WR vs. CB Matchups & Advice: Week 2 (2022 Fantasy Football)

Although I made every attempt to deflate my own model’s efforts last week, hopefully, you tuned in. I’ve been the first to admit that “Coverage Predicting Models” are loosely causal at best. And yes, the model is built on last year’s data, but we managed a pretty solid week to kick off the series. I’ll save you from the rest of the self-deprecating preamble, and allow you to simply check on the previous week’s WR vs. CB Matchups & Advice: Week 1 (2022 Fantasy Football), and get right into week 2 action.

Also note, as part of my pledge to objectivity let’s first take a look at how our picks fared last week. I will maintain this “report card” throughout the season, regardless of outcome and vow to you: will refrain from cheery picking results. What you see is what you get, and below is how we did last week.

Week 1 Results:

Our model did extremely well to start off the season, but should only get better once we have enough data to start implementing current season data points. Again, with a small sample set, we have been left with using last season as a proxy with all the issues that come with that (lack of rookie WR/DBs, nonpredictive data year over year). Nonetheless, here is how the selected picks fared last week: 

NameFP SelectionProjectedActualNet
Christian KirkSTART13.017.7+4.7 (W)
Tyler BoydSTART11.813.3+1.5 (W)
Davante AdamsSTART17.130.1+13.0 (W)
Cooper KuppSTART20.531.8+11.3 (W)
Sammy WatkinsSIT11.74.8-7.1 (W)
Robbie AndersonSIT8.621.2+12.6 (L)
Rashod BatemanSIT13.113.9+.8 (L)
Marquise BrownSIT14.514.3-.2 (W)
Courtland SuttonSIT14.911.2-3.7 (W)

*All stats based on Yahoo Fantasy Football ½ PPR

7-2 on the week, with a both a win and a loss that were so minuscule, it may be fairer to deem our performance from last week as 6-1 (granted with a couple no brainers). Likely, once we get a couple weeks under our belt we will “self-reflect” based on total points above/below projection from our picks, and possibly work into WR1 vs WR2 type projections. Stay tuned for that. Without further adieu here is how this week shapes up with the model:

*Note, based on feedback we made a few changes to the table:

  1. We aggregated all data into one spot (likely so you can copy and paste in to your own spreadsheet, you rascals, you)
  2. We made an executive decision to STILL track our “coverage bonuses” (who a QB targets more/less when blitzed/not blitzed and when facing zone/man), but not display and/or add to the model until we can incorporate 2022 data (likely in week 3-4)
  3. To standardize all variables we are tracking (and make it easier to read) we included a RANK Display, respective of each data point to the right AND sorted by the average rank across variables.

