lions four rounds ahead of his consensus projection and flashed potential as a rookie.
This year’s results identified players projected to be taken throughout the draft who might make good additions to the Commanders. That includes multiple players with higher than expected productivity who are currently projected to be drafted on Day 3, where the Commanders currently have four draft picks, and many others who might be available as undrafted free agents.
This analysis made use of the Pro Football Focus FBS database. Searching the entire database often turns up players from small programs with eye-popping stats, who have managed to receive zero attention on the internet, usually for good reasons.
To narrow the focus to players who might have a chance to be drafted, or at least make a preseason roster as UDFAs, I limited consideration to the 672 players on the Mock Draft Database Consensus Draft Board. Consensus ranks are reasonably accurate predictors of where prospects will be drafted on the first two days of the draft. By the end of Round 4, however, the variance becomes huge. By Round 7, differences of around 100 places or more are likely meaningless. I would consider players with consensus ranks up to at least 350 to be potentially draftable, as a conservative estimate.
Yardage Productivity
Tie: Makai Lemon, USC/Skyler Bell U Conn
The most commonly used metric to rank receivers is total receiving yards. Like all total production stats, it suffers from being confounded by playing time. A WR with 1,000 receiving yards in a season might be better than one with 500 receiving yards, or he might have just played in twice as many games.
Receiving productivity metrics adjust for differences in playing time to better reflect differences in playing ability. The best available productivity metric for receivers is Yards Per Route Run (Y/RR) which provides the most granular correction for differences in numbers of opportunities.
The following table shows the top 20 consensus-ranked WRs in descending order of Y/RR, along with the traditional production totals:
USC’s Makai Lemon and U Conn’s Skyler Bell tied for first place with Ohio State’s Carnell Tate close behind.
It should be no surprise that over half of the most productive WRs have Day 1 or Day 2 consensus projections. What might be of greater interest to Commanders’ fans is that highly productive receivers are projected to be available in every round, right through Day 3 and after the draft.
Scoring Productivity
Cyrus Allen, Cincinnati
Scoring productivity was measured in the same way as yardage productivity. Touchdown Rate (TD%) is the percentage of routes run by a WR on which he found the endzone. It provides the most accurate indication of how likely a WR was to score every time he ran a route.
In this case, I used a percentage instead of a decimal to keep the numbers from getting tiny. I adjusted TD counts by routes run, rather than targets, to capture the full skillset which leads to a score. This includes getting open to draw a target, making the catch, and sometimes running it in. Adjusting by targets misses the crucial step of gaining separation, and consequently provides an incomplete measure of scoring productivity.
Here are the 20 Touchdown Rate leaders among consensus-ranked WR prospects:
The 2025 TD Rate leader is a relative unknown who Commanders’ fans might want to know more about. Cyrus Allen is a 5-11, 180 lb do-it-all WR. He played his first two college seasons at Louisiana Tech, then transferred to Texas A&M in 2024. He played his fourth season with the Cincinnati Bearcats, where he switched from outside to the slot and broke out with 661 receiving yards and 12 TDs.
Allen leads a pack which includes many of the big-name WRs in the draft class, as well as a number of players with Day 3 and undrafted projections.
Catching the Football
Carnell Tate, Ohio State
It seems strange to say it, but catching the football is an underrated skillset for WRs. Every year, players shoot up draft boards after running a fast 40 yard dash at the Combine. A lot of first round busts at WR are players with elite measurables who are deficient at fundamentals of playing the position. Conversely, late round WRs who excel at the position are often players with unremarkable athletic testing numbers who catch everything that’s thrown to them.
The most commonly cited metric, Reception Rate (Rec%), is not particularly useful for comparing players, because it is highly dependent on another quantity, Average Depth of Target (ADOT). In order to compare two players’ Reception Rates meaningfully, they have to have run a similar route trees. If they have very different ADOTs, it’s hard to say if one has a higher Reception Rate because he has better hands or because he ran his routes closer to the line of scrimmage.
To illustrate what I mean, the following graph plots Reception Rate as a function of ADOT for 223 draft-eligible NCAA WRs who logged more than 100 pass plays in 2025.
As you’d expect, there is a fairly strong relationship between Rec% and ADOT. The farther a QB has to throw the ball, the less likely he is to complete a pass. In this sample, the negative correlation between Rec% and ADOT accounts for around 41% of the variance (R2 = 0.408) in Rec% from player to player. That is a huge confounding factor. The remaining variance is due to differences in catching ability as well as other factors, such as QB accuracy. It seems reasonable to assume that catching ability is the biggest factor, but we have to remain mindful of QB effects as well.
Catch Rate Over Expectation
In order to start get a meaningful handle on catching ability, we have to factor out the huge effect of ADOT. To do that, I calculated Catch Rate Over Expectation (CROE), using a mathematical technique called linear regression. The regression line, shown here in blue, is often referred to as the trend line. It plots the central tendency (aka local average) of the sample of Rec% values as they vary linearly with ADOT. In statistical terms, the regression line plots the expected value of Rec% at any value of ADOT.
