I must share a major disclaimer before jumping into to this: Pass Coverage performance as a whole, regardless of what metric you use, is by far the most volatile of datapoints. Hence, ANY model built to predict outcomes based on coverage ability (using the past to predict the future) must be taken with a hefty appetite for variance. This is said in the most macro of senses. That is, compared to other advanced statistics coverage data tend to be more elastic. As Pro Football Focus said in a relevant study in 2019:

“Coverage players who grade well one year are more likely to grade well the next year than those who do not, but the uncertainty is substantial. Second, and likely more importantly, your defensive success is largely a function of the offenses (and, more specifically, the quarterbacks) your team faces – something it has little control over even when sound personnel and scheme decisions are made.”

Essentially, how well a player is at covering a receiver is NOT as predictive game-over-game or even season-over-season, as other player performance metrics like pass pressure rate, broken/missed tackle rate, etc. Not to mention, figuring out WHO will cover a player, and how often is riddled with subjectivity. With that said, despite the fact it seems I’m telling you to stop reading, there is still some insight to glean from a properly calibrated model, WHEN YOU CONSIDER IT AT THE MARGIN (i.e., think when you choose a starter and have a 50/50 decision between WRs). Each week, FantasyPros plans to unveil our WR vs CB piece, utilizing our friends at PFF’s database, while applying our own model to determine marginal advantages for WRs in a given week for fantasy football, leveraging what we know to be predictive triggers between players.

As mentioned, the toughest part in creating a model that predicts WR performance based on the opposing “cover man” is NOT knowing strength/weakness of the opponent, but frankly who the WR is most likely to line up against. Given the different coverage schemes, presnap motion and potential shadow situations, one particular WR may be covered by 5-8 different DB/LB/Ss in any given game. Hence, weighting the likely proportion of snaps vs. a particular defender is crucial. You will see this as a hallmark of the model throughout.

Without further ado, we present to you the 2021 WR vs CB Week 1 Matchup Model.

*Mind you, this is last year’s data, so the model should be given another “layer” of risk tolerance, yet will be MUCH more reliable once we have 2-4 weeks of FRESH data to input

Net Performance Advantage Model

*Thanks to our friends at PFF for the data

Legend

• Snaps: estimated total dropback snaps a WR will play in the upcoming matchup

• Wt.ed Net PPRR: “Weighted Net Fantasy Points/Route Run” Simply, this is the net value of a WR’s PPRR average vs the DB’s PPRR given up, weighted according to each DB a WR is expected to play.
  Example:

• Say Davante Adams averages 2.0 points/route run

• DB1 (Adams expected to face 50% of snaps) gives up 3.0 points/route run

• DB2 (Adams expected to face 30% of snaps) gives up 4.0 points/route run

• DB3 (Adams expected to face 20% of snaps) gives up 1.0 points/route run

This first model would predict Adams to produce 2.45 points/route run (Adams 2.0 vs. aggregate defenders expected to face averages weighted to 2.9 = 2.45)

• *40 Adv: “40 Yard Dash Advantage” (weighted difference between WR 40 time and DB’s expected to face)
• *HT Adv: “Height Advantage” (same as above, but with height)
• nPFFwted Total: “Net PFF weighted Total Advantage” Our core model, similar to the Wt.ed Net PPRR above compares the PFF grade between WR and likely DB, weighted by expected snaps he’ll see each respective DB

*Not all WRs and DBs have 40 times, and/or height measurements. When this occurs with ONE party, the model ignores the opposing player (i.e., you need a WR and DB with a 40 time for this datapoint to populate)

Matchups to Target

• Christian Kirk and Tyler Boyd seem interesting for long-shot, possibly DFS spot starts (however given Kirk’s new team, it’s a tad risky)
• There are a few blue-chip WRs with great matchups, notably Davante Adams and Cooper Kupp having great snap-weighted net PFF Grade advantages

Christian Kirk, the $72-million dollar man, opens up with one of our best matchups predicted for Week 1 WRs. The model predicts about 78% of his snaps to go against Benjamin St. Juste who had a 53.2 Defensive PFF Grade last season compared to Kirk’s 73 grade.

Tyler Boyd, the most forgotten fantasy-relevant WR on the Bengals, not only has one of our model’s top three predicted advantages over expected this week, but he “checks all the boxes.” Beyond having a 72.6 to 58.1 PFF Grade advantage over his likely foe (as both players spend over 90% of their snaps in the slot), Arthur Maulet, Boyd also boasts a speed advantage (4.58 40-yard dash vs. 4.62) AND has four inches on the slot corner.

