WR vs. CB Matchups & Advice: Week 13 (2022 Fantasy Football)

WR vs. CB Matchups & Advice: Week 13 (2022 Fantasy Football)

Another crazy week in the NFL, and here we go at another attempt trying to predict the NEARLY unpredictable. Weekly readers will likely jump straight to the charts/our picks, but if you are new to this piece (as we have picked up some steam of late), here is what you are looking at: leveraging advanced data, our models hone in on player matchups AND defensive tendencies to predict what WRs are more or less likely to “boom” in a given week. The models take into account everything from height, speed advanced grading and yards per route run, and compare that to the (weighted, expected) corresponding defensive player’s respective data. We mesh that with some defensive tendencies we expect to see, along with how that WR has performed given those splits, and find players to start/sit for the given week. We’ve been on fire lately and hope we help you towards a winning week.

Week 12 Results:

Another week, another winning board. We are especially proud of the call on Jauan Jennings, and have a couple similar plays for this week!

NameFP SelectionProjectedActualNet
Jauan JenningsSTART6.417.0+10.6
Noah BrownSTART7.46.2+1.2
Tyler BoydSTART13.23.6-9.6
*Darnell MooneySIT10.40.0*-10.4
Tyreek HilSIT22.115.0-7.1

*Got hurt, not super fair…

Season Scored Card

Season Record: 45-28

*All stats based on Yahoo Fantasy Football ½ PPR

Week 13 WR vs CB Model Scorecard

  Raw NumbersWeekly Rank 
 SnapsWt.ed Net pprr40 Adv.HT Adv.nPFFwted TotalWt.ed Net pprr40 Adv.HT Adv.nPFFwted TotalAvg. Rk.
Christian Watson40.518.20.085.664.05723212
Amon-Ra St. Brown38.316.50.003.0120.882926116
Mike Williams41.714.
Drake London56.
Stefon Diggs41.417.30.041.599.271349719
Julio Jones40.913.50.023.567.32623201120
Davante Adams39.916.40.001.919.3112940622
Mike Evans55.319.0-0.045.367.447641024
Chris Olave51.919.60.040.345.4212602124
Garrett Wilson45.516.40.11-0.395.994681825
Brandon Aiyuk39.713.
Tee Higgins39.814.90.002.863.41329293827
Amari Cooper42.
Tyreek Hill39.619.10.00-1.319.332977529
Josh Palmer41.512.80.003.643.43129183729
Keenan Allen41.314.8-0.064.746.2148191430
D.K. Metcalf39.714.70.092.85-0.5176306630
Chris Godwin40.614.70.002.4413.3186335430
Curtis Samuel39.511.00.130.2514.856362331
Justin Jefferson39.914.20.031.643.72321473431
CeeDee Lamb56.920.9-0.053.465.2179222532
Jaylen Waddle39.816.40.00-1.156.41029751332
Tyler Lockett39.314.80.07-1.275.01510762832
DeVante Parker38.
Terry McLaurin41.
Nico Collins38.812.
A.J. Brown41.714.6-0.012.316.11965361534
D.J. Chark Jr.
Randall Cobb39.912.70.00-1.0919.3332874234
Allen Lazard39.212.2-0.045.725.1387712736
Darius Slayton40.
Jakobi Meyers38.713.8-
Mack Hollins39.910.80.004.472.26129105038
Damiere Byrd40.514.60.00-1.915.32029812338
DeVonta Smith41.812.
Marquez Valdes-Scantling42.
Courtland Sutton39.711.4-0.023.476.04871211739
Isaiah McKenzie41.011.60.17-1.443.6441793540
Olamide Zaccheaus40.813.40.00-2.745.62829851940
Chase Claypool41.910.80.044.81-2.2621487841
Deebo Samuel37.
Nick Westbrook-Ikhine39.
Ja’Marr Chase39.614.80.00-0.821.61629715242
Nelson Agholor38.711.
Michael Pittman Jr.39.012.8-0.083.663.33083174143
George Pickens58.415.7-0.022.600.21267346344
Donovan Peoples-Jones42.
Equanimeous St. Brown40.
Gabriel Davis41.311.3-0.034.352.34974134846
Richie James Jr.
Brandin Cooks38.611.40.06-2.622.64711844547
Devin Duvernay41.810.30.03-0.373.36716673947
Isaiah Hodgins36.08.7-0.024.414.67968123047
Corey Davis37.311.10.002.15-0.55429396747
Kalif Raymond38.711.50.00-3.145.14629882647
Noah Brown41.810.00.003.69-2.17029167748
Zay Jones39.912.00.001.593.34062504048
JuJu Smith-Schuster42.412.5-0.062.762.73782314449
Marquise Goodwin39.312.50.14-2.74-0.9352867149
Trent Sherfield40.310.40.001.750.66529425949
Christian Kirk39.714.5-0.05-1.786.02178801649
Jauan Jennings39.611.3-0.034.45-0.15273116450
Marvin Jones Jr.39.810.30.001.690.36629446250
Parris Campbell38.912.00.00-0.18-0.54227656851
Rashid Shaheed35.611.60.00-0.750.44529706051
Justin Watson33.
Treylon Burks42.212.7-
Dante Pettis41.910.50.001.69-4.06429458155
Van Jefferson41.710.90.00-0.90-0.46029726557
Adam Thielen40.
Jahan Dotson50.313.4-0.01-0.96-0.62766737059
Alec Pierce42.310.9-0.042.67-1.35975327360
Keelan Cole39.
Brandon Powell18.96.10.00-5.902.58629904663
Demarcus Robinson41.711.0-0.110.951.15787565564
David Bell36.24.5-0.022.89-0.58969286964
Jarvis Landry38.912.6-0.35-1.351.23490785464
Robert Woods38.810.1-0.100.972.26985554965
James Proche41.
Elijah Moore37.69.60.00-2.78-1.87329877566
K.J. Osborn40.17.80.00-0.28-8.28329668867
Lance McCutcheon32.
Diontae Johnson41.511.3-0.09-2.470.95184835669
Tyler Boyd39.911.9-0.201.02-12.54389529069
Jerry Jeudy39.710.7-0.020.86-4.76370588569
K.J. Hamler39.88.00.00-3.31-2.18229897669
Quez Watkins41.
Chris Moore38.69.7-0.030.15-1.07172637270
Michael Gallup41.89.2-0.062.16-4.67680388470
Steven Sims25.95.80.00-2.04-3.28829828070

