Advice

Modeling NFL Production in Young WRs

I first ran across RotoViz when I Googled “Principal Component Fantasy Football” — it returned the first Google hit1 — because I’m a huge nerd.

I quickly became enthralled with a fantasy football site that was all about metrics and analytics. Lo and behold, today I’m lucky enough to write for RotoViz,2 which in my humble (and almost surely biased) opinion is the best fantasy football site there is. That’s because we have groundbreaking articles such as Shawn Siegele’s 2014 piece Breakout Age is the Skeleton Key in addition to industry leading metrics like Jon Moore’s Phenom Index. Add to it the best apps on the web such as the RotoViz Screener, and we get a combination that lays the foundation for this particular article.

I’ve pulled together Moore’s Phenom Index numbers from 2004 to present, including the individual age and market share components from the linked article above, and meshed them with each receiver’s NFL statistics using the RotoViz Screener App to create a statistical model that predicts PPR fantasy points per game for receivers in their first four years in the NFL to a high degree of accuracy. I’ll show you which variables are significant in predicting success in each of the first four years of a receiver’s career, and how those variables change in significance throughout a WR’s early career. Then I’ll use these models to find the best candidates for success in 2016.

The Model Factors

I created a separate model for each of the first four seasons of an NFL receiver’s career. The factors I looked at included:

  1. Year (SEAS)3
  2. Logarithm of draft position (L.DPOS)
  3. Draft age (AGE.Z)4
  4. Final year collegiate market share (MS.Z)5
  5. PPR points per game the prior year (PPR.N)
  6. Games played the prior year (GMS.N)

For the rookie season, I obviously did not have bullets 5 and 6, but for the rest of the seasons I considered models using all six variables.

One final note, these models only use data points that were nonzero in the response. In other words, I only looked at seasons in which a player put up fantasy points. If a player didn’t play, he is not included in the results. Thus, we don’t get to see bust rates, but simply the model results for players who did play.

Here are the results.

Rookie Year Model

The rookie year model turned out to have first order terms for all four of the variables, as well as interaction terms for AGE.Z with SEAS and L.DPOS with SEAS.

Term Estimate p-value
Intercept -367.866 0.0045
SEAS 0.189 0.0033
AGE.Z -0.276 0.1706
MS.Z 0.482 0.014
L.DPOS -1.689 <.0001
(SEAS-2010.15)*(AGE.Z+0.402) -0.136 0.0503
(SEAS-2010.15)*(L.DPOS-4.598) -0.115 0.0835

The way to interpret the interactions is as follows:

  • For SEAS*AGE.Z — Back in the early portion of the observed time period (closer to 2004) AGE.Z was nearly flat. So back in the day the age of the receiver didn’t affect PPR production much at all. However, in recent years AGE.Z has a huge impact on rookie year PPR production per game.
  • For SEAS*L.DPOS — Similarly, back in 2004 draft position mattered far less than it does today.

This model produced an R-squared value of only 0.298 with a RMSE of 3.357 on 318 observations. In other words, projecting rookies is really hard.

The other thing that is interesting is, by plugging in 2015 for the SEAS, we find that the AGE.Z is more important than MS.Z by a 60/40 ratio in today’s world. The Phenom Index itself uses a 50/50 ratio (weighing AGE.Z and MS.Z equally), which is relatively close and a testament to Jon Moore’s methodology that this single Phenom Index number can predict rookie year success quite well.

Second Year Model

Moving on to second year receivers, we can now add in information from their first NFL season, including the number of games played (GMS.N) and PPR points per game scored (PPR.N).

Term Estimate p-value
Intercept 0.765 0.1757
GMS.N 0.260 <.0001
PPR.N 0.461 <.0001
AGE.Z -0.686 0.0045
MS.Z 0.882 0.0002
(GMS.N-9.450)*(PPR.N-6.060) 0.063 <.0001
(AGE.Z+0.533)*(MS.Z-0.406) -0.658 0.0109

This model has an R-squared of 0.514 with a RMSE of 3.451. Now we’re talking!

