Using TPRR to identify early AFC draft values
WRs and TEs to target in early best ball drafts
I’ve started and not finished several posts over the past couple weeks, for several reasons including my not being as engaged here as I am with some personal goals like getting physically healthier and doing more proactive parenting. But during some recent episodes of Ship Chasing I’ve come to realize I have already started formulating far more player opinions than I’d assumed, and I know many of you who are already drafting plenty of best ball teams have asked that I do a better job of staying on top of my player opinions through the offseason this year.
There’s more theory stuff coming, and some thoughts on the rookie class, which is frankly incredibly intriguing to me as the simple profiles that are easier to analyze are limited, and everything is a bit more complex this year (but we know the complex profiles with red flags do hit sometimes, and sometimes in big ways, so that presents some very interesting opportunities to understand the bets to be made and take some stands). I’m also working on a big project to categorize some past evergreen-ish writing, as I’ve promised for a couple years now since it became a frequent request. I have help this time, which gives me a lot more faith in the completion of the project, which could be loosely defined as a Stealing Signals book. (Just something to keep an eye out for in a couple months, and I probably won’t talk about it again until it’s ready or close to it.)
For today, I want to cut through anymore unnecessary pretense and get into analyzing players as quickly as possible. I used TPRR and wTPRR to do this even earlier last offseason, and I broke that down into two parts. You can find the first part here, unlocked for non-premium subscribers, which includes a substantial explanation of my relationship with these stats — why I trust them, how I use them, and what I’m trying to accomplish when I dig into this.
One of my notes last year discusses how my spreadsheets are set up to use PFF’s data, and it’s central to how these numbers will look if you’re used to RotoViz’s tools:
The Stealing Signals tool over at RotoViz uses SIS data, and they are a little more strict with their route classifications, lowering the total number of routes for most players and thus increasing stats like TPRR, so you’ll see some discrepancies between the specific numbers. Most players move in the same direction so the conclusions shouldn’t be noticeably different as long as the data source is consistent through the specific analysis.
As I go team-by-team, I’m going to look at players who ran 100 or more routes last year, and everyone will be assigned to their 2022 teams as I’m analyzing this data in a descriptive way. What you’re looking at displayed below is:
Player Name - TPRR, wTPRR (total routes)
For TPRR, anything over 0.25 — meaning a target on 25% of routes — is very strong. But anything below about 18% gets to be pretty replaceable, and below 15% is essentially not fantasy-relevant. If that range feels tight, consider that on any given play there are five eligible pass-catchers, so in a perfectly equitable world where every pass attempt is targeted to a player, everyone would have a TPRR of 20%. (Of course, there are throwaways and routes where no one is targeted, and also eligible receivers pass block sometimes so five players don’t run a route on every play, and those things don’t totally cancel out but essentially you wind up with this range of 15%-25% for most players.)
For wTPRR, the scale is higher and wider because of how it incorporates air yards, and what constitutes “good” or “interesting” will differ by position, but for a WR you’re essentially talking about 0.65 and above as the strong end.
Here’s a short list of every player who ran at least 150 routes (a slightly higher threshold than I’ll use as I go team-by-team) and hit that 25% TPRR threshold in 2022, sorted by TPRR. It should give a frame of reference of the high end of TPRR, but also how RBs and TEs can fit in, and what high TPRRs at those positions typically equate to in terms of wTPRRs (because the hierarchy of aDOTs goes WRs deepest — averaging around 11.0 yards, but there are different types obviously — then TEs around 7.5 on average, but same about types, then RBs basically being near the line of scrimmage so aDOTs of 0 or sometimes in the negative, and anything at 2.0 or higher being interesting in terms of providing some of those sweet, sweet RB air yards).
You’ll also see in the routes number in parenthesis some small-sample players on this list — typically from those positions that pass block a decent amount, e.g. RB and TE — and it’s an important reminder that there is necessary context all over within this stat. The most important note is we can’t just extrapolate smaller routes samples to larger roles and assume a player will maintain the same strong TPRR. There’s more variance in smaller samples, and importantly in certain profiles. I’ve used Rondale Moore’s rookie year in 2021 as an example here before, because he got a lot of designed touches on a limited number of routes. RBs who have the ability to rack up dumpoffs on a sub-200-route sample are a whole group who fit this idea.
