When Does Average Depth of Target Stabilize for Running Backs?

Similar to the pattern I cited with respect to quarterbacks (QB), Pro Football Focus’s (PFF) average depth of target (aDOT) rankings for running backs (RB) were mostly consistent from 2015 to 2016. For example, among 52 RBs that played at least 25 percent of team snaps in both seasons, David Johnson ranked second in 2015 (4.6 aDOT) and second in 2016 (4.6), while James Starks ranked 46th in 2015 (-1.4) and 51st in 2016 (-1.8). However, unlike at QB, there also were glaring instances of inconsistency. For example, Jerick McKinnon dropped from 15th (1.5) to 36th (-0.1), LeSean McCoy dropped from 18th (1.3) to 48th (-0.6), and Spencer Ware ascended from 47th (-1.7) to 5th (2.7).

Of course, you could argue that all three of these inconsistencies can be explained by differing offensive circumstances between seasons (e.g., McCoy’s 8 games without Watkins) and how defenses adjusted to those changing circumstances. And you’d probably be right; but therein lies the rub. You’ll recall that I prefer my analyses to focus on player games/seasons with the same team. McKinnon, McCoy, and Ware, although having played on the same team across both seasons, turn out to be examples of why. To wit, in this sample of 52 RBs, only 5 changed teams. Here are the changes to their aDOT rankings from 2015 to 2016:

  • Matt Forte moved from the Bears to the Jets. His aDOT ranking dropped from 14th (1.6) to 45th (-0.4).
  • John Kuhn moved from the Packers to the Saints. His aDOT ranking rose from 37th (-0.4) to 15th (1.3).
  • Lamar Miller moved from the Dolphins to the Texans. His aDOT ranking rose from 40th (-0.7) to 23rd (0.7).
  • DeMarco Murray moved from the Eagles to the Titans. His aDOT ranking rose from 45th (-1.2) to 29th (0.4).
  • Chris Ivory moved from the Jets to the Jaguars. His aDOT ranking rose from 50th (-2.5) to 29th (0.4).

So all five RBs in the “different team” group exhibit aDOT inconsistency, while only a handful of the 47 RBs in the “same team” group do. Got it, but I (methodologically) digress.

In light of the above, today’s reliability seeks to answer the question, “How many games/targets does it take for a RB’s aDOT to stabilize?”

Methods

I’m trying to save space for more-than-the-usual commentary, so click here for details on the procedure I used for QB split-half reliability analysis, which applies to the current RB analysis as well.

Results

Below is the usual stability table, this time with respect to aDOT for RBs. Once again, if you’re unfamiliar with how to read this table, click here.

GamesnrR2 = 0.50Avg aDOTObs 1.00 aDOT
Wtd Average140.590.80
45480.22140.640.82
83380.36140.610.80
122400.50120.670.84
161730.55130.630.81
201350.59140.490.74
24900.63140.430.72
28570.71120.410.70
32310.77100.370.68
36270.8180.300.65

Focusing as always on the “Wtd Average” row, it turns out that RB aDOT takes 14 games to stabilize, i.e., represent 50 percent skill vs. 50 percent luck. Across my entire sample, the average number of targets per game was 2.16, which means 14 games translates to approximately 30 targets.1 In addition, the “Wtd Average” row also dictates that a hypothetical RB with a 1.00 aDOT after 14 games has a “true” aDOT of 0.80 — which you’ll notice is exactly halfway between 1.00 and the weighted league average of 0.59. ((with intentional rounding, of course))

Headlines aside, there are a few subtle revelations in the table that might fly under the radar, so let me bring them to your attention via a somewhat theoretical discussion.

You’ll recall that it takes 10 games for QB aDOT to stabilize, which means RB aDOT takes longer. But of course, you (or I) probably could have guessed that before the analysis; simply via logic. Although “random vs. non-random” (aka “skill” vs. “luck”) isn’t precisely equivalent to “controllable vs. uncontrollable” (e.g., avoiding an interception is controllable, but also relies on a modicum of bad luck), it’s useful to use this “skill equals control” way of thinking as a theoretical starting point. So, when a QB receives the snap on a passing play, who is in more control of the target and its depth? The QB or the RB? Clearly, it’s the QB. Now, as will be revealed in future posts, this may not necessarily be the case for wide receivers and/or tight ends,2 but, as it relates to RBs, lack of control over targets (and target depth) is a logical explanation for why it takes longer for aDOT to stabilize.