The term for SEAS has dropped off, and that’s likely because we have better information — how the player performed the prior year via PPR.N. The prior year performance is extremely predictive of the following year performance, independent of the time period.6

The interaction between GMS.N and PPR.N simply says that if a player scored a lot of points per game the prior year (meaning high PPR.N), then it mattered a whole lot more if he did it over a 16 game season than if he only played in a small handful of games.

The interaction between AGE.Z and MS.Z says that if a player had poor college market share, then his draft age didn’t matter. It also says that if a player was old in his final year of college, then his market share numbers didn’t really matter. However, if a player was young and productive, that mattered immensely.

We can now see that the linear estimate for MS.Z has overtaken the linear estimate for AGE.Z, so in a player’s second year, we should give slightly more weight to his college market share than his draft age. However, prior year PPR points per game trumps all, as does rookie year playing time.

Third Year Model

Term Estimate p-value
Intercept 4.818 0.014
GMS.N 0.243 0.012
PPR.N 0.391 <.0001
MS.Z 1.029 0.0001
L.DPOS -0.874 0.0042
(GMS.N-11.348)*(PPR.N-7.596) 0.072 <.0001

This model has an R-squared of 0.513 and an RMSE of 3.641.

Notice the AGE.Z term has dropped off completely, meaning by a receiver’s third year in the NFL we shouldn’t even factor in his draft age. His draft position, NFL production, and collegiate production really tell the whole story. So in your Jordan Matthews arguments (I’m looking at you fellow RotoVizians), don’t tell me about how he produced at a young age!

The interaction term between GMS.N and PPR.N tells the same story as it does for second year receivers.

Fourth Year Model

Term Estimate p-value
Intercept 0.924 0.3075
GMS.N 0.219 0.0069
PPR.N 0.499 <.0001
(PPR.N-8.611)*(PPR.N-8.611) 0.027 0.0382

The R-squared for this model is 0.467 and the RMSE is 3.748.

For the fourth year model, any notion of draft position, age, and college production has dropped off. This is the point in a receiver’s career at which he has fully established (or failed to establish) himself as an NFL receiver. Prior year production and games played are all that matters among the variables I looked at. I’m sure there are other variables from the RotoViz screener that you can look at and use to improve the fourth year model. Play around and let me know what you find!

Modeling Young WRs in 2016

Here are the predicted results for 2016 using the four models for receivers in years 1-4 in the NFL:

