Gabriel and Hogan Topped 2016’s Receiving Efficiency Results

Last offseason I introduced my metrics YPCOE and YPTOE, which stand for Yards Per Carry/Target Over Expectation respectively. I used them to identify season-long regression candidates for rushing and receiving efficiency, and it successfully identified Davante Adams and Dez Bryant as bounce-back candidates, while also correctly identifying Tyler Lockett and Sammy Watkins as players likely to regress.

I’m going to do that again this year — for both rushing and receiving — but I’m going to expand upon what I did last year, because I’ve made significant improvements to the methodology, which has led to new insights.

Methodological Changes

In last year’s article, I simply regressed receiving TD rate (reTDRT) against receiving fantasy points over expectation per attempt (reFPOEPA). I did this to remove the TD part of the fantasy points over expectation so that we’re solely looking at yards over expectation per target. This gave some wonky results, which were both field position and attempt dependent. Instead, I built a neural network model to regress targets, TDs, and field position against receiving fantasy points over expectation (reFPOE), removing the per-attempt designation, since I’m now also including targets as a factor in the model.

Yes, reFPOE includes field position in it, but I found the TD adjustment part of the reFPOE calculation was actually under-fit (which isn’t a bad thing, it’s better to be conservative), so my model is a bit more aggressive on the field position part.

The other thing I did, which will be for part two of this series, is to see if YPTOE is depth dependent as well (it is). Using Josh Hermsmeyer’s air yards database, I depth adjusted the YPTOE results, to get depth-adjusted YPTOE (da.YPTOE — someone help me think of a better name for this metric).

Finally, I noticed there are certain players whose YPTOE and reTDRT numbers are somewhat “sticky,” i.e. somewhat repeatable from year to year. I will dive into this more at the end of this series. I believe (but can’t be sure yet), by depth adjusting YPTOE, plus making one other adjustment, we’ll have an efficiency metric that is somewhat stable from year to year.

Anyway, let’s dive into the 2016 results for the non-depth-adjusted version of this metric.

2016 Receiving Efficiency Results

Like last year, I grabbed the z-scores for both reTDRT and YPTOE and summed them up (Sum.Z) in order to identify which players were the most efficient in a combination of both metrics. Here are the results for all players with at least 48 targets (16 games multiplied by three targets per game).