*Again thanks to our friends at PFF for the data
**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.


  • Snaps: estimated total drop back snaps a WR will play in the coming 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 the DB each 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)


For this week we included data points/a model to represent the target share CHANGES given specific circumstances (Blitz, Man) we expect the WR to see, given the opponent’s tendencies. As we’ve noted previously, how a QB operates, and who he targets can change drastically given the defensive scheme, so keeping the data below in consideration for setting your lineup on a weekly basis is key.

NOTE: For week 13, I spared you the players within +/-2% change, and the chart below ONLY displays players that have a (relatively) strong indication in either direction

A few notes on the data below:

  • ALL stats ignore game scripts of greater than a 16 point differential (either way), the goaline (and from 0-10 yardline) and 4th downs and quarters
  • All % next to a WR represent target SHARE given the circumstance (i.e. Nico Collins is seeing 24% of targets without a blitz, and 62% when blitzed)
  • The first 6 columns represent how the respective WR performs, along with a “bonus” that’s reflective of target share increasing with Blitz (vs. non-Blitz) and Man (vs. Zone), meaning a negative number is not “bad”, but more so that the WR’s target share gets a bump with no Blitz or Zone respectively
  • The middle columns represent the WR’s opponent’s tendencies, along with a (3rd and 5th row in middle section) a metric for how many percentage points above or below league average THAT defense sends blitz/runs Man
  • The last 3 columns give an aggregate of how the WR performs relative to coverage and blitz schemes to expect
WR DetailsWR Target Ownership Splits Opponent Blitz/No Blitz & Man/Zone Splits Net WR vs Opp expected value
PlayerTeamno blitz %BLITZ %Blitz Bonuszone %MAN %Man Bonus W12 OppBLITZ RateBR > AvgMAN RATEMR>Avg “Blitz” Bonus“Man” BonusTotal “bump”
Corey DavisJets20%33%13%30%0%-30% Vikings14%-9%12%-17% -1%5%4%
DK MetcalfSeahawks32%42%9%40%27%-13% Rams30%7%17%-11% 1%1%2%
Terry McLaurinCommanders50%53%3%41%50%9% Giants38%15%46%17% 0%2%2%
Deebo Samuel49ers7%32%25%20%3%-17% Dolphins35%11%34%5% 3%-1%2%
Mack HollinsRaiders26%19%-7%18%37%19% Chargers25%1%39%10% 0%2%2%
Randall CobbPackers22%1%-21%28%12%-16% Bears16%-7%27%-2% 1%0%2%
K.J. OsbornVikings15%2%-13%6%9%3% Jets11%-12%27%-2% 2%0%2%
Jerry JeudyBroncos23%39%17%16%37%21% Ravens22%-1%22%-7% 0%-1%-2%
Parris CampbellColts22%6%-16%22%7%-15% Cowboys26%3%39%10% 0%-2%-2%
Justin JeffersonVikings51%70%18%55%66%11% Jets11%-12%27%-2% -2%0%-2%
A.J. BrownEagles37%61%24%41%50%9% Titans10%-13%24%-4% -3%0%-3%
Garrett WilsonJets28%47%19%29%42%13% Vikings14%-9%12%-17% -2%-2%-4%
Curtis SamuelCommanders35%1%-34%35%14%-21% Giants38%15%46%17% -5%-4%-9%