In other words, Ja’Marr Chase at 0.25, 0.61 on 518 routes is very strong, because Ja’Marr Chase is very good and his targets are very valuable. Rex Burkhead at 0.26, 0.47 on 189 routes both shows the reason for including wTPRR — Chase’s weighted volume is substantially higher per-route despite a slightly lower TPRR — and also is a word of caution on sample size, because Burkhead drawing a high TPRR on 189 routes is just a minor footnote.
Here’s the high-end list:
Tyreek Hill - 0.31, 0.85 (534)
Amon-Ra St. Brown - 0.29, 0.64 (483)
Austin Ekeler - 0.28, 0.45 (436)
Cooper Kupp - 0.28, 0.64 (338)
Drake London - 0.27, 0.71 (419)
Davante Adams - 0.27, 0.74 (618)
D'Andre Swift - 0.27, 0.45 (236)
Kyle Pitts - 0.27, 0.75 (211)
Chris Olave - 0.26, 0.77 (431)
Chigoziem Okonkwo - 0.26, 0.61 (172)
Stefon Diggs - 0.26, 0.69 (573)
Rex Burkhead - 0.26, 0.47 (189)
CeeDee Lamb - 0.26, 0.66 (571)
DeAndre Hopkins - 0.26, 0.66 (362)
Rhamondre Stevenson - 0.26, 0.42 (339)
Justin Jefferson - 0.26, 0.65 (690)
Mark Andrews - 0.25, 0.64 (434)
J.D. McKissic - 0.25, 0.45 (154)
Christian McCaffrey - 0.25, 0.44 (405)
Travis Kelce - 0.25, 0.58 (600)
Ja'Marr Chase - 0.25, 0.61 (518)
Let’s get into the data for the AFC teams, with some quick discussion for each team of how I’m viewing the data. I’d love to really get in-depth in every case, but I’m going to try to get through this without racking up a word count my in-season stuff would be jealous of.
AFC East
Buffalo Bills
Nyheim Hines - 0.27, 0.52 (136)
James Cook - 0.26, 0.49 (126)
Stefon Diggs - 0.26, 0.69 (573)
Isaiah McKenzie - 0.16, 0.38 (383)
Gabriel Davis - 0.16, 0.48 (583)
Dawson Knox - 0.14, 0.32 (472)
Devin Singletary - 0.13, 0.22 (373)
Khalil Shakir - 0.12, 0.32 (141)
Quintin Morris - 0.06, 0.14 (147)
After a strong Buffalo debut in 2020 and then a disappointing 2021 mostly fueled by lackluster after-the-target efficiency, Stefon Diggs bounced back across the board in 2022, getting his per-route volume and YPT both back to 2020 levels. Taking the three years together, there’s probably more downside to his 2022 data than additional upside, with the exception being that he could probably run another 50 or so routes above this 573 number.
Gabriel Davis saw the big routes increase that was expected, but his TPRR fell from 18.0% in 2021 to 16.0% in 2022. His already high aDOT spiked, and we know he played through an ankle issue, so it’s possible his lack of volume in the shorter and intermediate ranges was injury-related. Still, there’s no guarantee the routes will stay at this near-600 level. He’s a difficult case study for 2023.
James Cook drew volume well while Devin Singletary has consistently been poor in TPRR throughout his career, but Cook’s 2023 receiving outlook is obviously influenced by the presence of Nyheim Hines (whose data includes his time in Indy).
Dawson Knox continues to be a replacement-level type volume-earner, and Khalil Shakir didn’t do much to write home about as a rookie, but anyone’s per-route figures are more valuable in this offense due to pass rate and TD-scoring potential.
