This is the 7th in a series of articles I am writing to analyze some common NFL statistics, focusing on how much value they have relative to team wins. I want to acknowledge the work of Brian Burke, Chase Stuart, and even our own Matt Grecco, who inspired this analysis and whose methodologies I have leveraged, as well as Pro Football Reference, Armchair Analysis, NFL.com and the nflSCrapR project as the sources of my data.
I’ll start with a quiz. Since 2000, which Tight End with at least 75 career targets has the highest catch rate?
- A) Tony Gonzalez
- B) Antonio Gates
- C) Rob Gronkowski
- D) Jimmy Graham
- E) Some TE whose last name does not begin with “G”
If you chose “E”, congratulations, you are correct. Catching 77% of all passes thrown his way, the TE with the highest catch rate this century is . . . Jack Doyle.
In fact, no need to limit it to TEs, he has a catch rate higher than any wide receiver. That’s right, Doyle rules catch rate. Unfortunately, he also rules a crap stat.
In a perfect world, where all things are equal, I would have no problem with catch rate, but we don’t live in that world. Instead, we live in a murky, shadow world, where Sense8 warranted a second season and people happily cheer for the Patriots. And in that world, variables beyond a receiver’s control impact his catch rate.
Now, to be fair, all stats suffer from similar “noise”, but catch rate (CR) is particularly susceptible.
Because CR is such a noisy stat, it doesn’t even clear the simple threshold of being moderately related to wins and if a stat isn’t measuring a skill that leads to wins then what is the point? You might as well just track who wins the opening coin toss . . . which I do.
If you read my post on passer rating, then the following data won’t be new to you.
Receiving yards, yards per catch and catch rate aren’t very good stats because they aren’t good descriptors of how games are won (low correlation to wins). Of course, those are the 3 most common receiving metrics in use today, so there’s that (#shadow_world).
On the other hand, Yards Per Target (YPT), which is just receiving yards divided by the number of targets (attempts) is a good stat, but let’s put a pin in that for now.
QBs have variable skill levels and develop different relationships with their receivers that impact CR. Here is a sample of receivers who had at least 100 targets with 2 different QBs.
Catch Rate by QB
|Eddie Royal||Kyle Orton||98||181||54.1%|
|Jeremy Shockey||Eli Manning||206||363||56.7%|
|Kenny Stills||Ryan Tannehill||59||124||47.6%|
|Larry Fitzgerald||Derek Anderson||49||102||48.0%|
|Tony Gonzalez||Tyler Thigpen||74||128||57.8%|
Those are some big, cherry-picked-to-make-a-point, differences. There is probably a lot going on there outside of just the QB, but you get the idea. Without a change in skill, the same receiver can have very different CR numbers under different QBs.
The mere fact that QBs have varying completion rates is evidence of that. In 2017, Drew Brees completed 72% of his passes while DeShone Kizer notched less than 54%.
AIR YARD NOISE
But perhaps the biggest confounding factor of CR is how each receiver is used. Is he running deep routes or is he primarily a check down?
This takes us into the realm of air yards, which is the total distance that a football is thrown past (or short of) the line of scrimmage to the point of reception (or where the reception would have been if incomplete).
The following chart breaks out average NFL catch rate by air yards (aY).
Clearly, length matters.
There is a near perfect linear drop-off in CR the further down the field the receiver is. In fact, the relationship is so strong that statistically speaking, around 98% of the average catch rate is explained by air yards (1), leaving only 2% for other factors . . . like ball pressure (#shadow_world).
Divide a receiver’s total air yards by the number of times he was targeted and you get what we stat nerds call the average depth of target (aDOT). In simpler language, it is the receiver’s average route depth on plays where he is targeted.
I only have air yard data going back to 2009, but since then, of the top 50 TEs by targets, Doyle ranks #1 in catch rate but dead last in average depth of target (4.7yds). That is not a coincidence. He has a high catch rate because he catches short passes.
The bottom line is catch rate is a bad stat and it can’t be used to compare receivers. So, is there a good receiving stat? Well, there’s good news and bad news.
Remember YPT? <imagine the sound of one item being unpinned>. The good news is that yards per target explains wins very well (high correlation) and is predictive of wins. Also, since YPT is just yards per catch multiplied by CR (YPT = rYds/Ctch * Ctch/Tgt), it encompasses all the information of CR as well. Ironically, combining two not so good stats make a single good one (Oooh mathemagic).
The bad news is that YPT it is heavily biased by air yards. When comparing YPTs, Doyle’s wee 6.56 pales in comparison to Gronk’s massive 9.93, primarily because Gronk’s aDOT is about 6 yards longer.
Seriously, are we not doing phrasing anymore?