PLAYERGMS.NPPR.NNFL.YRPIAge.ZMS.ZDPOSL.DPOSPred.PPR.N1
Odell Beckham1521.332.34-1.50.84122.4819.09
Amari Cooper1613.523.9-2.191.7271.9518.67
DeAndre Hopkins1620.642.86-2.150.71273.3018.65
Allen Robinson161934.44-2.392.05614.1118.47
Keenan Allen820.442.98-2.030.95764.3316.66
Brandin Cooks1615.933.42-2.480.93203.0016.04
Jarvis Landry1616.832.6-1.571.03634.1415.79
Jordan Matthews1614.533.41-1.172.24423.7415.72
Sammy Watkins1316.832.92-2.170.7541.3915.20
Mike Evans1514.232.75-2.380.3771.9514.43
John Brown15143-0.111.381.28914.5112.99
Stefon Diggs1311.522.42-1.570.851394.9312.79
Allen Hurns1515.131.16-0.420.752705.6012.22
Tyler Lockett169.721.63-0.261.36914.5112.10
Corey Coleman12.17-1.111.05152.7111.97
Devin Funchess166.823.38-2.11.28533.9711.83
Laquon Treadwell12.22-2.180.04233.1411.62
Jamison Crowder168.222.12-1.071.051324.8811.47
Tavon Austin1612.341.17-0.790.3782.0810.95
Will Fuller11.94-0.881.07213.0410.94
DeVante Parker811.722.96-0.612.34132.5610.30
Donte Moncrief1610.832.24-2.33-0.1904.509.97
Tyler Boyd12.75-1.51.26554.019.42
Dorial Green-Beckham147.921.88-1.98-0.11323.479.34
Terrance Williams169.640.580.851.44744.309.25
Markus Wheaton169.341.16-0.680.48794.379.09
Josh Doctson10.20.660.86223.099.08
Michael Thomas OSU11.28-0.7430.54473.858.64
Martavis Bryant1115.73-0.12-0.53-0.661184.778.58
Davante Adams138.132.4-1.650.75533.978.51
Robert Woods148.641.7-1.98-0.28413.718.29
Pharoh Cooper13.1-1.881.221174.768.03
Jaelen Strong104.822.59-1.750.84273.307.82
Sterling Shepard10.120.450.57403.697.75
Leonte Carroo12.26-0.621.64864.457.61
Paul Richardson1532.75-0.881.87453.817.55
Tajae Sharpe13.2-1.651.561404.947.52
Kenny Stills155.941.64-2.01-0.381444.977.36
Nelson Agholor134.421.75-0.990.75564.037.25
Quinton Patton155.140.79-0.130.661284.857.10
Albert Wilson146.832.37-1.151.212705.607.08
Marquess Wilson117.342.62-2.450.172365.467.03
Ryan Grant125.131.010.571.591424.966.91
Matt Hazel1030.6600.661905.256.81
Marqise Lee94.931.02-0.460.56393.666.75
Ricardo Louis11.34-0.810.531144.746.61
Josh Huff135.930.7-0.330.37864.456.57
Rashard Higgins12.49-1.411.081725.156.55
Jeff Janis5231.750.051.82365.466.45
Demarcus Robinson10.77-1.36-0.591264.846.42
Jared Abbrederis5430.910.581.491765.176.25
Cody Latimer6331.82-1.450.37564.036.08
Justin Hunter9641.23-0.990.24343.536.08
Malcolm Mitchell10.98-0.050.931124.726.03
Phillip Dorsett114.420.64-0.570.07523.955.94
Rashad Greene94.520.82-0.250.57774.345.87
Brice Butler57.64-1.460.45-1.022095.345.84
J.J. Nelson95.92-0.330.22-0.122705.605.83
Kevin Norwood103-2.61.94-0.661234.815.83
Thomas Duarte11.69-1.95-0.262315.445.78
Michael Thomas SM11.81-1.250.562065.335.70
Jalin Marshall11.85-2.29-0.442705.605.69
Chris Conley94.820.28-0.35-0.071585.065.59
Devin Street102.430.950.271.211464.985.58
Jaron Brown103.14-2.170.51-1.662705.605.49
Ty Montgomery58.420.4-0.62-0.221224.805.48