Taylor GabrielATL51611.8%49.83.511.682.123.79
Chris HoganNE5846.9%34.24.942.440.793.23
Martellus BennettNE7379.6%
Hunter HenrySD53815.1%350.24-0.072.842.77
Terrance WilliamsDAL6146.6%
Adam ThielenMIN9255.4%46.13.971.920.292.21
Michael ThomasNO12197.4%57.12.631.210.962.17
Dwayne AllenIND52611.5%25.50.25-
Mark IngramNO5846.9%14.72.531.150.791.94
Doug BaldwinSEA12575.6%51.72.971.390.351.74
Julio JonesATL12964.7%
Malcolm MitchellNE4848.3%
Cameron BrateTB8289.8%31.40.430.041.621.65
Jimmy GrahamSEA9566.3%32.42.341.050.601.65
Brandin CooksNO11786.8%51.61.890.810.771.58
Devonta FreemanATL6523.1%
Jamison CrowderWAS9977.1%
Jack DoyleIND7556.7%221.520.620.711.33
Eli RogersPIT6634.5%20.12.881.34-0.041.30
Rishard MatthewsTEN10898.3%37.90.440.041.221.27
Melvin GordonSD5723.5%153.621.73-0.471.26
Zach MillerCHI6446.3%18.51.640.680.581.26
Jordy NelsonGB152149.2%50.9-0.06-0.221.471.25
Davante AdamsGB121129.9%44.3-0.48-0.451.661.21
DeMarco MurrayTEN6734.5%8.52.591.19-0.071.12
Theo RiddickDET6757.5%12.50.640.150.971.11
Travis KelceKC11743.4%
Cole BeasleyDAL9855.1%27.11.940.840.171.01
Vernon DavisWAS5923.4%
Mohamed SanuATL8144.9%13.61.970.860.110.96
Jordan ReedWAS8966.7%
Kenny StillsMIA81911.1%39.7-1.79-1.151.960.81
Delanie WalkerTEN10276.9%23.90.430.040.780.81
Anquan BoldinDET9588.4%8.9-0.48-0.451.250.80
Travis BenjaminSD7545.3%21.31.330.520.250.77
Travaris CadetNO5447.4%5.9-0.04-0.210.950.73
Tyrell WilliamsSD11975.9%20.20.830.250.450.70
Cameron MeredithCHI9744.1%
Antonio BrownPIT154127.8%48.5-0.36-0.381.070.68
Dez BryantDAL9688.3%22.9-0.67-0.551.220.67
A.J. GreenCIN10044.0%
Willie SneadNO10443.8%
Tyreek HillKC8367.2%13.2-0.32-0.360.890.53
Steve SmithBAL10254.9%181.140.410.090.51
Jerick McKinnonMIN5323.8%8.61.890.81-0.360.45
Brandon LaFellCIN10765.6%13.70.490.070.350.42
DeSean JacksonWAS10044.0%13.81.630.68-0.260.41
Jarvis LandryMIA13143.1%30.32.341.05-0.680.38
Kenny BrittLA11154.5%
James WhiteNE8655.8%15.40.21-0.080.430.34
Pierre GarconWAS11432.6%15.12.641.21-0.880.34
David JohnsonARI12043.3%13.61.990.87-0.550.32
Chris ThompsonWAS6223.2%
DeVante ParkerMIA8844.5%14.90.860.27-0.040.22
T.Y. HiltonIND15563.9%22.21.350.53-0.320.21
Donte MoncriefIND56712.5%7.8-3.55-
Phillip DorsettIND5923.4%4.71.720.72-0.530.20
Marvin JonesDET10343.9%
Amari CooperOAK13153.8%111.310.50-0.340.16
Kelvin BenjaminCAR11776.0%13.8-0.24-0.320.480.16
Randall CobbGB8444.8%5.70.580.120.040.16
Mike WallaceBAL11743.4%17.71.590.65-0.510.14
Odell BeckhamNYG169105.9%20.1-0.28-0.340.460.12
Andrew HawkinsCLE5335.7%-2.7-0.14-0.270.370.10
Stefon DiggsMIN11232.7%
Sterling ShepardNYG10587.6%16.8-1.42-0.951.010.06
Le'Veon BellPIT9422.1%
Dontrelle InmanSD9744.1%120.760.21-0.210.00
LeSean McCoyBUF5811.7%9.12.791.29-1.34-0.05
Darren SprolesPHI7122.8%3.71.670.70-0.79-0.09
Demaryius ThomasDEN14453.5%-
Zach ErtzPHI10643.8%1.70.820.24-0.36-0.12
Coby FleenerNO8233.7%-6.20.890.28-0.41-0.13
Kyle RudolphMIN13275.3%-6.7-0.34-0.370.24-0.13
Greg OlsenCAR12932.3%
Quincy EnunwaNYJ10543.8%-3.70.640.15-0.34-0.20
Mike EvansTB171127.0%20.1-1.58-1.030.83-0.21
Larry FitzgeraldARI15164.0%-6.70.440.04-0.28-0.23
Giovani BernardCIN5112.0%
Charles ClayBUF8744.6%1.9-0.07-0.23-0.02-0.25
Michael CrabtreeOAK14585.5%0.8-0.72-0.580.32-0.26
Sammie CoatesPIT4924.1%-1.20.28-0.04-0.23-0.27
Gary BarnidgeCLE8122.5%3.81.560.64-0.95-0.32
Garrett CelekSF5135.9%1.3-1.1-0.780.45-0.33
Golden TateDET13543.0%
Jason WittenDAL9533.2%-1.80.920.30-0.63-0.33
Clive WalfordOAK5235.8%-8.1-1.05-0.750.41-0.34
J.J. NelsonARI7456.8%-0.5-1.72-1.110.74-0.37
Julius ThomasJAC5147.8%1.5-2.36-1.451.08-0.37
Bilal PowellNYJ7511.3%-2.42.551.16-1.56-0.39
Sammy WatkinsBUF5223.8%2.90.11-0.13-0.33-0.46
Marqise LeeJAC10532.9%20.740.20-0.77-0.57
Antonio GatesSD9377.5%-12.2-2.57-1.560.98-0.58
Emmanuel SandersDEN13753.6%-17.60.03-0.18-0.41-0.59
Ted GinnCAR9544.2%7.7-0.48-0.45-0.18-0.63
Cordarrelle PattersonMIN7022.9%-
Tyler LockettSEA6611.5%0.51.660.69-1.46-0.76
C.J. FiedorowiczHOU8944.5%-9.9-0.99-0.72-0.06-0.78
Brian QuickLA7733.9%-6.6-0.58-0.50-0.31-0.81
Robert WoodsBUF7611.3%-3.21.740.73-1.57-0.83
Adam HumphriesTB8322.4%-4.60.530.09-0.98-0.89
Seth RobertsOAK7756.5%-19.8-2.6-1.580.66-0.92
Jermaine GreshamARI6123.3%-8.1-0.37-0.39-0.57-0.96
Michael FloydNE7656.6%-15.4-2.79-1.680.68-0.99
Eric EbronDET8511.2%1.11.560.64-1.65-1.01
Alshon JefferyCHI9422.1%-6.70.570.11-1.13-1.02
Dennis PittaBAL12121.7%-6.110.34-1.38-1.04
Jesse JamesPIT6035.0%-11.5-1.96-1.240.13-1.11
Robby AndersonNYJ7822.6%-5.1-0.21-0.30-0.91-1.21
Jordan MatthewsPHI11632.6%-17.9-0.32-0.36-0.90-1.26
Devin FunchessCAR5846.9%-11.1-3.51-2.060.79-1.27
Breshad PerrimanBAL6634.5%-6.2-2-1.26-0.04-1.30
Tyler BoydCIN8111.2%-
Duke JohnsonCLE7400.0%-5.22.391.08-2.41-1.33
Allen HurnsJAC7633.9%-16.7-1.61-1.05-0.29-1.34
Julian EdelmanNE16031.9%-26.50.19-0.09-1.26-1.35
John BrownARI7222.8%-9.9-0.67-0.55-0.81-1.35
Jordan HowardCHI5012.0%-6.8-0.12-0.26-1.19-1.45
Isaiah CrowellCLE5300.0%-
T.J. YeldonJAC6811.5%-7.10.34-0.01-1.48-1.49
Ty MontgomeryGB5600.0%-
Victor CruzNYG7211.4%-12.50.370.00-1.53-1.52
Albert WilsonKC5123.9%-14.6-2.07-1.29-0.30-1.59
Jeremy MaclinKC7622.6%-13.7-1.07-0.76-0.88-1.64
Ryan GriffinHOU7422.7%-15-1.17-0.82-0.84-1.66
Terrelle PryorCLE14142.8%-25.3-1.31-0.89-0.78-1.67
Torrey SmithSF4936.1%-11.8-3.9-2.270.53-1.74
Allen RobinsonJAC15064.0%-41.7-2.41-1.48-0.26-1.74
Kyle JuszczykBAL4900.0%-101.450.58-2.41-1.83
Kamar AikenBAL5012.0%-13.7-0.86-0.65-1.19-1.84
Will TyeNYG7011.4%-14.3-0.32-0.36-1.50-1.87
Brandon MarshallNYJ12943.1%-47.7-1.97-1.24-0.66-1.90
Marquise GoodwinBUF6834.4%-14.4-3.09-1.84-0.10-1.94
Lance KendricksLA8722.3%-22.9-1.43-0.95-1.04-1.99
Jared CookGB5112.0%-13.4-1.16-0.81-1.21-2.02
Will FullerHOU9222.2%-23.4-1.37-0.92-1.10-2.03
Chris ConleyKC6900.0%-150.970.32-2.41-2.09
Todd GurleyLA5800.0%-10.10.820.24-2.41-2.17
DeAndre HopkinsHOU15142.6%-47-2.2-1.36-0.87-2.23
Jeremy KerleySF11532.6%-30.3-2.27-1.40-0.89-2.29
Corey ColemanCLE7334.1%-20.6-3.58-2.10-0.22-2.32
Tajae SharpeTEN8322.4%-23.9-2.34-1.44-0.98-2.42
Trey BurtonPHI6011.7%-24.1-1.82-1.16-1.37-2.53
Tavon AustinLA10632.8%-35.5-2.95-1.76-0.78-2.54
Dorial Green-BeckhamPHI7422.7%-34.6-3.23-1.91-0.84-2.75
Nelson AgholorPHI6922.9%-29.7-3.44-2.02-0.75-2.77
Jermaine KearseSEA8911.1%-49.9-1.96-1.24-1.68-2.92
Corey BrownCAR5311.9%-27-3.35-1.98-1.25-3.23
Quinton PattonSF6300.0%-22.4-1.42-0.95-2.41-3.36