Miami Dolphins
Tyreek Hill - 0.31, 0.85 (534)
Jaylen Waddle - 0.22, 0.58 (524)
Alec Ingold - 0.15, 0.28 (149)
Jeff Wilson Jr. - 0.14, 0.26 (253)
Mike Gesicki - 0.14, 0.36 (354)
Raheem Mostert - 0.13, 0.23 (294)
Cedrick Wilson - 0.12, 0.32 (137)
Trent Sherfield - 0.12, 0.31 (402)
Durham Smythe - 0.12, 0.24 (160)
Tyreek Hill was a volume monster in his first year in Miami. As Hill operated around the line of scrimmage a decent amount, Jaylen Waddle saw his aDOT spike from 7.0 as a rookie to 12.2 last year, which helped lead to a massive YPT increase. Waddle’s TPRR dipped but his wTPRR rose, and while his efficiency will be tough to match, you love seeing the guys who can win at all depths. There’s some obvious pseudo-handcuff upside with Waddle where Hill missing any time would likely decrease Waddle’s aDOT and increase his TPRR as he presumably took over some of the free touches near the line of scrimmage that Hill feasted on last year.
I wrote a little last summer about Alec Ingold being a pest for RB receiving upside in this offense, and I mean if I’m not going to victory lap a fullback take every chance I get, what am I even doing here?
It’ll be interesting as defensive approach to what Mike McDaniel did last year evolves, whether that forces targets to a third or fourth option with more regularity. Obviously, the top end of this passing game was pretty concentrated last season.
New England Patriots
Rhamondre Stevenson - 0.26, 0.42 (339)
Jakobi Meyers - 0.22, 0.55 (423)
Jonnu Smith - 0.22, 0.42 (171)
Nelson Agholor - 0.17, 0.45 (295)
Kendrick Bourne - 0.16, 0.40 (296)
DeVante Parker - 0.15, 0.46 (316)
Hunter Henry - 0.14, 0.32 (419)
Tyquan Thornton - 0.13, 0.38 (326)
Tyquan Thornton wound up being a disappointment in Year 1, and Hunter Henry really fell off from an 18.4% TPRR in his first year with New England that matched his prior career numbers.
Rhamondre Stevenson is good, that’s pretty evident, although his YPT wasn’t strong and Damien Harris posted a TPRR of 23% on 96 routes, just missing this cutoff. With RBs, identifying any signal is often a matter of seeing if the player was substantially better than his teammates in the same situation; this appears to be a situation where offense, QB preference, and lack of receiving weapons led to a lot of dumpoffs regardless of RB.
There really wasn’t much of anything in this passing game outside Jakobi Meyers posting another very solid season on a 423-route sample. Meyers is obviously gone now, joining his former offensive coordinator Josh McDaniels in Las Vegas, where he’ll have more target competition but is a good bet to take on a lot of what Darren Waller did at the shallower depths. As for how the Patriots’ passing game takes shape for 2023, I’m worried about another season without anyone racking up huge routes totals and no real standout.
New York Jets
Breece Hall - 0.28, 0.58 (109)
Garrett Wilson - 0.23, 0.60 (595)
Michael Carter - 0.18, 0.30 (267)
Braxton Berrios - 0.17, 0.38 (180)
Tyler Conklin - 0.17, 0.40 (487)
Corey Davis - 0.15, 0.42 (396)
Denzel Mims - 0.15, 0.41 (164)
James Robinson - 0.12, 0.20 (106)
Elijah Moore - 0.12, 0.32 (495)
C.J. Uzomah - 0.12, 0.26 (225)
A big reason to utilize TPRR is its stability year over year, and that makes Elijah Moore one of the most confounding players heading into 2023, a fact not helped by his sudden trade, and only for a pick swap (a valuable pick swap, but not a crazy strong one working back to the Jets). I’m often talking about the longer timeline, and Moore had an incredibly strong TPRR record in college, then posted 24.0% at a reasonably high 12.4-yard aDOT as a rookie, albeit on just 308 routes. That makes this cratering down to 11.9% on nearly 500 routes at a similar 12.6 aDOT so difficult to understand; even after the slow start and leaving the team for a bit, I just expected that to pick up down the stretch, and it never really did. Again, the long view back to college suggests there’s still upside here and 2022 will go down as a bizarre season in an otherwise good player’s career. But I won’t just throw out 2022, either.
