We are closing in on the Fantasy Football playoffs among all the chaos, parity, and “changings of the guard” (yes, that’s the oddly correct plural) within the 2022 NFL Season. Unfortunately, the schedule makers did not consider us fantasy football managers, as we have six teams on a bye in Week 14. Our models below will try to quiet the noise, peel back the variance and help you select the optimal WRs in your lineup this week.
Week 13 Results
Our winning streak ended, but we find some consolation in a .500 weekend and doing relatively well (in magnitude/points) sans the AJ Brown call.
Season Scored Card
Season Record: 47-30
We are closing in on the Fantasy Football playoffs among all the chaos, parity, and “changings of the guard” (yes, that’s the oddly correct plural) within the 2022 NFL Season. Unfortunately, the schedule makers did not consider us fantasy football managers, as we have six teams on a bye in Week 14. Our models below will try to quiet the noise, peel back the variance and help you select the optimal WRs in your lineup this week.
Week 13 Results
Our winning streak ended, but we find some consolation in a .500 weekend and doing relatively well (in magnitude/points) sans the AJ Brown call.
Season Scored Card
Season Record: 47-30
*All stats based on Yahoo Fantasy Football 1/2 PPR
Week 14 WR vs. CB Model Scorecard
*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 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 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 data point 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 14, 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 goal line (and from 0-10 yard line) 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 the middle section) metric for how many percentage points above or below league average THAT defense sends blitz/runs Man.
