When Does Average Depth of Target Stabilize for Tight Ends?

Back in January, I tweeted out this poll:

Admittedly, I was quite surprised that RBs won. Given that quarterbacks and wide receivers (and tight ends to a certain extent) are far more involved in the passing game on average, I actually thought RBs would garner the fewest votes, not a winning plurality. If I were to attempt a journey into the minds of voters, a more statistics-savvy group than the general population it’s worth noting, I’m guessing a vote for RBs was based on the idea that there’s less variation in distance from one target to the next for this position, and less variation (generally) equals more consistency. If you voted for RB via different logic, let me know in the comments or on Twitter.

Whatever the reasoning (and the validity thereof), findings I’ve presented so far have already rendered the conclusion incorrect. Namely, whereas it takes 14 games (or 30 targets) for a RB’s average depth of target (aDOT) to stabilize, it takes 10 games (or 286 aimed throws) for QBs and only 4 games (or 24 targets or 117 routes run) for WRs. However, although we now know that RB aDOT doesn’t stabilize fastest, the question at the heart of the poll remains outstanding because I have yet to present the result for TEs. Today’s post achieves that end.


As always, below is the standard procedure I use to determine a stat’s stabilization point; this time applied to TE aDOT:

  1. I collected aDOT data for all TEs that played at least 8 games for the same team from 2006 to 2016.
  2. Starting with TEs that played 8+ games for the same team, I randomly selected two sets of 4 games for each TE and calculated their aDOT in both sets.
  3. I calculated the split-half correlation (r) between the two randomly-selected sets of games.
  4. I performed 25 iterations of Steps 2 and 3 so that r converged.
  5. I repeated Steps 2-4 in 8-game intervals, from 16+ games all the way to 72+ games.
  6. For each “games played” interval, I calculated
    1. the number of games at which the aDOT variance explained, R2, would mathematically equal 0.5.1
    2. the “true” aDOT for a hypothetical TE that’s had an observed performance of 8.50 aDOT through that number of games.2
  7. I calculated a weighted average of the results from Step 6.3

Results and Discussion

Below is the familiar stability table, this time for TE aDOT:

GamesnrR2 = 0.50Avg aDOTObs 8.50 aDOT
Wtd Average98.148.32

Focusing on the bottom “Wtd Average” row, we see that TE aDOT takes 9 games to stabilize. Given an average of 3.8 targets and 18.2 routes run per game in my sample, 9 games is equivalent to 32 targets and about 157 routes. With this information, we now have our answer to the poll question: At 4 games (or 27 targets or 117 routes), WR aDOT stabilizes the fastest among offensive skill positions. Furthermore, we also now know that the position picked by poll voters to finish first actually finished last.

Although TEs didn’t win, adding their results to those of QBs, RBs, and WRs reveals two points to ponder. First, the fact that WRs and TEs exhibit more stable aDOTs from game to game than RBs suggests that the depth of “standard” targets within the context of an offensive scheme (i.e., primary or secondary reads) are more consistent than the depth of “non-standard” targets (i.e., tertiary reads aka dumpoffs). Conceptually, this makes sense, as the vast majority of WR and TE routes are designed, taught, and practiced with a specific depth in mind, whereas the vast majority of RB routes are of a nebulous “find open space in the short zone, wherever it may be” variety.4 Incidentally, this hypothesis would also explain why WRs and TEs have a lower aDOT stabilization point than QBs: Unlike the crystal clear route-design/target-depth aims of the former, the latter has their target depth waters muddied by the cloudiness of RB routes.

The second TE result worth pondering is the inverse of what I discussed in my RB aDOT post. To refresh memories, there was a trend in the RB results table that suggested aDOT decreases as tenure on the same team increases. In today’s table, we see the exact opposite trend for TEs: From 8 to 48 games, the “Avg aDOT” column steadily increases. The point to ponder here is the possibility of an emergent survivor effect. It may be the case that teams cut bait on pass-oriented RBs before they cut bait on pass-oriented TEs. Or maybe a RB’s receiving skill decreases with age, whereas a TE’s increases. Or maybe I just have too much Tony Gonzalez, Antonio Gates, and Jason Witten in my data set.