CROE is simply the vertical distance between a player’s point in the scatter plot and the regression line. It tells us how much better or worse a player is at catching the football than expected based on their ADOT. For example, Indiana’s Omar Cooper had a 75.8% Catch Rate, which was 9.1% higher than expected based on his ADOT of 9.7 yds.
Best Pass Catchers in the Draft Class
The 17 consensus ranked WRs with CROE > 7.25% (red line) are indicated by the gold circles in the plot. These are the best pass catchers who have achieved any attention from draft analysts.
Ohio State’s Carnell Tate leads the pack by a comfortable margin. Other top pass catchers with consensus first round rankings include Makai Lemon (USC), Denzel Boston (Washington) and Omar Cooper (Indiana). But WRs who excel at catching the football can be found throughout the draft and likely into the UDFA ranks.
Making Contested Catches
Carnell Tate, Ohio State
Catching the football is valuable. Being able to win contested balls from defenders elevates a receiver to the next level. WRs who excel at making contested catches provide extra value as chain movers and red zone targets.
The following table list the top 12 consensus-ranked contested catch artists in the draft class in descending order of Contested Catch Rate (CTC%; minimum 10 contested targets).
Carnell Tate further cements his status as the WR with the strongest hands in the draft class.
Tate is joined by six other sure handed WRs making repeat appearances from the previous list: Kevin Coleman Jr, Lewis Bond, Denzel Boston, Makai Lemon, C.J. Daniels, Malik Benson. The good news for the Commanders, if they decide to go another direction than Carnell Tate in Round 1 is that WRs who can win at the catch point can probably be found later on Day 2 (Skyler Bell, Ted Hurst) and well into Day 3 (Coleman, Josh Cameron, C.J. Daniels, Lewis Bond, Malik Benson). In fact, some are might still be available after the draft (e.g. C.J. Williams).
To avoid spurious small sample effects, I set a limit of 10 contested targets. That eliminated a few other high CROE WRs with high CTC%: Andrel Anthony (CTC 77.8%), Emmanuel Henderson Jr (CTC 75.0%), and Zachariah Branch (CTC 62.5%).
Best Deep Threat
Carnell Tate, Ohio State
Receivers who stretch the field vertically stress defenses by making explosive plays and creating opportunities for others, underneath. Certain WRs are deep ball specialists, while true X receivers are usually dangerous at all levels. This next category uncovers both kinds of players.
To find the best deep threats in the WR draft class, I searched PFF’s receiving depth dataset for players with the best productivity on targets over 20 yds. The following table shows consensus-ranked players with the best combination of productivity and production on deep balls, listed in order of Catch Rate (min 10 deep targets).
Carnell Tate tied for the lead with Malik Benson in Catch Rate and Passer Rating (P Rtg) on targets over 20 yds. But Tate clearly separates from the rest of the pack on balance of production and productivity stats on deep balls, including some I didn’t show in the table.
Speaking of which, Tate caught 6 of 7 contested targets, to give him a Contested Catch Rate of 85.7% on targets over 20 yds. To put that in context, referring back to the regression plot in the Catch Rate section, a WR with an ADOT of just 0.5 yds (draft class min. = 1.9 yds) would be expected to have a Catch Rate of 85.7% across all of his targets. Achieving that Catch Rate on passes with an ADOT of 36.2 yds alone would make Tate an outlier in the draft class. Doing it on contested targets at that ADOT is unworldly.
This ranking revealed deep threat WRs with consensus projections spread throughout the draft and thereafter. What may be of particular interest to Commanders’ fans is that 7 of the top 12 deep threats are projected to be late round picks or go undrafted.
Gaining Yards After the Catch
Kendrick Law, Kentucky
Receivers who excel at gaining yardage after the catch can exploit soft underneath coverage to turn short completions into large gains. Yards after the catch can also be an indicator of receivers who are good at generating separation.
Here are the top 21 consensus-ranked WR prospects:
This one is a bit more interesting than some of the previous metrics. Obviously, YAC/Rec is not the best primary indicator for top ranked WR prospects. The NCAA leader is not well known. And the top 21 list only contains 5 players with consensus ranks in the top 100.
A few well known players were particularly good at generating YAC, including Makai Lemon, K.C. Conepcion, Omar Cooper, Zachariah Branch and Skyler Bell.
What YAC/Rec seems to be good at finding are players who got relatively little to medium amounts of playing and were highly productive when they got opportunities. These players are good candidates to be sleepers in the WR draft class.
Some of the potential sleepers are projected to be Day 3 picks, including Eric McAllister, De’Zhaun Stribling, Aaron Anderson, Colbie Young, and Kendrick Law. While quite a few others could be available after the draft, including Hank Beatty, Emmanuel Henderson, Kobe Prentice, Joseph Manjack and Trebor Pena.
