Team-by-team TPRR, wTPRR takeaways from the first month

My favorite post, and not a moment too soon

Oct 04, 2023

∙ Paid

I’m picturing some of you who have been around for a while seeing the title of this post and doing a little fist pump. I’m as excited as you are. I love this exercise.

For those of you newer around here, I started doing work on TPRR back in 2020, when I argued the fantasy industry should be spending a lot more time with it as a stat. It’s been great to see that in the years since, but there’s also been the (expected) attempts to delegitimize it, and so I’ve written a lot of theory about the stat, and how I prefer to use it. That first link is the original post where I introduced it here at the newsletter (I’d previously that summer introduced it through a Twitter thread, while still at CBS), and it still contains some of my best thoughts on it, but then there’s:

More early thoughts on how air yards fit in from October 2020
General thoughts and how WOPR relates to it from September 2021
Even more general thoughts as I started looking at college data in April 2022
The “All roads lead to A.J. Brown” post in July 2022 (that starts “I’ve gotten some questions this offseason about my reliance on TPRR as a metric…”)

If you’re looking for more information about the stat, those are some great places to read up on how I use it, and why I love it (huge hat tip to reader Scott Wolf for helping me catalog my archives this offseason). One short note I’m sure I emphasized multiple places there is that I almost couldn’t care less about the r-squared — even though it’s strong — especially relative to other (blended) stats with lots of stuff thrown into them, because TPRR is a very simple stat, that literally measures what it says, which is the number of targets a player earns per route he runs.

For so many of the stats people want you to believe matter, there is more complexity. Maybe it’s a model with layers of inputs, so you’re never really sure when parts of it are unsustainable small samples. Maybe something has to be charted or determined that is subjective, like some of my recent comments about the new “first-read” targets data sets that aren’t actually charting first reads, or at least not as cleanly as the title of the stat makes you believe. They are doing the best they can with it, which I respect that, but then the stat is discussed in a way that doesn’t acknowledge that.

This is a major issue with so much stat use. People draw conclusions from stuff they can’t even explain, and don’t really understand. But those stats are going to tell you one player ranks higher than another, so you just buy it, even when half the time it doesn’t move the needle for fantasy (and the other half is dubious, at best). But people also don’t acknowledge their misses, so there’s certainty in wrong analysis, bad results, ignoring the bad results, and the same certainty the next week. Just how we were taught the scientific method should look.

Which isn’t to say TPRR is perfect, and then I’ve also created one of those complicated stats — wTPRR, which adds air yards and weights targets and air yards such that targets are more notable — but I tend to use this stat a little less frequently, because it isn’t so simple to understand. But that short note I wanted to emphasize about TPRR and its r-squared is that what we’re not able to glean is large-sample testing is referred to as “unexplained variance,” and with TPRR I always know what the unexplained variance is. It’s everything else.

So many of the other stats try to throw everything into a model, but that model still isn’t predictive enough to be the only thing you can use (not even close, because it’s football), and it becomes so opaque a stat that you also can’t use it with anything else to do multi-variable analysis, which is really what we need to be doing in all of these spots. We need to be looking at things from different angles to read them right. That’s why we all love analyzing football, because there are no clear answers and each little debate is like a new puzzle where the key piece might be something different.

So with TPRR, I have this great foundational piece, and then I know that I need to add in context about the size of the player’s routes role, and his aDOT, and whether the offense runs a lot of two-WR or three-WR sets (and the logical extension of what his competition for targets is across the board, in terms of the other four possible eligible receivers on any given route), and whether his QB is mobile or takes sacks at a high rate such that a high number of dropbacks (and thus routes) aren’t even becoming pass attempts (and thus targets).

That’s a whole bunch of stuff that sounds vaguely complicated but really isn’t, at least not if you’re reading Stealing Signals each week (or writing it). We know what these offenses are, and what the players’ roles are, and we’re not actually comparing the TPRRs across teams like it’s apples to apples.

In the years I’ve used TPRR, I’ve done extensive early offseason looks at the full-season data, that have been integral to my offseason process, and then perhaps more importantly I’ve done a couple in-season checks each year. And since the beginning, I’ve done it by going team by team and looked at the ecosystem of that individual offense, where I can apply my own context to the unexplained variance of what TPRR is not catching.