- The last three 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 |
no blitz % |
BLITZ % |
Blitz Bonus |
zone % |
MAN % |
Man Bonus |
|
W14 Opp |
BLITZ Rate |
BR > Avg |
MAN RATE |
MR>Avg |
|
“Blitz” Bonus |
“Man” Bonus |
Total “bump” |
| A.J. Brown |
37% |
61% |
24% |
41% |
50% |
9% |
|
NYG |
38% |
15% |
46% |
17% |
|
4% |
1% |
5% |
| Nico Collins |
24% |
62% |
38% |
17% |
46% |
28% |
|
DAL |
26% |
3% |
39% |
10% |
|
1% |
3% |
4% |
| Deebo Samuel |
7% |
32% |
25% |
20% |
3% |
-17% |
|
TB |
29% |
6% |
19% |
-10% |
|
2% |
2% |
3% |
| Josh Reynolds |
41% |
11% |
-31% |
21% |
18% |
-2% |
|
MIN |
14% |
-9% |
12% |
-17% |
|
3% |
0% |
3% |
| DeVante Parker |
20% |
39% |
19% |
30% |
18% |
-12% |
|
ARI |
33% |
10% |
18% |
-11% |
|
2% |
1% |
3% |
| Christian Kirk |
44% |
28% |
-16% |
36% |
32% |
-4% |
|
TEN |
10% |
-13% |
24% |
-4% |
|
2% |
0% |
2% |
| Tyreek Hill |
44% |
77% |
32% |
47% |
66% |
18% |
|
LAC |
25% |
1% |
39% |
10% |
|
0% |
2% |
2% |
| Kalif Raymond |
27% |
25% |
-2% |
30% |
21% |
-9% |
|
MIN |
14% |
-9% |
12% |
-17% |
|
0% |
2% |
2% |
| Davante Adams |
50% |
72% |
22% |
49% |
48% |
-1% |
|
LAR |
30% |
7% |
17% |
-11% |
|
1% |
0% |
2% |
| Jaylen Waddle |
30% |
22% |
-9% |
31% |
13% |
-18% |
|
LAC |
25% |
1% |
39% |
10% |
|
0% |
-2% |
-2% |
| Brandon Aiyuk |
48% |
44% |
-4% |
40% |
57% |
17% |
|
TB |
29% |
6% |
19% |
-10% |
|
0% |
-2% |
-2% |
| Garrett Wilson |
28% |
47% |
19% |
29% |
42% |
13% |
|
BUF |
16% |
-8% |
24% |
-5% |
|
-1% |
-1% |
-2% |
| Amari Cooper |
34% |
71% |
37% |
46% |
34% |
-11% |
|
CIN |
17% |
-6% |
33% |
4% |
|
-2% |
0% |
-3% |
| Mack Hollins |
26% |
19% |
-7% |
18% |
37% |
19% |
|
LAR |
30% |
7% |
17% |
-11% |
|
0% |
-2% |
-3% |
| Brandin Cooks |
43% |
17% |
-26% |
35% |
13% |
-23% |
|
DAL |
26% |
3% |
39% |
10% |
|
-1% |
-2% |
-3% |
| Josh Palmer |
36% |
18% |
-18% |
37% |
18% |
-19% |
|
MIA |
35% |
11% |
34% |
5% |
|
-2% |
-1% |
-3% |
| Jauan Jennings |
16% |
13% |
-3% |
9% |
40% |
30% |
|
TB |
29% |
6% |
19% |
-10% |
|
0% |
-3% |
-3% |
*ONLY WRs with > +- 2% Included in chart above
**Thanks to our friends at Sports Info Solutions and their SIS Database for the info!
WR Matchups to Target in Week 14
*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)
Nico Collins (WR – HOU)
We have been all over Collins late in the season (in both directions) and feel we have a good read on his splits. Collins is a “Man/Blitz” WR. That is, his target share jumps from 24% to 62% when his QB is blitzed and 17% to 46% when facing Man Coverage. Given the Cowboys’ blitz 3% points more than the league average and play Man 10% points more than the league average, this is a great schematic matchup for Collins. Collins also comes in with a favorable CB matchup coming in the top aggregate quarter of our base for the week. Yes, he will face Trevon Diggs for 51% of his expected pass snaps, but he will also see 36% vs. Kelvin Joseph, who’s sporting a 63.2 PFF Grade, and Collins’ 72.4.
Josh Reynolds (WR – DET)
Reynolds has the inverse split of Collins. He sees a 4x jump in target share when NOT Blitzed (41% vs. 11%) and a marginal bump vs. zone. This is good news for Reynolds, as the Vikings only blitz 14% of the time (9 % points less than the league average). Additionally, Reynolds comes in with one of the week’s best “snap weighted height advantages,” taking his 75 inches up against likely main cover man (40% expected pass snaps) Duke Shelley‘s 69 inches.
Christian Kirk (WR – JAC)
Kirk was a WR we targeted early on in the season, but we lost a good read on him the last few weeks. He popped back up on our radar this week, given a strong schematic split advantage (say that 5 times fast). Kirk, similar to Reynolds, is an “in structure WR,” meaning he prefers NO BLITZ (44% target share vs. 28% with Blitz) and Zone Coverage (36 vs. 32 %). Since the Titans blitz the least in the NFL (10%) and run Man 4% less than the league average, this bodes well for the veteran WR, not to mention a very strong net PFF grade vs. likely (78% expected pass snaps) cover man Amani Hooker. Kirk boasts a 75.1 grade, while Hooker only scores 63.2.
WR Matchups to Avoid in Week 14
Amari Cooper (WR – CLE)
We are all familiar with the odd splits Cooper has put up at home vs. away. Conventional wisdom would point to this simply being “noise,” but the fact it has been maintained across multiple teams is something we can’t completely ignore. Given that the Browns are traveling (not far) this week, many will be fading based on this notion alone. Beyond this, Cooper has an insane split based on defenses bringing 5+. Cooper sees a whopping 71% target share when blitzed, yet only 34% when NOT Blitzed. The Bengals bring a blitz 6% points less than the league average, so we may not see that ridiculous target share. Similarly, but at a lower level, Cooper “prefers” Zone Coverage, and the Bengals run zone 4% points less than the league average.
Brandon Aiyuk (WR – SF)
Beyond the QB issues facing Aiyuk, he’s seen a 57% target share vs. Man (40% vs. zone). Given the Bucs show man coverage is 10 percentage points less than the league average, we have a marginal schematic disadvantage. Blend that with our base model disliking his CB matchup based on 41% of his snaps going up against CB Jamel Dean, and Aiyuk is looking like a good fade for the week. Dean sports 73 inches and a 4.3 40, and at 72 inches and 4.5 40, Aiyuk is both smaller and slower than his main expected counterpart.
We hope the data helps you towards a playoff spot this weekend. Good luck, and see you in Week 15!
