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: 

Name	FP Selection	Projected	Actual	Net
Christian Kirk	START	13.0	17.7	+4.7 (W)
Tyler Boyd	START	11.8	13.3	+1.5 (W)
Davante Adams	START	17.1	30.1	+13.0 (W)
Cooper Kupp	START	20.5	31.8	+11.3 (W)
Sammy Watkins	SIT	11.7	4.8	-7.1 (W)
Robbie Anderson	SIT	8.6	21.2	+12.6 (L)
Rashod Bateman	SIT	13.1	13.9	+.8 (L)
Marquise Brown	SIT	14.5	14.3	-.2 (W)
Courtland Sutton	SIT	14.9	11.2	-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 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.
D.J. Chark Jr.	52.1	17.3	0.01	4.28	11.8	30	24	9	4	17
Chase Claypool	69.4	15.2	0.11	5.91	3.0	37	3	1	28	17
Tee Higgins	52.9	20.1	0.00	4.15	6.9	22	29	11	15	19
Drake London	51.6	20.9	0.00	4.76	3.0	18	29	5	29	20
Devin Duvernay	36.7	27.9	0.04	-0.85	14.2	6	15	75	3	25
Julio Jones	44.2	13.2	0.03	3.35	6.1	43	16	23	19	25
Ja’Marr Chase	61.7	27.7	0.00	0.87	8.3	7	29	56	11	26
Byron Pringle	36.1	17.3	0.07	0.83	7.3	29	7	57	12	26
Marquez Valdes-Scantling	46.8	12.2	0.00	4.76	4.5	50	29	4	23	27
Robbie Anderson	57.5	25.2	0.00	3.72	0.6	10	29	17	50	27
Jerry Jeudy	51.4	20.7	0.00	2.61	2.9	19	27	32	31	27
Stefon Diggs	56.0	33.4	0.01	-0.51	6.7	2	25	71	16	29
Mike Evans	55.2	24.7	-0.10	5.20	9.3	12	91	3	10	29
A.J. Brown	55.8	23.1	0.00	-0.58	9.9	13	28	72	7	30
Justin Jefferson	59.5	42.5	-0.01	1.59	9.4	1	68	49	8	32
Michael Pittman Jr.	61.1	28.2	-0.06	3.85	4.4	5	85	14	25	32
Cooper Kupp	64.1	29.4	-0.03	2.03	9.3	4	74	43	9	33
Jaylen Waddle	74.7	31.1	0.00	-1.13	6.3	3	29	80	18	33
Zay Jones	47.5	13.5	0.01	1.76	3.7	41	21	46	26	34
Christian Kirk	51.2	21.3	-0.01	1.06	20.6	15	65	54	1	34
Equanimeous St. Brown	35.2	11.9	0.01	4.53	0.0	53	26	6	52	34
Terry McLaurin	58.0	15.9	0.14	-0.19	1.9	32	2	66	37	34
Tyreek Hill	67.7	27.7	0.00	-1.21	5.7	8	29	82	21	35
Curtis Samuel	36.6	12.9	0.20	-0.04	2.5	46	1	63	33	36
D.K. Metcalf	53.5	14.3	0.00	3.68	-1.5	38	29	18	58	36
Corey Davis	44.5	14.2	0.00	2.55	1.2	40	29	33	43	36
Davante Adams	58.9	26.7	-0.03	0.39	16.5	9	75	59	2	36
Rashod Bateman	49.5	19.5	0.00	1.80	0.6	23	29	44	49	36
Mecole Hardman	50.9	19.5	0.00	-0.30	2.9	24	29	68	30	38
A.J. Green	53.8	12.3	0.03	4.15	-4.3	48	17	12	76	38
Amon-Ra St. Brown	49.9	18.5	0.00	-1.26	6.4	26	29	83	17	39
Jahan Dotson	56.5	19.5	0.06	-1.17	1.4	25	12	81	41	40
Courtland Sutton	58.5	15.2	-0.02	3.62	2.0	36	70	20	36	41
Marquise Brown	84.5	21.0	0.00	-1.73	2.1	17	29	86	35	42
Marvin Jones Jr.	56.6	13.1	0.00	1.70	0.7	44	29	47	48	42
Cedrick Wilson	40.7	10.6	-0.01	3.77	2.8	57	66	15	32	43
DeAndre Carter	36.3	24.7	0.00	-4.90	2.4	11	29	96	34	43
Brandin Cooks	53.6	16.4	0.02	-1.27	1.6	31	20	84	39	44
JuJu Smith-Schuster	48.6	15.6	0.00	3.38	-8.2	35	29	22	89	44
Adam Thielen	57.6	13.1	0.00	2.44	-3.2	45	29	36	67	44
Tyler Lockett	56.9	14.3	0.00	-1.44	3.4	39	29	85	27	45
Nico Collins	42.9	8.1	0.00	3.89	-2.2	76	29	13	63	45
Parris Campbell	45.5	12.3	0.02	0.13	-0.5	47	18	62	55	46
Bryan Edwards	46.9	10.6	0.00	3.46	-4.2	58	29	21	75	46
Garrett Wilson	42.1	12.0	0.05	-0.88	1.3	52	13	76	42	46
DeVante Parker	54.2	9.6	0.08	2.22	-4.3	65	5	39	77	47
George Pickens	56.5	11.0	0.00	4.26	-1.7	56	63	10	59	47
Michael Thomas	52.2	21.3	-0.16	2.51	0.7	14	94	35	47	48