For the in-season stuff, I like to wait a few weeks so we can get a decent routes sample. Say, four weeks, so we can get a good number of players over 100 routes. Oh hey, that means we’re up to do that today. Let’s get it.

I always like to start with a look at the league leaders, so you can get an idea of the scale of the different stats. Let’s start with the top TPRRs so far this year with at least 100 routes.

The displayed data is - TPRR, wTPRR (routes)

Davante Adams - 0.35, 0.92 (142)
Tyreek Hill - 0.33, 0.92 (120)
Puka Nacua - 0.30, 0.73 (168)
A.J. Brown - 0.29, 0.81 (139)
Mike Evans - 0.29, 0.83 (103)
DeAndre Hopkins - 0.28, 0.78 (104)
Justin Jefferson - 0.28, 0.74 (167)
Stefon Diggs - 0.27, 0.68 (141)
Chris Olave - 0.27, 0.77 (141)
Keenan Allen - 0.27, 0.65 (160)
Zach Ertz - 0.27, 0.61 (112)
Romeo Doubs - 0.26, 0.71 (121)
Amon-Ra St. Brown - 0.26, 0.62 (128)
Sam LaPorta - 0.26, 0.56 (105)
Nico Collins - 0.25, 0.69 (127)
Ja'Marr Chase - 0.25, 0.56 (162)
Marquise Brown - 0.24, 0.64 (132)
Jakobi Meyers - 0.24, 0.61 (109)
Chris Godwin - 0.24, 0.58 (123)
Garrett Wilson - 0.24, 0.59 (140)

You can see at the top how someone like Puka Nacua has a bit lower wTPRR because of a lower aDOT and fewer total air yards. Let’s reshuffle to sort by the top wTPRRs.

Tyreek Hill - 0.33, 0.92 (120)
Davante Adams - 0.35, 0.92 (142)
Mike Evans - 0.29, 0.83 (103)
A.J. Brown - 0.29, 0.81 (139)
DeAndre Hopkins - 0.28, 0.78 (104)
Chris Olave - 0.27, 0.77 (141)
Justin Jefferson - 0.28, 0.74 (167)
Puka Nacua - 0.30, 0.73 (168)
Romeo Doubs - 0.26, 0.71 (121)
Nico Collins - 0.25, 0.69 (127)
Stefon Diggs - 0.27, 0.68 (141)
Keenan Allen - 0.27, 0.65 (160)
Kendrick Bourne - 0.23, 0.65 (122)
Marquise Brown - 0.24, 0.64 (132)
Mike Williams - 0.23, 0.63 (107)
Jayden Reed - 0.22, 0.63 (103)
Amon-Ra St. Brown - 0.26, 0.62 (128)
Jakobi Meyers - 0.24, 0.61 (109)
Zach Ertz - 0.27, 0.61 (112)
Amari Cooper - 0.20, 0.59 (136)

Toward the bottom of this list, you get some names added that didn’t make the first list. Keep in mind, the point of these stats is not to compare across teams necessarily, so let’s go team by team.

For the below, I’m going to lower the routes minimum to 40 (10 per game) to bring in RBs and secondary players, because a big thing we want to look at is guys who haven’t had a full role yet, and how they are doing.

Arizona Cardinals

Zach Ertz - 0.27, 0.61 (112)
Marquise Brown - 0.24, 0.64 (132)
Michael Wilson - 0.17, 0.50 (103)
Trey McBride - 0.15, 0.33 (40)
James Conner - 0.14, 0.20 (66)
Rondale Moore - 0.12, 0.22 (102)

Michael Wilson’s Week 4 was his best game yet, and it’s possible he continues to develop.

Zach Ertz is going to haunt me when I’m 50.

There’s probably not a ton of upside to go hunting for in this passing game, so players need to be pretty special to matter.

Atlanta Falcons

Jonnu Smith - 0.22, 0.52 (90)
Bijan Robinson - 0.21, 0.32 (105)
Drake London - 0.17, 0.44 (127)
Kyle Pitts - 0.16, 0.42 (127)
Mack Hollins - 0.14, 0.44 (104)

The big thing with Kyle Pitts and Drake London last year was they still had the strong TPRRs, and I didn’t actually know what their four-week number would look like this year when I started this, because I get a little caught up in the weekly stuff while writing Signals in-season (which is why I like to do these periodic checks). Anyway, seeing that they aren’t earning much per-route volume this year was another little dagger.

