Little did I know that when I took over this project, it would undergo a total transformation, from data sources to expansion to new metrics based on what Jon had been doing previously.
Welcome to my second installment of the Fantasy Football Usage Report, which Jon Jackson provided in the previous few seasons. My first time getting my feet wet was two weeks ago. At the time of this writing, Monday Night Football hasn’t finished yet.
Feel free to check out the full report here.
Stats were pulled from Pro Football Reference, and built up from there. Metrics referenced here include:
USG — Usage, or Targets+Carries/Team Snaps, denoting how much a player was used during all of their team’s field time
INVL — Involvement, or Targets+Carries/Individual Snaps, denoting how much a player was used during all of their individual field time
U/I — USG/INVL. Ultimately, this is just a player’s snap percentage. But because I look at multiple weeks of USG and INVL, USG/INVL actually uses simpler formulas in my spreadsheet than always referencing a player’s snap percentage
UIP — (USG/INVL)*PPR. Similarly, UIP is just a player’s snap percentage multiplied by their PPR score over the week(s), and to me “UIP” is easier to think of than any abbreviation that would come from “snap percentage times PPR”
DOM — Dominator Rating. A common term in the fantasy football community, it looks at a player’s production and places a value on how much of the individual production accounted for the team’s production
UIP has been particularly fun for me in looking at who is making the most of their opportunities.
In all screenshots, a player’s week 11 Snap Percentage is included to help give context to who the fantasy-relevant players are in general. We’re not worried about anyone consistently getting five snaps a game.
Keeping in line with past Workload Reports, its focus on rushing and receiving work means we won’t really be looking at QBs, but instead RBs, WRs, and TEs, and ideally, I’ll be able to add in post-Monday Night Football data later on Tuesday.
First, here’s a look at players’ fantasy scoring differences compared to week 11.
Player Fantasy Scoring Comparisons (In PPR)
Players with the highest percentage of their team’s PPR points (excluding QB) in week 11:
For dynasty players hoping that the only obstacles in the way of Ricky Pearsall or Jacob Cowing‘s future were Brandon Aiyuk and Deebo Samuel, a third obstacle may have been standing right in front of you the whole time.
Before this season, Jauan Jennings was a player who could fill in admirably if Aiyuk or Deebo were out, but this year, he looks like he’s progressed even further. We saw a glimpse of that when he put up a fantasy 40-burger against the Rams earlier in the year. But hey, that was just one game, right?
Maybe not. Occasionally we’ll have our late risers at receiver in the NFL and Jennings is putting up advanced metrics that say he just might be more than a fill-in starter for the 49ers past this season.
Unfortunately his buy-low window may have just closed after catching 10 of 11 targets for 91 yards and a score against the Seahawks. Might still be worth a shot though, in case Jennings managers still consider it to be fluky.
Players who saw the highest jump in PPR% compared to their week 6-10 rolling average:
Xavier Worthy‘s lack of production so far his rookie season started to get some dynasty players concerned that they drafted a bust, if for no other reason than the incredibly predictive stat for receivers that don’t hit 500 receiving yards their rookie year:
Before week 11, Worthy was only on pace for 468 receiving yards this year. After week 11? 522. Rest easy dynasty players, at least for this week.
Players who saw their biggest drop in PPR% from their week 6-10 rolling average:
Not sure that I’m too concerned about the drop in production for most of the fantasy-relevant players here, but Tucker Kraft‘s case is interesting. In weeks 4-8, Kraft scored five touchdowns, making him a hot waiver pickup and a solid weekly play during that stretch. But this season, he only has four targets-per-game, highlighted by this past week’s one-target goose egg that may be a sign of balance.
Weeks 4-8, we saw the highs, when Kraft was beginning to be considered an essential piece of the offense. This week we experienced the lows, and there may be more coming.
Involvement (INVL)
Players with the highest week 11 INVL:
If you play the game long enough, you know to take anything Sean Payton says with a grain of salt. It almost makes me regret dropping Javonte Williams after he resumed the lead spot in Denver, taking a 52-percent snap share and turning it into 9 carries for 59 yards, a TD, adding another four receptions against the Falcons.
But like I said, almost. I still have him in other leagues where the Sean Payton experience is all but guaranteed to mean maddening running back usage again before the season’s over, and that’s not a problem I prefer to deal as the fantasy playoffs approach.
Players with the highest jump in INVL compared to their week 6-10 rolling average:
We knew this kind of game was coming at some point for Taysom Hill, he has one or three games each year where he lights up the stat sheets. Week 11’s performance was his highest fantasy performance of his career (not bad for 34 years old), and his only time scoring at least a fantasy 30-burger besides once in 2022.
