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!

Name	FP Selection	Projected	Actual	Net
Jauan Jennings	START	6.4	17.0	+10.6
Noah Brown	START	7.4	6.2	+1.2
Tyler Boyd	START	13.2	3.6	-9.6
*Darnell Mooney	SIT	10.4	0.0	*-10.4
Tyreek Hil	SIT	22.1	15.0	-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 Numbers	Weekly Rank
	Snaps	Wt.ed Net pprr	40 Adv.	HT Adv.	nPFFwted Total	Wt.ed Net pprr	40 Adv.	HT Adv.	nPFFwted Total	Avg. Rk.
Christian Watson	40.5	18.2	0.08	5.66	4.0	5	7	2	32	12
Amon-Ra St. Brown	38.3	16.5	0.00	3.01	20.8	8	29	26	1	16
Mike Williams	41.7	14.3	0.00	5.02	7.1	22	29	5	12	17
Drake London	56.1	17.7	0.00	4.87	3.7	6	29	7	33	19
Stefon Diggs	41.4	17.3	0.04	1.59	9.2	7	13	49	7	19
Julio Jones	40.9	13.5	0.02	3.56	7.3	26	23	20	11	20
Davante Adams	39.9	16.4	0.00	1.91	9.3	11	29	40	6	22
Mike Evans	55.3	19.0	-0.04	5.36	7.4	4	76	4	10	24
Chris Olave	51.9	19.6	0.04	0.34	5.4	2	12	60	21	24
Garrett Wilson	45.5	16.4	0.11	-0.39	5.9	9	4	68	18	25
Brandon Aiyuk	39.7	13.7	0.03	1.01	7.5	25	20	53	9	27
Tee Higgins	39.8	14.9	0.00	2.86	3.4	13	29	29	38	27
Amari Cooper	42.2	12.8	0.03	2.61	4.2	29	18	33	31	28
Tyreek Hill	39.6	19.1	0.00	-1.31	9.3	3	29	77	5	29
Josh Palmer	41.5	12.8	0.00	3.64	3.4	31	29	18	37	29
Keenan Allen	41.3	14.8	-0.06	4.74	6.2	14	81	9	14	30
D.K. Metcalf	39.7	14.7	0.09	2.85	-0.5	17	6	30	66	30
Chris Godwin	40.6	14.7	0.00	2.44	13.3	18	63	35	4	30
Curtis Samuel	39.5	11.0	0.13	0.25	14.8	56	3	62	3	31
Justin Jefferson	39.9	14.2	0.03	1.64	3.7	23	21	47	34	31
CeeDee Lamb	56.9	20.9	-0.05	3.46	5.2	1	79	22	25	32
Jaylen Waddle	39.8	16.4	0.00	-1.15	6.4	10	29	75	13	32
Tyler Lockett	39.3	14.8	0.07	-1.27	5.0	15	10	76	28	32
DeVante Parker	38.8	8.7	0.07	4.00	4.6	80	8	15	29	33
Terry McLaurin	41.0	12.5	0.03	0.27	5.6	36	17	61	20	34
Nico Collins	38.8	12.0	0.00	3.23	3.1	41	29	23	42	34
A.J. Brown	41.7	14.6	-0.01	2.31	6.1	19	65	36	15	34
D.J. Chark Jr.	39.0	11.1	0.07	4.03	0.4	53	9	14	61	34
Randall Cobb	39.9	12.7	0.00	-1.09	19.3	33	28	74	2	34
Allen Lazard	39.2	12.2	-0.04	5.72	5.1	38	77	1	27	36
Darius Slayton	40.1	11.0	0.03	1.61	5.3	55	19	48	24	37
Jakobi Meyers	38.7	13.8	-0.13	3.00	7.9	24	88	27	8	37
Mack Hollins	39.9	10.8	0.00	4.47	2.2	61	29	10	50	38
Damiere Byrd	40.5	14.6	0.00	-1.91	5.3	20	29	81	23	38
DeVonta Smith	41.8	12.1	0.00	1.17	3.5	39	29	51	36	39
Marquez Valdes-Scantling	42.2	9.6	0.01	4.99	1.6	74	24	6	51	39
Courtland Sutton	39.7	11.4	-0.02	3.47	6.0	48	71	21	17	39
Isaiah McKenzie	41.0	11.6	0.17	-1.44	3.6	44	1	79	35	40
Olamide Zaccheaus	40.8	13.4	0.00	-2.74	5.6	28	29	85	19	40
Chase Claypool	41.9	10.8	0.04	4.81	-2.2	62	14	8	78	41
Deebo Samuel	37.0	11.3	0.04	0.91	2.7	50	15	57	43	41
Nick Westbrook-Ikhine	39.2	10.1	0.00	3.06	2.4	68	29	24	47	42