The other statistical finding here that I hope opens up a more theoretical discussion is that, unlike the QB stability table, which showed remarkably consistent aDOTs across sample groups, aDOT for RBs clearly decreases with tenure.3 In other words, the longer a RB stays with a team, the lower his aDOT gets. The obvious question to ask here, which I don’t have an answer for at the moment, is “Is this a symptom of RB aging or a sample size effect?” In favor of the former is the sheer magnitude of the unmistakable aDOT decline in the table. Also in favor of the former is that the QB table didn’t show anything approaching such a decline despite smaller sample sizes in each “games played” group.

Returning from the theoretical back to the practical, below is a table showing actual and “true” aDOT for every running back in 2016 that played at least 25 percent of snaps per PFF:

PlayerTmGTgtaDOTRkTrue aDOTRk
David A. JohnsonARI161074.623.71
Travaris CadetNO13503.832.62
Shaun DraughnSF14373.832.43
Jamize OlawaleOAK13144.912.04
Damien WilliamsMIA15283.452.05
Spencer WareKC14422.771.86
Jalen RichardOAK16392.771.87
Duke JohnsonCLE16681.9121.58
Carlos HydeSF13322.3101.59
Chris ThompsonWAS16591.8141.410
Giovani BernardCIN10481.8141.311
Tevin ColemanATL13371.8141.312
Jerome FeltonBUF1193.261.213
Alfred BlueHOU14152.2111.114
James WhiteNE16761.3211.115
Devontae BookerDEN16371.4191.016
Doug MartinTB8161.8141.017
Kyle JuszczykBAL16421.3211.018
Devonta FreemanATL16611.2241.019
Patrick DiMarcoATL9101.9120.920
C.J. AndersonDEN7201.4190.921
Andy JanovichDEN762.590.922
Aaron RipkowskiGB10101.8140.923
Jay AjayiMIA15331.1250.924
John KuhnNO13171.3210.925
Jonathan GrimesHOU8171.1250.826
Benny CunninghamLA10201.0270.827
Jeremy LangfordCHI12240.8280.728
Rob KelleyWAS14170.8280.729
Todd GurleyLA16550.7310.730
Mark IngramNO16520.7310.731
Terrance WestBAL16400.7310.732
Wendell SmallwoodPHI12120.8280.733
Lamar MillerHOU14360.7310.734
Theo RiddickDET10620.6350.635
Jordan HowardCHI15460.6350.636
Paul PerkinsNYG13200.6350.637
Derrick HenryTEN14140.6350.638
Mike GillisleeBUF15110.5390.639
Melvin GordonSD13530.5390.540
Darren SprolesPHI15630.5390.541
Chris IvoryJAX11260.4420.542
Ryan MathewsPHI13140.3450.543
Fozzy WhittakerCAR16310.4420.544
DeMarco MurrayTEN16640.4420.545
Christine MichaelSEA9250.3450.546
Rex BurkheadCIN9200.2480.447
Ty MontgomeryGB10390.3450.448
Jacquizz RodgersTB10150.1510.449
DeAngelo WilliamsPIT8260.2480.450
Charcandrick WestKC15340.2480.451
Thomas RawlsSEA9170.0520.452
Jonathan C. StewartCAR1316-0.1530.353
LeGarrette BlountNE167-0.7710.354
Mike TolbertCAR1515-0.3630.355
DeAndre WashingtonOAK1322-0.2580.356
Jeremy HillCIN1525-0.2580.257
Matt AsiataMIN1638-0.1530.258
Bobby RaineyNYG823-0.3630.259
Kenneth DixonBAL1240-0.1530.260
Jerick McKinnonMIN1551-0.1530.261
Ezekiel ElliottDAL1537-0.2580.162
Latavius MurrayOAK1438-0.2580.163
Zach ZennerDET1121-0.5680.164
Frank GoreIND1644-0.2580.165
Tim HightowerNO1523-0.5680.166
Le'Veon BellPIT1289-0.1530.167
Isaiah CrowellCLE1647-0.3630.068
Dwayne WashingtonDET1113-1.2740.069
Matt ForteNYJ1437-0.4660.070
Kenyan DrakeMIA1310-2.076-0.171
Bilal PowellNYJ1669-0.466-0.172
LeSean McCoyBUF1554-0.670-0.273
Robert TurbinIND1533-0.973-0.274
T.J. YeldonJAX1560-0.872-0.375
James StarksGB924-1.875-0.576
Rashad JenningsNYG1341-2.377-1.177

DT : IR :: TL : DR

Based on a split-half reliability analysis, I found that it takes 14 games (or about 30 targets) for a running back’s average depth of target to stabilize. I also found that there appears to be an aging effect, such that a running back’s depth of target decreases the longer and longer he stays with the same team.

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  1. If you’re a stickler for precision, the intentionally rounded numbers here are 2.16 targets per game times 13.54 games, which equals 29.25 targets. 

  2. Hint: Design. 

  3. Read the “Avg aDOT” column from top to bottom. 

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