My waxing theoretical/hypothetical done, here’s True aDOT for TEs that played at least 25 percent of snaps in 2016 per Pro Football Focus:

PlayerTmGTargaDOTRkTrue aDOTRk
Rob GronkowskiNE83615.1111.61
Greg OlsenCAR1612211.8310.62
Erik SwoopeIND92111.9210.23
Delanie WalkerTEN159610.649.84
Jimmy GrahamSEA169310.279.65
Coby FleenerNO168010.199.56
Cameron BrateTB157810.1109.57
Ed DicksonCAR101710.459.58
Vance McDonaldSF104310.369.49
Jared CookGB105010.289.310
Hunter HenrySD134710119.311
Garrett CelekSF13479.9129.312
Vernon DavisWAS15599.8149.313
Austin HooperATL11259.9139.214
Tyler EifertCIN8399.8159.115
Tyler KroftCIN6129.8168.916
Levine ToiloloATL13189.2178.917
Clive WalfordOAK15499188.818
Zach ErtzPHI141019198.819
Dwayne AllenIND14489208.820
Mychal RiveraOAK10249218.721
Nick O'LearyBUF10139228.722
Antonio GatesSD13858.8248.623
Darren WallerBLT7158.9238.624
Richard RodgersGB15438.7258.625
Charles ClayBUF15818.6268.526
Brandon MyersTB7148.6278.527
Eric EbronDET13858.4288.428
Jordan ReedWAS12858.3298.329
Trey BurtonPHI14588.1308.230
Jermaine GreshamARZ16588.1318.231
Julius ThomasJAX9478328.232
Anthony FasanoTEN9128338.233
Daniel BrownCHI6207.9358.234
Luke WillsonSEA10198348.235
Virgil GreenDEN10347.7378.036
C.J. FiedorowiczHST15827.8368.037
Gary BarnidgeCLV16777.7387.938
C.J. UzomahCIN8377.4427.939
Ryan GriffinHST16727.6397.940
Demetrius HarrisKC12307.5407.941
Jesse JamesPIT16577.5417.842
Zach MillerCHI10627447.643
Kyle RudolphMIN161207.2437.644
Jacob TammeATL8306.7497.645
Josh HillNO8196.7507.646
Dion SimsMIA13347457.547
Tyler HigbeeLA11266.9467.548
Travis KelceKC161146.9477.449
Jason WittenDAL15916.8487.450
Ben KoyackJAX9246.3547.351
Jack DoyleIND16726.6517.252
Will TyeNYG16686.6527.253
Dennis PittaBLT161166.5537.154
MarQueis GrayMIA8165.8577.155
Darren FellsARZ9175.9567.156
Brent CelekPHI10175.8587.057
Martellus BennettNE16666556.858
Lance KendricksLA16745.5596.559
Marcedes LewisJAX10284.8606.460
Brandon BostickNYJ8102.8615.761

DT : IR :: TL : DR

At the end of this reliability journey, it turns out that aDOT for TEs (9 games) stabilizes faster than for RBs (14 games) and QBs (10 games), but slower than for WRs (4 games). Taken together, these results suggest that RB aDOT muddies the otherwise pristine waters of QB aDOT based on WR aDOT and TE aDOT. In addition, in direct contrast to what I found for RBs, TEs seem to exhibit an aDOT survivor effect such that the lengthier their aDOT, the lengthier their tenure with a team.

Now that I’ve cycled through aDOT, next comes my series of analyses regarding the reliability of yards after catch (YAC) for QBs, RBs, WRs, and TEs. I hope you stay tuned.

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  1. The formula is (Games/2)*[(1-r)/r]

  2. The formula is [(Observed Performance * Games) + (League-Average Performance * Stabilization Point)] / (Observations + Stabilization Point)  

  3. Weighted by group size. 

  4. Fifteen yards is the NFL official scoring threshold for “short” targets, and less than one percent of games in my RB sample (104 of 15,411) had an aDOT of 15 yards or more.  

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