Week 2 WR vs CB Scorecard

Raw NumbersWeekly Rank
SnapsWt.ed Net pprr40 Adv.HT Adv.nPFFwted TotalWt.ed Net pprr40 Adv.HT Adv.nPFFwted TotalAvg. Rk.
D.J. Chark Jr.
Chase Claypool69.415.20.115.913.037312817
Tee Higgins52.920.
Drake London51.620.90.004.763.0182952920
Devin Duvernay36.727.90.04-0.8514.261575325
Julio Jones44.
Ja’Marr Chase61.727.70.000.878.3729561126
Byron Pringle36.
Marquez Valdes-Scantling46.812.20.004.764.5502942327
Robbie Anderson57.525.20.003.720.61029175027
Jerry Jeudy51.420.70.002.612.91927323127
Stefon Diggs56.033.40.01-0.516.7225711629
Mike Evans55.224.7-
A.J. Brown55.823.10.00-0.589.9132872730
Justin Jefferson59.542.5-0.011.599.416849832
Michael Pittman Jr.61.128.2-0.063.854.4585142532
Cooper Kupp64.129.4-
Jaylen Waddle74.731.10.00-1.136.3329801833
Zay Jones47.513.50.011.763.74121462634
Christian Kirk51.221.3-0.011.0620.6156554134
Equanimeous St. Brown35.
Terry McLaurin58.015.90.14-0.191.9322663734
Tyreek Hill67.727.70.00-1.215.7829822135
Curtis Samuel36.612.90.20-0.042.5461633336
D.K. Metcalf53.514.30.003.68-1.53829185836
Corey Davis44.514.20.002.551.24029334336
Davante Adams58.926.7-0.030.3916.597559236
Rashod Bateman49.519.50.001.800.62329444936
Mecole Hardman50.919.50.00-0.302.92429683038
A.J. Green53.812.30.034.15-4.34817127638
Amon-Ra St. Brown49.918.50.00-1.266.42629831739
Jahan Dotson56.519.50.06-1.171.42512814140
Courtland Sutton58.515.2-0.023.622.03670203641
Marquise Brown84.521.00.00-1.732.11729863542
Marvin Jones Jr.56.613.10.001.700.74429474842
Cedrick Wilson40.710.6-0.013.772.85766153243
DeAndre Carter36.324.70.00-4.902.41129963443
Brandin Cooks53.616.40.02-1.271.63120843944
JuJu Smith-Schuster48.615.60.003.38-8.23529228944
Adam Thielen57.613.10.002.44-3.24529366744
Tyler Lockett56.914.30.00-1.443.43929852745
Nico Collins42.
Parris Campbell45.512.30.020.13-0.54718625546
Bryan Edwards46.910.60.003.46-4.25829217546
Garrett Wilson42.112.00.05-0.881.35213764246
DeVante Parker54.
George Pickens56.511.00.004.26-1.75663105947
Michael Thomas52.221.3-0.162.510.71494354748
Gabriel Davis35.413.4-0.042.540.84276344650
Noah Brown30.
Demarcus Robinson42.
Jarvis Landry52.020.2-0.33-0.086.02195642050
Josh Reynolds50.28.6-0.052.915.67080302251
Diontae Johnson59.720.6-0.11-0.897.02092771451
Mike Williams52.
Tyler Boyd58.517.8-0.053.74-6.02878168151
D.J. Moore56.411.40.01-0.64-0.15523735351
Mack Hollins31.12.9-0.022.9010.0957231651
Bennett Skowronek35.
Chris Moore27.86.4-0.022.984.48571272452
Jakobi Meyers58.215.7-
K.J. Osborn47.910.00.001.27-2.86129526552
Robert Woods81.212.10.01-0.12-3.35122656952
Nick Westbrook-Ikhine38.
Olamide Zaccheaus39.218.30.00-3.94-2.02729956253
Chris Olave50.78.90.08-0.38-4.0676697354
Kenny Golladay48.49.3-0.064.44-1.8668686055
Jauan Jennings44.09.7-0.370.7611.5639658556
Dennis Houston42.
Russell Gage49.
Allen Robinson II54.39.9-0.063.02-1.56282265757
Kyle Philips40.415.9-0.05-0.461.23381704457
Nelson Agholor28.68.70.10-0.78-6.1694748257
Quez Watkins47.
Donovan Peoples-Jones54.312.3-0.022.19-3.54969417158
Darnell Mooney79.88.40.07-0.99-5.0728787859
Amari Cooper54.
Marquise Goodwin29.77.10.00-2.631.88229913860
Randall Cobb38.38.30.05-1.91-2.97314896661
Elijah Moore40.010.20.00-3.85-2.46029946462
Sammy Watkins35.
Josh Palmer30.
Sterling Shepard46.821.2-0.03-1.06-6.61673798463
David Sills30.32.7-0.094.49-1.8968976163
Brandon Aiyuk76.711.8-0.070.21-0.25487615464
CeeDee Lamb55.38.9-0.052.93-6.86879288565
DeVonta Smith61.45.60.00-0.22-5.68729678066
Hunter Renfrow51.710.5-0.05-1.861.25977874567
Isaiah McKenzie24.97.70.07-3.39-8.5779939067
Allen Lazard47.15.1-0.065.26-23.4928429669
K.J. Hamler37.25.30.00-1.90-4.19129887471
Greg Dortch42.78.30.00-2.71-16.37429929372
Alec Pierce35.16.5-0.011.78-16.58467459473
Laviska Shenault Jr.46.47.3-0.103.28-21.28190249573
David Bell41.25.5-0.091.60-6.58988488377
Deebo Samuel56.25.6-0.06-1.93-5.18883907985

*Again thanks to our friends at PFF for the data


  • Snaps: estimated total dropback snaps a WR will play in the coming matchup
  • Wt.ed Net PPRR: “Weighted Net Fantawy 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 the each DB a WR is expected to play. 