Best Dual Threat WR
Germie Bernard, Alabama
This draft class does not seem likely to produce the next Deebo Samuel. But there are plenty of WRs who can be effective running the ball. This may not be a glaring need with Dyami Brown (66.7% Career Rushing Success Rate) back in the Commanders’ line-up. But it might be an interesting wrinkle to some WRs who would be good pick-ups for their receiving ability.
Here I broke with my theme and listed players in order of 2025 rushing total, rather than productivity (Y/A). That is because WR runs were rare in 2025, and I wanted to avoid the stats being captured by players with a single long run, with one notable exception. Also, a foolish consistency is the hobgoblin of small minds.
The WR rushing yards leader was Georgia’s Dillon Bell. Bell is a powerful, explosive wideout who does his best work after he gest the gets the ball in his hands (5.2 YAC/Rec receiving, 3.47 YAC/att rushing). But he is not really much of a receiver.
The title of best dual threat receiver falls to the guy just behind him in the receiving totals ranking, Alabama’s Germie Bernard. Bernard’s rushing total supplemented his impressive receiving output of 862 yds and 7 TDs accumulated while sharing time with three other high volume targets in the Tide’s WR room.
As I alluded to earlier, rushing ability adds an extra facet to the games of a few WRs who are already high on draft boards, including K.C. Concepcion, Omar Cooper Jr, and Antonio Williams. Cooper’s appearance on the list is due to a single 75 yard house call against powerhouse Kennesaw State. Bernard, Concepcion, Williams, Kevin Coleman Jr. (2x Commanders’ meetings), Zavion Thomas and Trebor Pena have deeper resumes as rushers. Kaden Wetjen’s greatest value is in another phase of the game.
Special Teams Productivity
Kaden Wetjen, Iowa
To find the WR with the greatest value in the return game, I focussed on punt and kick return averages, while also being mindful of TDs and fumbled catches. I didn’t have to look very hard. There was a clear leader.
Iowa’s Kaden Wetjen (5-9, 193 lbs, 4.47 40, RAS 6.58) led the entire draft class in punt return average (min 8 total returns) and TDs, not just consensus-ranked players. He was second in kick return average among consensus-ranked WRs, and third among consensus-ranked players. He led the NCAA in return TDs.
Wetjen had relatively modest receiving output: 22 Rec, 158 yds, 1 TD, 1.46 Y/RR. But the Hawkeyes spread the ball around, and their leading receiver (TE DJ Vonnahme) only had 434 receiving yards. Wetjen is effective on shallow routes as a slot receiver and pitches and swing passes from the backfield. His small catch radius might set his ceiling as a receiver, but the sky is his limit as a return specialist.
The best kick returner in the WR draft class was LSU’s slim wideout, Barion Brown (5-11, 177 lbs, 4.40 sec 40).
Run Blocking
Denzel Boston, Washington
Run blocking is an important, and often overlooked, aspect of playing WR. Sadly, I haven’t found any truly satisfying run blocking metrics for football players in general, let alone college football players. So I made do with PFF Run Blocking Grades because they were available.
The highest graded consensus-ranked WR prospect was the Hokies’ Donavon Green, who ranked 670th out of 677 players on the consensus board. Greene’s blocking grades might be impressive, but his receiving stats are not.
As with the Dual Threat category, I am comfortable reaching one spot down the blocking grade ranking to award this one to Washington’s Denzel Boston (6-4, 212 lbs) who is a very impressive receiver (62 Rec, 8.34 CROE, 76.9% CTC, 881 yds, 11 TDs/3.05%, 2.44 Y/RR) and blocker, and no slouch on punt returns. I sincerely doubt that PFF blocking grades have the precision to separate Boston from Greene or any of the next couple of players in the ranking.
Best Receiver in Class
Carnell Tate, Ohio State
To identify the best overall receivers in the WR draft class, I tallied the number of top ten finishes in the six receiving categories. I also included run blocking, because that’s what WRs spend close to half their time doing. Ten WRs had three or more top 10 appearances:
No surprise that Carnell Tate takes first prize, walking away. Tate finished in the top 10 in six of seven categories, and led the class with three first place finishes (CROE, CTC%, Catch Rate over 20 yds).
There was a tie for second place between the second ranked WR in the class, Makai Lemon, and U Conn’s Skyler Bell, who currently carries a third round projection. The two second place finishers were tied for first place in Yardage Productivity (Y/RR), arguably the most important category.
Third place was where things got really interesting. Of the seven players with three top-10 finishes, only Denzel Boston and Omar Cooper have consensus first-round projections. Four of these players (C.J. Daniels, Lewis Bond, De’Zhaun Stribling, Malik Benson) are expected to be drafted in the fifth round or later, where the Commanders currently hold four selections. These players, along with several other WRs highlighted in this article, might be sleepers in the draft class and are worth watching for the Commanders on Day 3.