I’ve seen Garrett Wilson compared to Chris Olave a ton, particularly around the time when Wilson won Offensive Rookie of the Year and it was trendy to argue Olave should have (it’s then been extended to argue Olave’s merits at the expense of Wilson, which I just don’t understand). My take is Wilson was flat better in terms of stats like TPRR with a year less experience in the same college offense, and then Wilson hit very strong per-route numbers as a rookie that don’t need to be compared to anyone. Olave’s production in New Orleans was even stronger, yes, because Olave’s was good enough to be in the top 10 of the above league-wide list. If you want to compare Wilson’s TPRR and wTPRR to someone, you can say they are right in the ballpark of Justin Jefferson’s or Ja’Marr Chase’s rookie seasons (but with much worse efficiency). Given his circumstances were bad enough in 2022 that they seemingly destroyed another promising young WR in Moore, Wilson had an incredibly successful Year 1 on his own merits, and boasts the type of profile I expect to continue to elevate. There’s no reason to cast any shade on Wilson entering Year 2, especially if he gets the QB upgrade we all expect to come. He should easily be the alpha over Allen Lazard.
AFC North
Baltimore Ravens
Mark Andrews - 0.25, 0.64 (434)
Rashod Bateman - 0.23, 0.65 (120)
Isaiah Likely - 0.21, 0.48 (268)
Demarcus Robinson - 0.21, 0.52 (357)
Josh Oliver - 0.15, 0.37 (154)
Kenyan Drake - 0.15, 0.25 (162)
Devin Duvernay - 0.13, 0.33 (352)
Sammy Watkins - 0.13, 0.37 (203)
James Proche - 0.12, 0.31 (131)
Justice Hill - 0.10, 0.15 (124)
Patrick Ricard - 0.08, 0.13 (166)
If a tree falls in the woods, does it make a sound? The Ravens were interesting for the first part of the season, but not enough has been made of how their offense was essentially as bad for passing-game production as the worst in the league after about Week 7. That crushed Mark Andrews, who went from 89+ receiving yards in four of his first six games to not breaking 65 again until Week 17. During that stretch, he was a lot more like Kyle Pitts than the fantasy community has been willing to admit, which is to say that Pitts’ strong per-route-run stuff is still very defensible and I’m absolutely going to do that (while staying reserved because Arthur Smith is back and that passing offense might suck again, but we’ll save that for the NFC article).
Rashod Bateman’s season was a disappointment, but after just a 15.9% TPRR on 409 routes as a rookie, it’s at least something to see him at 23.3% in 2022, albeit on just 120 routes. That’s the guy we thought he’d be given his profile, and that small sample does pair with the rest of the context of his young career (specifically his strong production profile coming into the league) to create some optimism in him as a buy low headed into Year 3.
Isaiah Likely was a fun rookie, and given his longer-term profile, is a good bet to be a productive NFL tight end. The issue of his role behind Andrews will be one for some time, but he’s the kind of guy that might be worth trying to acquire and stash in dynasty TE Premium formats, if the price is right.
In five seasons in Kansas City, Demarcus Robinson’s career high TPRR was 12.1%. That’s not a typo. In his final year there in 2021, he was at 7.8% (!!!) on 464 routes. Mostly I note this just to reiterate that even with a solidly stable stat like TPRR, situation can matter. Things broke right for Robinson last year, and the way the offense viewed him and started to treat him, he wound up seeing a TPRR of 20.7%. But I still would argue that in any functional offense, Robinson is a total afterthought. So again, what I’m saying here is if Demarcus Robinson can get to a TPRR of over 20% on 357 routes, then we shouldn’t take every piece of data as some type of cold, hard, objective fact that can be compared apples to apples with every other piece of similar data. That goes for individual players across seasons of their own careers, for comparing young players like the comments above about Garrett Wilson and Chris Olave, and for a wide variety of different football statistics.