And it gets worse. Receiving yards can be deconstructed into completed air yards and total yards after the catch: rYds = cmp_aY + ttl_YAC
And just like catch rate, YAC is also dependent on air yards, as illustrated below.
It’s not a linear function like CR, but YAC is clearly not just receiver skill. My guess is that this represents defensive choke-points about 10 yards downfield, but that is an analysis for another day.
So, while YPT is a good stat to measure a receiver’s impact on wins, it is still not something you can use to compare them directly. Frankly, I don’t believe you can legitimately compare receivers with any of the standard receiving stats. There are just too many variables.
However, there’s nothing stopping me from creating my own rating stat.
What follows is a fun exercise (yes this is fun for me) demonstrating how a comparison stat could be made. I’ll apologize up front because this section is going to get a bit mathy. For the algebraic milksops, you should skip to the results section.
Okay, are all the wussies gone? If you look back at the CR chart, you’ll notice Doyle’s 4.7 aDOT equates to an NFL average CR of about 71%, which he is outperforming (77%).
Using regressions, I can calculate CR and YAC for each receiver given the air yards of their targets. Then I just use those expected values to adjust an existing stat and determine a performance over expectations. And since CR is a crap stat, I will use YPT.
I showed you earlier that YPT is just CR multiplied by receiving yards per catch:
YPT = CR * rYds / Ctch
and also that total receiving yards can be deconstructed into completed air yards and total YAC:
YPT = CR * ( cmp_aY + ttl_YAC) / Ctch
And since total YAC per catch is just YAC then:
YPT = CR * (cmp_aY/Ctch + YAC)
And if I swap actual values with expected values, then it becomes expected YPT:
xYPT = xCR * (cmp_aY/Ctch + xYAC)
And finally, the difference between actual YPT and expected YPT is YPT over expectations:
YPToe = YPT - xCR * (cmp_aY/Ctch + xYAC)
For the expected data, I used equations other than the trend lines in the graphs(2) to calculate xCR and xYAC, but Doyle’s numbers look like this:
YPToe = 6.56 - 63.9% * (723 / 174 + 5.0)
That comes to 0.68 which is a measure of how many more yards than expected he is producing with every ball thrown his way(3). And since YPToe uses air yards as a variable that number can be directly compared to other receivers.
If this is a stat that actually captures a player skill that is related to wins and not already captured by standard YPT, then the correlation to wins (especially predictive correlation) should increase.
And it does. So, after crunching all of those numbers, I finally have an apples to some-other-fruit-that-is-closer-to-apples-than-it-was-before-all-of-this comparison of receivers. However, this is a rough draft of a stat, so treat this as more chainsaw than scalpel.
Taking the 50 top TEs and 50 top WRs by targets since 2009, here are the top 10 players in YPToe.
Top 10 YPToe
Of the 50 TEs, Doyle ranks 43rd in standard YPT, but improves to 11th place using YPToe. TY Hilton has the opposite journey. His YPT ranks 5th among WRs, but drops a few spots to 9th in YPToe.
Adding in running backs, here are the Colts receivers numbers for 2017 (at least 30 targets). Negative YPToe numbers indicate a receiver who did worse than expected .
And here are all teams ranked by their 2017 receiver’s weighted YPToe(4). Robert Woods, Todd Gurley and Cooper Kupp all had very strong results leading the Rams to a #1 spot.
1) R-squared of 0.9835 for linear regression of league average catch rate by air yards for attempts between 1 and 30 yards. This strictly linear trend does not extend to shorter or longer passes and have been excluded for illustrative purposes.
2) All regressions utilized individual play data and not the averages displayed in the charts. The dependent variable for CR is aDOT as that includes all passes but for YAC only completions matter and so avg completed air yards was used.
The trend lines do not display a universal trend and so they were broken into separate ranges to better fit the data. The CR trend had 4 ranges: <0 aDOT (positive linear), 0 aDOT (point estimate), 1 - 30 aDOT (negative linear) and >30 aDOT (negative linear). For YAC, 3 different ranges applied: <-5 (pos. linear), -4 to 25 (cubic) and >25(neg linear).
3) One additional adjustment I made was to account for QB accuracy. Receivers of highly accurate QBs (Brees, Rodgers) should be penalized a bit and receivers of the Gabbert/Sanchez persuasion should get a bump. I didn’t go into the math here, but using a similar methodology of completion rate over expectations, I created a weighted QB adjustment for each receivers’s xCR before calculating his final xYPT.
4) Receivers with less than 30 targets were assigned a YPToe of 0. YPToe for each receiver was multiplied by attempts and totaled to get a team YPToe. This was then divided by team attempts to get a weighted avg YPToe.