Tre McBride24.821.7-0.461.241154.745.46
Demarcus Ayers11.98-1.130.852295.435.46
Rashad Ross56.54-1.180.44-0.742705.605.39
Stedman Bailey84.541.53-0.421.12924.525.39
Chris Moore1-0.540.06-0.491074.675.34
Kenbrell Thompkins65.64-2.912.11-0.82705.605.28
Marlon Brown92.840.42-0.91-0.482705.605.22
Cameron Meredith63.820.78-0.240.542705.605.18
Justin Hardy94.52-0.160.610.461004.615.17
Myles White92.44-1.110.27-0.842705.605.15
Aaron Burbridge11.68-0.531.152135.365.13
Jordan Payton10.5-0.180.331545.045.13
Quincy Enunwa114.931.3-1.030.282095.345.09
Jonathan Krause21.630.62-0.6202705.605.07
Aaron Dobson64.540.31-1.06-0.76594.084.95
Braxton Miller1-1.80.66-1.14854.444.92
Hunter Sharp11.88-0.910.982705.604.90
Jordan Williams11.8-0.950.852705.604.88
Taylor Gabriel124.330.050.390.452705.604.83
Marquez North10.32-2.01-1.692705.604.79
Tyrell Williams28.520.94-0.590.342705.604.76
Charles Johnson63.640.071.471.542165.384.72
De'Runnya Wilson10.99-1.34-0.352705.604.72
Imani Davis11.18-1.080.12705.604.65
Cody Core1-0.09-0.88-0.971995.294.64
Devin Smith73.82-0.460.38-0.09473.854.64
Russell Shepard71.74-1.53-0.43-1.972705.604.61
Jenson Stoshak11.39-0.830.562705.604.61
Dennis Parks10.92-1.19-0.272705.604.59
Sammie Coates12.120.82-0.830413.714.59
Daniel Braverman11.26-0.2612305.444.59
Brandon Coleman165.530.44-1.09-0.662705.604.58
Kolby Listenbee1-0.14-0.63-0.761925.264.56
Bruce Ellington102.93-0.240-0.241064.664.51
Darren Waller31.320.36-0.190.171525.024.41
Javontee Herndon76.430-0.1-0.12705.604.40
Carlos Harris11.46-0.391.082705.604.39
Kenny Lawler10.03-1.09-1.072435.494.35
Daje Johnson10.74-0.89-0.152705.604.33
Marcus Johnson10.26-1.22-0.952705.604.30
KJ Maye10.87-0.720.152705.604.29
Geronimo Allison10.99-0.60.392705.604.28
Keith Mumphery83.42-1.180.09-1.092705.604.25
Jaydon Mickens10.54-0.89-0.352705.604.23
Mario Alford12.520.43-0.72-0.292705.604.22
Max McCaffrey10.42-0.97-0.562705.604.22
Bryce Treggs10.32-0.92-0.612705.604.14
Corey Fuller42.940.090.010.11715.144.14
Quinshad Davis10.18-1-0.812705.604.13
Roger Lewis10.82-0.440.382705.604.10
Trevor Davis1-1.150-1.151635.094.09
Geremy Davis31.42-0.090.540.452545.544.08
Amara Darboh10.41-0.64-0.232705.604.02
Malachi Jones10.21-0.8-0.592705.604.02
Devin Lucien10.320.030.352255.424.02
Jehu Chesson10.42-0.54-0.122705.603.97
Vince Mayle102-1.21.19-0.011815.203.89
Cordarrelle Patterson41.140.28-0.8-0.51293.373.89
Marquise Goodwin14.44-1.08-0.44-1.52784.363.83
DeAndrew White13.82-1.550.8-0.752705.603.78
Paul McRoberts11.480.712.192705.603.74
Rashon Ceaser10.51-0.030.482705.603.71
Bralon Addison10.13-0.33-0.22705.603.71
Devin Fuller1-0.92-0.64-1.562385.473.68
Teddy Ruben10.40.010.412705.603.64
Darius Powe1-0.58-0.78-1.362705.603.63
Joe Hansley1-0.46-0.66-1.132705.603.61
Byron Marshall1-0.58-0.68-1.262705.603.57
Robby Anderson10.280.170.462705.603.49
Jakeem Grant1-0.840.76-0.081865.233.48