Ja’Marr Chase	39.6	14.8	0.00	-0.82	1.6	16	29	71	52	42
Nelson Agholor	38.7	11.0	0.11	1.00	1.3	58	5	54	53	43
Michael Pittman Jr.	39.0	12.8	-0.08	3.66	3.3	30	83	17	41	43
George Pickens	58.4	15.7	-0.02	2.60	0.2	12	67	34	63	44
Donovan Peoples-Jones	42.2	8.9	0.00	3.62	0.7	78	26	19	57	45
Equanimeous St. Brown	40.8	9.2	0.01	5.45	-2.3	77	25	3	79	46
Gabriel Davis	41.3	11.3	-0.03	4.35	2.3	49	74	13	48	46
Richie James Jr.	40.0	9.7	0.02	-0.52	5.4	72	22	69	22	46
Brandin Cooks	38.6	11.4	0.06	-2.62	2.6	47	11	84	45	47
Devin Duvernay	41.8	10.3	0.03	-0.37	3.3	67	16	67	39	47
Isaiah Hodgins	36.0	8.7	-0.02	4.41	4.6	79	68	12	30	47
Corey Davis	37.3	11.1	0.00	2.15	-0.5	54	29	39	67	47
Kalif Raymond	38.7	11.5	0.00	-3.14	5.1	46	29	88	26	47
Noah Brown	41.8	10.0	0.00	3.69	-2.1	70	29	16	77	48
Zay Jones	39.9	12.0	0.00	1.59	3.3	40	62	50	40	48
JuJu Smith-Schuster	42.4	12.5	-0.06	2.76	2.7	37	82	31	44	49
Marquise Goodwin	39.3	12.5	0.14	-2.74	-0.9	35	2	86	71	49
Trent Sherfield	40.3	10.4	0.00	1.75	0.6	65	29	42	59	49
Christian Kirk	39.7	14.5	-0.05	-1.78	6.0	21	78	80	16	49
Jauan Jennings	39.6	11.3	-0.03	4.45	-0.1	52	73	11	64	50
Marvin Jones Jr.	39.8	10.3	0.00	1.69	0.3	66	29	44	62	50
Parris Campbell	38.9	12.0	0.00	-0.18	-0.5	42	27	65	68	51
Rashid Shaheed	35.6	11.6	0.00	-0.75	0.4	45	29	70	60	51
Justin Watson	33.4	8.1	0.00	3.03	-1.3	81	29	25	74	52
Treylon Burks	42.2	12.7	-0.11	2.16	0.7	32	86	37	58	53
Dante Pettis	41.9	10.5	0.00	1.69	-4.0	64	29	45	81	55
Van Jefferson	41.7	10.9	0.00	-0.90	-0.4	60	29	72	65	57
Adam Thielen	40.1	9.5	0.00	1.84	-4.4	75	29	41	82	57
Jahan Dotson	50.3	13.4	-0.01	-0.96	-0.6	27	66	73	70	59
Alec Pierce	42.3	10.9	-0.04	2.67	-1.3	59	75	32	73	60
Keelan Cole	39.9	6.7	0.00	1.68	-4.4	85	29	46	83	61
Brandon Powell	18.9	6.1	0.00	-5.90	2.5	86	29	90	46	63
Demarcus Robinson	41.7	11.0	-0.11	0.95	1.1	57	87	56	55	64
David Bell	36.2	4.5	-0.02	2.89	-0.5	89	69	28	69	64
Jarvis Landry	38.9	12.6	-0.35	-1.35	1.2	34	90	78	54	64
Robert Woods	38.8	10.1	-0.10	0.97	2.2	69	85	55	49	65
James Proche	41.9	6.0	0.00	0.60	-7.1	87	29	59	87	66
Elijah Moore	37.6	9.6	0.00	-2.78	-1.8	73	29	87	75	66
K.J. Osborn	40.1	7.8	0.00	-0.28	-8.2	83	29	66	88	67
Lance McCutcheon	32.2	3.7	0.00	0.00	-12.0	90	29	64	89	68
Diontae Johnson	41.5	11.3	-0.09	-2.47	0.9	51	84	83	56	69
Tyler Boyd	39.9	11.9	-0.20	1.02	-12.5	43	89	52	90	69
Jerry Jeudy	39.7	10.7	-0.02	0.86	-4.7	63	70	58	85	69
K.J. Hamler	39.8	8.0	0.00	-3.31	-2.1	82	29	89	76	69
Quez Watkins	41.3	7.0	0.00	1.75	-5.8	84	64	43	86	69
Chris Moore	38.6	9.7	-0.03	0.15	-1.0	71	72	63	72	70
Michael Gallup	41.8	9.2	-0.06	2.16	-4.6	76	80	38	84	70
Steven Sims	25.9	5.8	0.00	-2.04	-3.2	88	29	82	80	70