Gabriel Davis	35.4	13.4	-0.04	2.54	0.8	42	76	34	46	50
Noah Brown	30.1	7.4	0.00	2.24	0.3	80	29	38	51	50
Demarcus Robinson	42.2	8.5	0.00	1.33	7.2	71	64	51	13	50
Jarvis Landry	52.0	20.2	-0.33	-0.08	6.0	21	95	64	20	50
Josh Reynolds	50.2	8.6	-0.05	2.91	5.6	70	80	30	22	51
Diontae Johnson	59.7	20.6	-0.11	-0.89	7.0	20	92	77	14	51
Mike Williams	52.2	9.7	0.00	3.63	-8.8	64	29	19	91	51
Tyler Boyd	58.5	17.8	-0.05	3.74	-6.0	28	78	16	81	51
D.J. Moore	56.4	11.4	0.01	-0.64	-0.1	55	23	73	53	51
Mack Hollins	31.1	2.9	-0.02	2.90	10.0	95	72	31	6	51
Bennett Skowronek	35.6	8.1	0.00	2.92	-3.7	75	29	29	72	51
Chris Moore	27.8	6.4	-0.02	2.98	4.4	85	71	27	24	52
Jakobi Meyers	58.2	15.7	-0.11	2.20	1.5	34	93	40	40	52
K.J. Osborn	47.9	10.0	0.00	1.27	-2.8	61	29	52	65	52
Robert Woods	81.2	12.1	0.01	-0.12	-3.3	51	22	65	69	52
Nick Westbrook-Ikhine	38.3	5.4	0.00	3.18	-3.2	90	29	25	68	53
Olamide Zaccheaus	39.2	18.3	0.00	-3.94	-2.0	27	29	95	62	53
Chris Olave	50.7	8.9	0.08	-0.38	-4.0	67	6	69	73	54
Kenny Golladay	48.4	9.3	-0.06	4.44	-1.8	66	86	8	60	55
Jauan Jennings	44.0	9.7	-0.37	0.76	11.5	63	96	58	5	56
Dennis Houston	42.0	6.8	0.00	2.09	-3.4	83	29	42	70	56
Russell Gage	49.2	7.6	0.00	0.27	-1.4	79	29	60	56	56
Allen Robinson II	54.3	9.9	-0.06	3.02	-1.5	62	82	26	57	57
Kyle Philips	40.4	15.9	-0.05	-0.46	1.2	33	81	70	44	57
Nelson Agholor	28.6	8.7	0.10	-0.78	-6.1	69	4	74	82	57
Quez Watkins	47.1	4.3	0.07	2.36	-7.8	94	10	37	88	57
Donovan Peoples-Jones	54.3	12.3	-0.02	2.19	-3.5	49	69	41	71	58
Darnell Mooney	79.8	8.4	0.07	-0.99	-5.0	72	8	78	78	59
Amari Cooper	54.9	7.6	0.02	0.99	-7.1	78	19	55	86	60
Marquise Goodwin	29.7	7.1	0.00	-2.63	1.8	82	29	91	38	60
Randall Cobb	38.3	8.3	0.05	-1.91	-2.9	73	14	89	66	61
Elijah Moore	40.0	10.2	0.00	-3.85	-2.4	60	29	94	64	62
Sammy Watkins	35.5	4.7	0.06	1.20	-11.4	93	11	53	92	62
Josh Palmer	30.4	5.9	0.00	1.34	-7.7	86	29	50	87	63
Sterling Shepard	46.8	21.2	-0.03	-1.06	-6.6	16	73	79	84	63
David Sills	30.3	2.7	-0.09	4.49	-1.8	96	89	7	61	63
Brandon Aiyuk	76.7	11.8	-0.07	0.21	-0.2	54	87	61	54	64
CeeDee Lamb	55.3	8.9	-0.05	2.93	-6.8	68	79	28	85	65
DeVonta Smith	61.4	5.6	0.00	-0.22	-5.6	87	29	67	80	66
Hunter Renfrow	51.7	10.5	-0.05	-1.86	1.2	59	77	87	45	67
Isaiah McKenzie	24.9	7.7	0.07	-3.39	-8.5	77	9	93	90	67
Allen Lazard	47.1	5.1	-0.06	5.26	-23.4	92	84	2	96	69
K.J. Hamler	37.2	5.3	0.00	-1.90	-4.1	91	29	88	74	71
Greg Dortch	42.7	8.3	0.00	-2.71	-16.3	74	29	92	93	72
Alec Pierce	35.1	6.5	-0.01	1.78	-16.5	84	67	45	94	73
Laviska Shenault Jr.	46.4	7.3	-0.10	3.28	-21.2	81	90	24	95	73
David Bell	41.2	5.5	-0.09	1.60	-6.5	89	88	48	83	77
Deebo Samuel	56.2	5.6	-0.06	-1.93	-5.1	88	83	90	79	85

*Again thanks to our friends at PFF for the data

Legend

• 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. 

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)

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:

	START	SIT
RB from teams	Browns, Rams, 49ers, Broncos, Packers, Bills	Jets, Falcons, Seahawks, Texans, Bears, Titans
WR/TE from teams	Jets, Falcons, Seahawks, Texans, Bears, Titans	Browns, 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.