At least Pitts’ 0.8% pass block rate is a career low. I also want to say I was maybe a bit presumptuous with my comments on Pitts’ health this week, and it’s probably the case that I am just wrong in thinking he’s mostly healthy. I’ve just seen some things that mention his gait, and if you go back to his college film, he’s an easy strider then, too. So from that perspective, what I think people see as a lack of full speed in highlights is something that to me has always been there with how he moves.

Bijan Robinson is obviously acquitting himself well in the passing game, already up over 20% TPRR while also being a high-routes RB — only four RBs have 100+ routes so far (Kyren, Etienne, Bijan, and Rachaad White, with Rhamondre at 99 and then CMC at 92).

Baltimore Ravens

Zay Flowers - 0.23, 0.50 (127)
Mark Andrews - 0.20, 0.46 (89)
Nelson Agholor - 0.16, 0.43 (76)
Devin Duvernay - 0.15, 0.37 (41)
Rashod Bateman - 0.13, 0.31 (68)
Odell Beckham Jr. - 0.13, 0.34 (54)
Isaiah Likely - 0.10, 0.22 (42)
Gus Edwards - 0.05, 0.07 (41)

First of all, Gus Edwards’ 5% TPRR is maybe a new record, in the funny way.

Zay Flowers and Rashod Bateman are clearly on different trajectories. That’s not great for Bateman, who I’m started lose some faith in, although he’s another where we don’t know how much is injury-related. Between Bateman and Odell Beckham, though, the Ravens haven’t actually realized much of the weaponry increase they’d hoped to. Flowers has that low wTPRR relative to his 0.23 TPRR because of the underneath looks, as well.

Mark Andrews has room to rise, with higher career numbers than this and the WRs struggling. He’s hit at least 23.9% TPRRs every year since 2019. One minor note is he’s being asked to pass block 5.1% of the time, after previously never hitting even 2.0% in any season of his career.

Buffalo Bills

Stefon Diggs - 0.27, 0.68 (141)
Deonte Harty - 0.27, 0.54 (41)
James Cook - 0.17, 0.32 (76)
Latavius Murray - 0.16, 0.27 (44)
Dalton Kincaid - 0.15, 0.30 (107)
Gabe Davis - 0.13, 0.42 (134)
Dawson Knox - 0.12, 0.26 (101)

The TEs and Gabe Davis are all struggling to earn volume so far, while Stefon Diggs does Diggs stuff.

Deonte Harty has a long track record of earning volume per-route, despite never playing big snaps due to his size. Just something to keep in mind.

Carolina Panthers

Miles Sanders - 0.25, 0.43 (84)
Adam Thielen - 0.20, 0.46 (169)
Jonathan Mingo - 0.19, 0.50 (95)
Terrace Marshall Jr. - 0.19, 0.43 (123)
Chuba Hubbard - 0.18, 0.27 (61)
Hayden Hurst - 0.13, 0.29 (117)
DJ Chark Jr. - 0.11, 0.34 (123)

Adam Thielen is one route shy of Tutu Atwell for most in the NFL. His actual per-route volume isn’t actually all that exciting, nor is anyone else’s for that matter, and we see what can happen sometimes where the RB’s volume gets elevated as a result (checkdowns up as WRs can’t separate).

You understand why the Panthers are reportedly in the market for a No. 1 WR. Who could have seen this coming only four weeks into Bryce Young’s career?

“I like Bryce Young longterm, but I’m pretty out in Year 1 due to what I see as a really poorly constructed WR room.”

Chicago Bears

Roschon Johnson - 0.25, 0.42 (48)
Cole Kmet - 0.22, 0.49 (111)
Khalil Herbert - 0.20, 0.32 (65)
DJ Moore - 0.16, 0.48 (147)
Chase Claypool - 0.13, 0.38 (90)
Darnell Mooney - 0.11, 0.27 (111)

Cole Kmet has been good at earning volume, but most of the players here are negatively impacted by the sack and scramble rates.

D.J. Moore’s specific TPRR isn’t as much of a concern as most we’d see in the 16% range, because he’s running a ton of routes and we know some of the lack of volume goes back to the QB.