*Thanks to our friends at Sports Info Solutions, and their SIS Database for the info!

WR Matchups to Target in Week 13

*For the matchup sections below, we refrain from “obvious recommendations” and/or players you are starting no matter what (and the opposite for players recommended to sit)

  • Corey Davis

Although it is a “supplemental” model, we’ve had a lot of success the last few weeks focusing on “coverage/blitz type splits”, and Davis has the strongest we’ve seen yet. In 2022, Davis basically does not get targeted when it’s man coverage. Yet, sees a 30% target share vs. zone, and the Vikings play zone 17% points (88%) more than the league average.

  • Darius Slayton

Astute readers have noticed our pattern in the last few weeks, and Slayton fits the mold: “multiple models” pointing towards the same likely strong game. Slayton has a 25% target share when the Giants face zone, but a 36% share vs. man coverage. His opponent, the Commanders, play Man coverage 34% of pass snaps (or 5% points MORE than the league average). Couple that with a strong matchup, mainly due to a speed advantage (19th best of the week), which is mostly weighted from the pass snaps he will see against Bobby McCain (4.51 40-yard dash vs 4.39 for 23% of pass snaps expected), you have a recipe for a “boom” week.

  • Marquez Valdes-Scantling

MVS has been a tough weekly play due to his inconsistency. However, we think we may have a few predictive variables that indicate the likelihood of a solid outing from the veteran WR. Valdes-Scantling is another of these “anomalies” we’ve seen that essentially do NOT get targeted when an opponent blitzes (31% target share vs 0%, seriously). Given that the Bengals blitz only 17% of the time (6% points less than the league average) our supplemental model likes MVS this week. His 76 inches of height also gives him more than 3 inches on both likely cover men Eli Apple (73 inches 36% snaps) and Mike Hilton (69 inches, 35% snaps).

WR Matchups to Avoid in Week 13

  • Jerry Jeudy

Jeudy has a legitimate Man vs Zone target share split: 37% vs. Man and 16% vs Zone. This does not fare well for the young WR, as the Raiders run man 7% points less Man coverage than the league average. He also comes in with our base model’s 6th worst projection for the week. This mostly has to do with a league-low, net-weighted PFF grade based (mostly) on the 74% of expected coverage snaps he will see against 78.6 PFF-graded Malon Humphrey (vs. his own 69.2).

  • AJ Brown

I will be the first to admit, it’s very tough to put Brown on ANY “do not start list” in 2022, yet our models do not like him in week 13. Yes, his overall matchup grade this week is “not terrible” (27th), BUT that is the lowest we have seen from the young star this year (mainly due to the speed the titans have to match/beat his own 4.5 40). Additionally, we’ve noticed although Brown demands a healthy target share regardless when defenses play “vanilla” (zone/no blitz) his rates dip a bit comparatively. Take his target share vs the blitz alone: he’s demanding a whopping 61% (vs 37% when NOT blitzed) when the defense brings 5+. Yet, his opponent, AND FORMER team (looking for some revenge) blitz only 10% of the time (and plays 4% points less man than the league average), meaning the chance for an explosive play (or two) out of Brown is less likely than usual.

We all know how critical these last couple weeks are for those fighting for playoff spots for their respective fantasy leagues, and hope this data helps bring you home a “W”!

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