Donovan Harden1-0.22-0.2-0.432705.603.46
D.J. Foster1-0.6-0.43-1.032705.603.41
Mose Frazier1-0.39-0.18-0.572705.603.37
Dezmon Epps11.010.971.982705.603.36
K.J. Brent1-0.34-0.1-0.442705.603.34
Kasen Williams12.32-0.84-0.47-1.312125.363.32
Durron Neal1-0.76-0.37-1.132705.603.30
Macgarrett Kings Jr.1-0.63-0.26-0.892705.603.30
J.D. McKissic1-0.48-0.13-0.612705.603.29
Jemond Hazely1-0.43-0.06-0.492705.603.28
Bennie Fowler132.83-0.430.05-0.382705.603.22
Chris Harper30.520.56-1.6-1.031935.263.21
Jamal Robinson10.110.530.652705.603.19
Rodney Smith10.54-1.440.32-1.112705.603.19
Jay Lee1-0.570.02-0.552705.603.16
Shaq Washington1-0.540.07-0.472705.603.15
Nelson Spruce10.060.650.712705.603.09
Cayleb Jones1-0.370.32-0.052705.603.08
Jordan Thompson1-0.760.01-0.752705.603.08
Keyarris Garrett10.130.870.992705.602.99
Davonte Allen1-0.50.37-0.132705.602.99
Mekale McKay1-1.06-0.09-1.162705.602.99
Alex Erickson1-0.090.740.652705.602.97
Miles Shuler1-1.33-0.25-1.592705.602.95
Paul Turner1-0.870.17-0.72705.602.93
Cameron Dickerson1-1.5-0.3-1.82705.602.90
Ryan Longoria1-0.70.44-0.262705.602.85
T.J. Thorpe1-1.10.12-0.982705.602.85
Danny Anthrop1-1.130.11-1.022705.602.84
Tevaun Smith1-0.710.48-0.232705.602.82
Jarvis Bentley1-1.110.27-0.842705.602.75
Melvin Ray1-1.230.22-1.012705.602.73
Simms McElfresh1-0.980.5-0.492705.602.68
Antwane Grant1-1.430.25-1.182705.602.61
Autrey Golden1-1.830.05-1.782705.602.54
Dom Williams1-1.140.63-0.512705.602.53
Gary Chambers1-1.60.34-1.262705.602.48
Tommylee Lewis1-1.110.78-0.342705.602.45
Charone Peake1-1.680.8-0.872415.482.44
Devon Cajuste1-1.580.48-1.12705.602.40
Richard Mullaney1-1.770.41-1.362705.602.35
Ryan Burbrink1-1.730.49-1.242705.602.33
Alonzo Russell1-1.390.86-0.532705.602.27
Casey Martin1-1.420.86-0.562705.602.26
Stephen Anderson1-1.920.48-1.452705.602.24
Kenneth Scott1-1.50.89-0.612705.602.20
Chris Shillings1-2.010.51-1.52705.602.18
Maurice Harris1-2.090.73-1.372705.602.01
Tevin Jones1-2.240.62-1.622705.602.00
Pig Howard1-2.590.36-2.232705.601.99
Jamal Turner1-2.560.48-2.082705.601.93
DeAndre Reaves1-1.561.32-0.242705.601.92
Danny Woodson II1-20.97-1.042705.601.91
Brandon Sheperd1-2.360.71-1.652705.601.89
Amir Carlisle1-2.260.87-1.382705.601.85
Bobo Beathard1-2.280.91-1.372705.601.81
D'haquille Williams1-1.931.28-0.652705.601.76
Brandon Swindall1-2.460.87-1.592705.601.75
Mitch Mathews1-2.011.37-0.652705.601.67
Max Morrison1-2.381.18-1.22705.601.61
KJ Myers1-30.7-2.32705.601.59
Jordan Fredrick1-2.770.92-1.852705.601.57
Joe Morrow1-2.840.94-1.92705.601.53
Ed'marques Batties1-1.831.81-0.022705.601.50
DeAnthony Arnett1-2.661.14-1.522705.601.49
Marvin Shinn1-2.41.77-0.632705.601.25
Kyle Klein1-2.291.9-0.42705.601.22
Bubba Poole1-3.261.41-1.852705.601.05
Quenton Bundrage1-2.572.03-0.552705.601.01
Von Pearson1-3.132.28-0.852705.600.60
Shamier Jeffery1-3.662.04-1.622705.600.48
Tres Houston1-3.423.12-0.32705.60-0.04