Both Cooper Kupp and Davante Adams score extremely high on their net PFF values (big shocker there) but also score as good as any WR this week in the net yards per route run, weighted to their most likely cover man. This will be Taron Johnson for Kupp (estimate about 42 snaps) and about 35% of Bryce Callahan, who had a deceptively low grade last season coming off an injury.

Matchups to Avoid

• There are plenty of “bubble” guys who are likely best to wait until later in the year to take a flier on them, but if you are looking for deep sleepers, WRs Sammy Watkins and Robbie Anderson could be decent options to surprise.
• When considering the most fantasy-relevant WRs, we are fading Rashod Bateman, Marquise Brown and Courtland Sutton hard this week. This is particularly true for the “very sticky” metric outside of the model that shows both Sutton and Bateman failed to create separation on their routes over the course of 2021.

Deeper Dive

Although INDIVIDUAL DB performance may be tough to predict, one thing that’s much more consistent is what coverage a defense tends to use. Now, to be clear, if we are planning to use coverage tendencies to try to predict WR outcomes on a week-to-week basis, the granularity may actually be our enemy. Instead, what I have found to be a more predictive split is to look at the following four pass defenses, broken down into these segments:

1. Defense: Blitz, with zone coverage behind
2. Defense: NOT blitz with zone coverage behind
3. Defense: Blitz with man coverage behind
4. Defense: NOT blitz with man coverage behind

*And then, of course, our secondary model compares WRs’ relative performance in these respective circumstances (think: who’s the go-to WR when a QB is pressured).

*Our secondary model captures a WR’s respective performance in these situations (from 2021) and how often a defense played said coverage. We take the Zone vs Man / Blitz vs. NOT Blitz Grid, apply it to the expected distribution of opposing defenses deployed and attempt to glean some additional insight on performance.

Here is how the model works out, INCLUDING the first PFF net grade model to the left. Note that the 6 columns on the right are self-explanatory, with the respective numbers representing the percentage target share increase (vs. average) given the situation. The column furthest to the right simply aggregates the TWO models together:

*Similar to the first chart, any rookie will not be included in this graph, and/or any player with no snaps in a given metric will result in a ____

**Thanks to our friends at Sports Info Solutions for the data

The additions to the table are based exclusively on net target share per circumstance (vs. average). For example, the “5.6” for Amari Cooper under the “Man NO Blitz” column represents how much more of a target share Cooper demands vs. his average (6% points) OVER the expected RANK of a defense deploying said (Man NO Blitz) coverage (most in the NFL).

In layman’s terms, the model expects Cooper to gain more targets than usual given the larger relative share he earns when a defense plays man without blitzing, and how often his opponent will deploy such a tactic.

*Again, we are making big leaps in assumptions here, especially when it comes to a WR that switched teams and/or an opposing defense with a new defensive coordinator (buyer beware during the first few weeks before the model populates with fresh data).

What this tells us:

• We still feel solid about Kirk, coming in first overall with the aggregate model. However, with a change of team, it’s tough to be sure.
• Tyler Boyd also remains high on the list given the lack of blitzing the Bengals are expected to see.
• Cooper Kupp drops a bit but is hoping to see his favorite (relative) combo: Man Blitz.
• Although the first model did NOT like Rashod Bateman, the second likes the amount of Zone NO Blitz he will likely see.
• We absolutely should reconsider our fade on Hollywood Brown, as although he has a negative PFF matchup, he’s likely to see a large dose of Zone NO Blitz, which he generated almost a 5% higher target share given that setup (albeit with a different team).

Again, I should note that this secondary model should be used as a way to “confirm our priors” based on the original, more predictive model based on net PFF grades.

All in all, based on our models, if you want the easier answer:

Start

• Christian Kirk
• Keenan Allen
• Amari Cooper
• Tyler Boyd

Sit

• CeeDee Lamb
• Rashod Bateman
• Robbie Anderson
• JuJu Smith-Schuster
• Chase Claypool

*Mind you, especially for early weeks, if a WR is facing a new defensive coordinator, you can throw these numbers out the window. Additionally, this model does NOT consider “coverage elasticity.” or how stringent a defensive coordinator’s DECISION to deploy XYZ coverage is.

I hope this data helps you build a winning lineup. Our models will only improve throughout the season, but we hope to see you back next week!