*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.

Legend

• 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. 

Example:

• 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)

SECONDARY / BONUS CHART:

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 Details	WR Target Ownership Splits		Opponent Blitz/No Blitz & Man/Zone Splits		Net WR vs Opp expected value
Player	Team	no blitz %	BLITZ %	Blitz Bonus	zone %	MAN %	Man Bonus		W12 Opp	BLITZ Rate	BR > Avg	MAN RATE	MR>Avg		“Blitz” Bonus	“Man” Bonus	Total “bump”
Corey Davis	Jets	20%	33%	13%	30%	0%	-30%		Vikings	14%	-9%	12%	-17%		-1%	5%	4%
DK Metcalf	Seahawks	32%	42%	9%	40%	27%	-13%		Rams	30%	7%	17%	-11%		1%	1%	2%
Terry McLaurin	Commanders	50%	53%	3%	41%	50%	9%		Giants	38%	15%	46%	17%		0%	2%	2%
Deebo Samuel	49ers	7%	32%	25%	20%	3%	-17%		Dolphins	35%	11%	34%	5%		3%	-1%	2%
Mack Hollins	Raiders	26%	19%	-7%	18%	37%	19%		Chargers	25%	1%	39%	10%		0%	2%	2%
Randall Cobb	Packers	22%	1%	-21%	28%	12%	-16%		Bears	16%	-7%	27%	-2%		1%	0%	2%
K.J. Osborn	Vikings	15%	2%	-13%	6%	9%	3%		Jets	11%	-12%	27%	-2%		2%	0%	2%
Jerry Jeudy	Broncos	23%	39%	17%	16%	37%	21%		Ravens	22%	-1%	22%	-7%		0%	-1%	-2%
Parris Campbell	Colts	22%	6%	-16%	22%	7%	-15%		Cowboys	26%	3%	39%	10%		0%	-2%	-2%
Justin Jefferson	Vikings	51%	70%	18%	55%	66%	11%		Jets	11%	-12%	27%	-2%		-2%	0%	-2%
A.J. Brown	Eagles	37%	61%	24%	41%	50%	9%		Titans	10%	-13%	24%	-4%		-3%	0%	-3%
Garrett Wilson	Jets	28%	47%	19%	29%	42%	13%		Vikings	14%	-9%	12%	-17%		-2%	-2%	-4%
Curtis Samuel	Commanders	35%	1%	-34%	35%	14%	-21%		Giants	38%	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”!