A few quick hits:

  • Amari Cooper projects for the second most PPR points per game among all receivers in years 1-4. Maybe his ADP of WR13 is too low.
  • Alternatively, Mike Evans projects as only the 10th best WR in this group of receivers. So his WR10 ADP could be a bit too high when we add in veterans like Antonio BrownJulio Jones, and Dez Bryant.
  • Corey ColemanLaquon TreadwellWill Fuller, Tyler Boyd and Josh Doctson project as the top five rookie WRs by the rookie model.
  • I see you Stefon Diggs!
  • Donte Moncrief and DeVante Parker are WR2s on their respective teams and the model doesn’t like them.

You can find the full data set that I built the models from below.

PLAYERSEASreRECS.Q1reTDS.Q1reYDS.Q1GMS.NPPR.NYEAR.Q1reRECS.Q1.N1reTDS.Q1.N1reYDS.Q1.N1GMS.Q1.N1PPR.Q1.N1PIAge.ZMS.ZDraftL.DPOS
Dante Ridgeway2005202614.61000005.26-23.261925.26
Allen Robinson20144.80.254.81011.5150.987.516194.44-2.392.05614.11
Nate Washington20050001012.20.239167.64.41-1.293.122705.60
Demaryius Thomas20102.20.228.3106.213.20.455.11011.24.2-0.553.65223.09
Dez Bryant20103.80.546.81211.414.30.662.11514.14.08-1.52.58243.18
Amari Cooper20154.50.467.61613.51000003.9-2.191.7271.95
Kenny Britt20092.80.246.7158.713.80.870.51115.83.9-2.471.44303.40
Stephen Hill20121.90.322.9115.8120.128.5125.43.73-2.021.71433.76
Hakeem Nicks20093.40.455.61411.516.10.880.91319.23.7-1.721.99293.37
Calvin Johnson20073.20.350.41510.614.90.883.21617.63.69-1.392.320.69
Mike Williams USC20052.10.12514511.10.114.173.43.64-2.80.84102.30
Rueben Randle20121.90.329.8106.712.60.438.2168.63.64-2.061.58634.14
Dezmon Briscoe201030.546.5210.712.70.529.8138.43.54-2.411.131915.25
Brandin Cooks20145.30.3551013.915.20.671.11615.93.42-2.480.93203.00
Jordan Matthews20144.20.554.51612.615.30.562.31614.53.41-1.172.24423.74
Vincent Jackson20051019.73312.10.534.8138.53.39-0.592.8614.11
Devin Funchess20151.90.329.6166.81000003.38-2.11.28533.97
Danario Alexander20102.50.138.287.112.60.243.1108.13.29-1.232.052705.60
Devin Thomas20081.4010.9113.511.90.225135.83.25-1.531.71343.53
Earl Bennett20080001013.40.144.8168.63.15-1.921.23704.25
Sidney Rice20072.60.333128.311.20.311.8124.43.1-2.410.69443.78
Keenan Allen20134.70.570.2151515.50.355.91412.82.98-2.030.95764.33
DeVante Parker20153.20.461.8811.71000002.96-0.612.34132.56
Chris Henry20052.40.532.5138.412.80.746.51311.62.95-0.971.98834.42
Dwayne Jarrett20071.2014.652.911.2014.982.72.93-2.440.49453.81
Ramses Barden2009101612.611.201642.82.930.533.47854.44
Sammy Watkins20144.10.461.41612.514.60.780.51316.82.92-2.170.7541.39
DeAndre Hopkins20133.20.150.116914.80.475.61614.62.86-2.150.71273.30
Mike Evans20144.50.870.11516.314.90.280.71514.22.75-2.380.3771.95
Paul Richardson20142.60.124.6115.611040152.75-0.881.87453.81
Braylon Edwards20053.20.351.21010.113.80.455.21611.62.72-0.712.0231.10
Mario Manningham20080.704.360.914.10.458.71412.12.7-1.011.7954.55
Randall Cobb20111.70.126.4144.915.30.563.61515.82.68-2.380.3644.16
Greg Little20113.80.144.3169.113.30.240.1168.92.65-2.130.52594.08
Marquess Wilson20130.704.331.112.3019.974.32.62-2.450.172365.46
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Jaron Brown20151.10.114.4103.1300000-2.170.51-1.662705.60
Adam Jennings200800020300000-2.270.67-1.61845.21
David Nelson2012203115.1330.235.2127.5-2.280.7-1.582705.60
Yamon Figurs20090.503.520.83108.521.7-2.391.61-0.78744.30
Julian Edelman20110.705.361.332.60.429.488.4-2.470.09-2.382705.60
Devin Hester20083.50.244.3159.534.40.258.21311.6-2.910.72-2.2574.04
Kenbrell Thompkins20152.8027.565.6300000-2.912.11-0.82705.60
Trindon Holliday2012201713.7300010.7-3.581.29-2.292705.60
Jordan Matthews20151614.523.41-1.172.24423.74