We play him in fantasy when we think these small windows are going to happen. If he sat on your bench, well, here’s hoping for one more peak game before season’s end, just wish I could tell you when exactly it’s going to happen.
Usage (USG)
Players with the highest week 11 USG:
Khalil Herbert remains a non-factor so far in Cincy for those hoping for him to at least occupy the Zach Moss role at some point.
And the Jets figured out how to get (one of) their best players the ball this week in Breece Hall, as dynasty players who took him 1.01 in 2022 still patiently wait for New York to find a way to get the offense clicking consistently.
Players with the highest jump in USG compared to their week 6-10 rolling average:
Jaxon Smith-Njigba (10 rec, 110 yds, 1 rush for 8 yds @ SF) looks like he may be taking over Tyler Lockett‘s presence on this team full-time. Still too early to call it for sure, but we all know the talent, we all know Lockett is on the wrong side of 30, and offensive coordinator Ryan Grubb made the absolute most of every opportunity he had as OC in a handful of places during his college coaching tenure.
Now a lot of times, that doesn’t translate to the pros (Urban Meyer, Steve Spurrier,Dave Wannstedt, etc, etc), but this is the kind of effect many were expecting: maximizing the talent you have, of which JSN has plenty. I wouldn’t be surprised to see him put up WR2 numbers the rest of the year.
Players with the biggest drop in USG compared to their week 6-10 rolling average:
Amari Cooper (2 rec, 55 yds vs KC on a 51-percent snap share) didn’t see a ton of work in his first game back from a wrist injury, but I’m not too concerned about him being a playmaker for the Bills down the stretch.
He tends to play up, or down, to the talent around him, so while he led the league in drops during the Browns’ early struggles, he looks motivated to come through for the juggernaut Bills.
UIP (USG/INVL*PPR)
Players with the highest week 11 UIP:
A couple players at the top worth highlighting are Tee Higgins and Jonnu Smith.
It’s good to see Tee getting back into the action like this despite the Bengals loss to the Chargers, which drops Cincy to 4-7. The Bengals may have to win out to have a shot at the playoffs, and since the Bengals defense has led them to this point, there could be plenty of pass-happy game scripts from a QB in Joe Burrow who is playing at an MVP level.
Jonnu Smith‘s performance (6 rec, 101 yds, 2 TD vs LV) shouldn’t be all too surprising. Two touchdowns are very most likely not happing again in ’24, but this wasn’t Smith’s first game this season with eight targets.
He’s been getting 6.5 targets-per-game the last six games, and figures to remain a threat the rest of the season, even if he doesn’t put up fantasy starter-worthy numbers each week. The journeyman tight end looks like he found his ideal spot in a Dolphins offense that typically didn’t prioritize the position during Mike McDaniels‘ tenure.
One player fantasy managers were hoping to see in the green here was Nick Chubb.
Of any week to return to pre-injury form, this was a prime one for him, going against probably the worst run defense he was going to see the rest of the season in the New Orleans Saints. Unfortunately, he only saw 11 carries for 50 scoreless yards and no receiving work.
A sad truth may be emerging that since the Browns are essentially out of the playoff picture, they see no need to risk overworking Chubb since returning from his devastating knee injury last season. That doesn’t bode well for his fantasy prospects this year. On brighter notes, he very likely was a late-round, nothing-to-lose type of pick in redraft leagues, and in dynasty, may make him an ideal buy-low candidate for those in a good position to contend next season.
So instead, Jerry Jeudy (6 rec, 142 yds, 1 TD)and Elijah Moore (6 rec, 88 yds, 1TD) make their presence felt this week against a Saints team that may have been giving extra attention to stopping Cedric Tillman.
Players with the highest UIP compared to their week 6-10 rolling average:
I would expect Noah Gray to return to fantasy irrelevance next week, moreso than Nick Westbrook-Ikhine and, dare I say it, Marques Valez-Scantling.
One good deep ball was all it took for Rashid Shaheed to put up a starting fantasy stat line any given week when he was healthy, and Derek Carr has not been afraid to try and supply that this season.
And as the Titans’ WR2, Westbrook-Ikhine has been able to do just enough with the occasional long ball ( 98-yarder this week) and TD (five in the last six games) to bring himself onto all fantasy rosters for the rest of this year, as he’s been on the WR31 pace the last four games.
DOM (Dominator Rating)
Players with the highest week 11 DOM:
Players with the highest DOM compared to their week 6-10 rolling average:
Thanks for reading!