First, note I’ve included all players, regardless of position. This is important later on, when I use the depth-adjusted version, but for now it’s interesting to see how all players who met the target threshold fared.

Topping the list are Taylor Gabriel, Chris Hogan, and Martellus Bennett. Notice anything about those three names? Yep, all three played in the Super Bowl. If we add Julio Jones, Devonta Freeman, Malcolm Mitchell, and Mohammed Sanu, that’s seven players inside the top 30 that were on the two Super Bowl teams.

In general, you’ll notice a lot of team grouping. All the Washington players that qualified have positive Sum.Z values. Coby Fleener was the only Saints player to post a negative number (-0.13 Sum.Z).

On the flip side, the Eagles, Texans, and Jaguars had all their players on the negative side, while the Rams had all but Kenny Britt below average. Boy, it certainly seems like QB play matters! Britt stands out head and shoulders above his Rams teammates last year. If his new team, Cleveland, has any kind of improvement in QB play (unlikely, but we’ll see what happens after the draft), he’s a player I would target heavily in drafts .


I think this is where my premise from last year’s article that receiving efficiency is largely random is somewhat flawed. If we not only depth-adjust, but QB adjust these results, we might find the best players pop out more often than not. After all, there’s a reason some players are really good year after year, independent of volume. For example, Julio Jones has been in the positive efficiency territory by the Sum.Z metric every year except one in his career. A.J. Green and Doug Baldwin are each in positive territory every year of their careers. Good QB play helps, but even adjusting for QB play, these three certainly stand out. It’s interesting to see they also each have different average air yards per target, with Jones at 14.1, Green 12.4, and Baldwin 8.9 in 2016.

Next up in the series is depth-adjusted YPTOE, which will come out tomorrow.

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