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  1. Try it!  (back)
  2. Thanks Charles Kleinheksel (insert eyeroll GIF here)…  (back)
  3. The NFL has trended toward a more passing oriented league over the last decade.  (back)
  4. I used the z-score as defined in Jon Moore’s Phenom Index article.  (back)
  5. Again the z-score as used in Phenom Index.  (back)
  6. That makes sense intuitively, good players tend to be good the next year, regardless of what era they played in  (back)
By RotoDoc | @RotoDoc | Archive

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  1. Jason says:

    You're killing it today Doc. My one question is a guy like Terrance Williams. He has some things working against him (Age, potential low volume pass offense, etc). But I just can't give up on him as a post-hype sleeper.

    In year 4 and even year 3, his old age should've have hurt him anymore according to your model. However, he seemingly didn't produce as well in year 3 due to Romo being hurt and basically everything going wrong for the Cowboys. If you look at weeks 1 and 11, the two weeks Romo played the full game, Williams scored 11 and 17 PPR points in both of those weeks. This is an incredibly small sample, but I really think Williams has a solid floor of a lower-end WR3 this year with TD upside playing alongside one of the most efficient QBs in the NFL. His OC is a plus too.

    What is the call on Williams then? The Cowboys defense is shaping up to be awful this season. Is there any way to factor in a lost type of season like Williams had in his year 3 into a model like this?

  2. Looking forward to the answer to this one. Williams is really interesting because he benefits in the 3rd year model from his massive final year market share being considered independent of age, since he was a 22-year-old senior. But then gets dinged in the 4th year from the quarterback nightmare the Cowboys had. I suppose you could run the 4th year model again, using his projection from the 3rd year model as his PPR.N. It's probably a wash though, because he probably lost a TD or two from Romo's absence, but gained targets and yards from Dez's injury.

  3. Jason says:

    I think the past bet on this one is just to use human intuition. I'll need to check out more of Williams in the preseason, but I am willing to take a flier on him in rounds 13-15 because I think he has a solid floor of a guy who can be a safe bye week fill-in (as opposed to a guy like Coates or Perriman who are extremely boom or bust). Laurent Robinson had a 10+ TD season playing with Romo and Dez so it's also possible the Cowboys are very efficient and Williams has a season like that where he gets some random type TD upside over the course of the full season.

    Williams is also in a contract year if you pay attention to those sorts of things. Going down narrative street here, but he will likely want to do everything in his power to get one (reasonably) big contract especially given his age. The Cowboys may also pepper him with more targets to see if they want to keep him or give up on him as their long-term WR2.

  4. I'm not sure where Amari ranks in the pantheon on the young WRs in the NFL. I know he profiles as a more athletic Julio Jones and broke out at a very early age but I think another year is needed to determine just how special he may or may not be. The headwinds against him this year is Oakland might be decent (not playing from behind). The head coach is a former DC and the offense is run-based, and the run game with Latavius and now DeAndre Washington should continue being good. Then there are the 5 games against Denver, KC and Carolina. I think I'd still take ARob against him straight up in dynasty.

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