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.

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: I will refrain from cheery picking results. What you see is what you get, and below is how we did last week.

These values are determined using rankings from our analysts. Values will be different league-to-league, so the goal of the trade value chart is to provide you with opportunities to buy low and sell high throughout the season based on what we view as the actual value of players versus the perceived value of your leaguemates. This is based on half-PPR scoring.

Week 1 Results

Our model did extremely well to start off the season, but it 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: 

*All stats based on Yahoo Fantasy Football ½ PPR

The model went 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 ado, 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 into 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 or 4)
3. To standardize all variables we are tracking (and to 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

*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 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 snaps each DB and 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

• D.J. Chark (WR – DET)

Chark comes in as our model’s 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 it’s 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 (WR – PIT)

Claypool is an interesting play, coming in as the aggregate model’s second 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) in 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 (WR – CIN)

Higgins, after scoring less than 33% of what we had expected in Week 1 due largely to sustaining a concussion (4.7 points on a 15.2 expectation) is in a great spot (somewhat anecdotally) to bounce back, provided he clears the protocol. He comes in with the model’s third highest-rated matchup advantage, but with no single variable really jumping out. Frankly, in my experience,(I 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 who are at the top of some metric, but ones who 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 (WR – ATL)
• Stefon Diggs (WR – BUF)

WR Matchups to Avoid in Week 2

• Deebo Samuel (WR – SF)

Admittedly, Deebo kind of breaks all WR models given his outlier volume of RB touches. Hence, take this with a grain of salt. If you have him on a season-long team, I probably wouldn’t 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 overmatch his counterparts.  Taken along with the PFF grade mismatch (albeit one with a SUPER small sample after one week), this is a week to fade Samuel.

• DeVonta Smith (WR – PHI)

Smith has already ceded the WR1 role to newcomer AJ Brown, but we see an additional reason to fade him this week. This is mainly due to a physical mismatch in height vs. likely cover man Cameron Dantzler.

• Josh Palmer (WR – LAC)

Palmer burst on the scene last week and has a decent speed advantage vs. his likely opponents, but he still comes in with 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 nets, 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 who seek out this article (and made it this far down) are 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. However, in the aggregate, sports bettors are pretty sharp in predicting the future. 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 an RB, you want a team that is likely to be winning most of the game (that’s when you run). When picking a WR or TE, 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:

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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