  • Say Davante Adams averages 2.0 points/route run
    • DB1 (expected to face 50% of snaps) gives up 3.0 points/route run
    • DB2 (expected to face 30% of snaps) gives up 4.0 points/route run
    • DB3 (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 averages weighted to 2.9)

  • *40 Adv: “40 Yard Dash Advantage” (weighted difference between WR 40 time and DB’s)
  • *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, it 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 other (i.e. you need a WR and DB with a 40 time for this datapoint to populate)

WR Matchups to Target in Week 2

  • DJ Chark

Chark comes in as our models highest rated “play” this week. Remember the model weights toward conditions/environment are ideal for a high performance game. In other words, we are not saying to automatically start ALL players listed at the top of this chart, but to alter overall rankings you already had. For Chark, some of this high ranking has to do with a height advantage he will have across his likely cover men, but its mainly weighted based on his overall net PFF grade. The model calls for Chark to see Kendall Fuller for the majority of his coverage snaps, and is taking into account Fuller’s horrible (current) 32.6 PFF graded performance through one week.

  • Chase Claypool

Chase Claypool is an interesting play, coming in as the aggregate model’s 2nd highest rated “matchup advantage”. Contrary to Chark above, this ranking is almost entirely based on “physicals”. Not the least of which is Claypool’s height advantage across the board, but his legit speed advantage (4.42 40 yard dash) vs. roughly three of the coverage snaps he should see vs. Myles Bryant (4.62 40 with 45% expected coverage snaps) and Jalen Mills (4.61 27% expected snaps). Between the two, there is a baseline level mismatch that could be intriguing. 

  • Tee Higgins

Tee Higgins, after scoring less than 33% of what we had expected in week 1 (4.7 points on a 15.2 expectation) is in a great spot (somewhat anecdotally) to bounce back. He comes in as the model’s 3rd highest rated matchup advantage, but with no single variable really jumping out. Frankly, in my experience, (I’ll admit I have NO data to back this up) these are typically the best opportunities to pounce on. That is, don’t target the guys that are at the top of some metric, but ones that are consistently in the first quartile of ALL metrics you are considering (i.e. there are no holes in the “environment” that could cause him to have a BAD game).

Others to consider bumping up:

  • Drake London
  • Stefon Diggs

WR Matchups to Avoid in Week 2

  • Deebo Samuel

Addmittadely, Deebo kind of breaks all WR based models given his outlier volume of RB touches. Hence, take this with a grain of salt. And if you have him in a season long team, I wouldn’t automatically bench him, but for DFS guys, this may NOT be “his week”. This play is similar to Tee Higgins above, where Samuel literally “checks all the boxes” to sit. Between having a 40-yard dash disadvantage (mainly due to Tariq Wooeln’s 4.26 40-yard dash  vs Deebo’s 4.53) and NOT having a height advantage vs ANY likely cover man across the board there’s little to tell us he can physically out-physical his counterparts.  Take this with the PFF grade mismatch (albeit one with a SUPER small sample after over one week), this is a week to fade Samuel.

  • Devonta Smith

Smith has already ceded the WR#1 role to newcomer AJ Smith, but we see an additional reason to fade him this week. This is mainly due to a physical mismatch in height and likely cover man Cameron Dantzler

  • Josh Palmer

Palmer burst on the scene last week and has a decent speed advantage vs. his likely opponents, but still comes in at an aggregate poor grade within the framework of our model for this week. This is mainly due to the Yards per route run NET value between Palmer and the DB he’s most likely to see the most this week, L’Jarius Sneed. Between Palmer’s .2 and Sneed’s .7 respective YPRR net, the model doesn’t like the fact Palmer will see that MISMATCH for almost half of the game (47% of likely snaps).

Tie-Breaking Bonus

I would imagine most that seek out this article (and made it this far down) are those in a conundrum. And if you are I have a solution:

When in doubt, trust in the wisdom of the crowd. 

That is, no one person (or model for that matter) is “good” at predicting outcomes of a game between 22 men running into each other–no matter how much we tell ourselves they are. However, in the aggregate, sports bettors are pretty sharp in predicting the future. This means when in absolute doubt, lean on what the majority of people think. When picking a WR you want to chase opportunity (more than skill), and this can manifest itself through sportsbook lines. In other words, when picking a RB you want a team that is likely to be winning most of the game (that’s when you run). When picking a WR/TE or pass catcher you want a team that is likely to be losing most of the game (that’s when you pass). Hence, as a last resort to break any “ties” use the current sportsbook lines to glean some insight. At a quick glance, here is how I would tie-break any decisions for Week 2 (2022) in the NFL:

RB from teamsBrowns, Rams, 49ers, Broncos, Packers, BillsJets, Falcons, Seahawks, Texans, Bears, Titans
WR/TE from teamsJets, Falcons, Seahawks, Texans, Bears, TitansBrowns, Rams, 49ers, Broncos, Packers, Bills

*All teams above have > 7 points in spread currently.

Here’s to another winning week, check back in next week as we get closer to a proper “in-season-based” data model.

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