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7 Undervalued Wide Receivers According to Advanced Analytics

Earlier this week I introduced my wide receiver projections for 2016, which was developed using several machine learning algorithms.

Today I’m going to ensemble those projections with two more projections, those from our RotoViz Staff composite projections from the Projection Machine and the composite projections from fantasyfootballanalytics.net1 to find over and undervalued WRs — one per WR tier. We’ll start with the undervalued WRs.

The Projections

Reminder — my machine learning projections only include players who saw 30+ targets last year or played in 8+ games, who have an ADP inside the top 90 WRs. That means no rookies and no players who didn’t play enough last year like Jordy Nelson. However, I still included them in the other projection systems for you to see, but won’t consider them for evaluation in this exercise.

Here are my machine learning projections (Machine), the RotoViz Staff composite (RotoViz), and fantasyfootballanalytics.net’s projections (FFA.net):

PLAYERMachineRotoVizFFA.netAvgAvgZADPRnkDiffRelDiff
Antonio Brown19.6622.6822.9121.7521.781100.0%
Odell Beckham18.5321.8619.820.0620.223-1-50.0%
Julio Jones19.8421.2520.9620.6820.8732133.3%
A.J. Green16.9518.4216.5717.3117.5646-2-50.0%
DeAndre Hopkins18.0518.2818.4618.2618.4954120.0%
Dez Bryant14.0417.6116.6716.1116.15612-6-100.0%
Allen Robinson18.9117.1416.3817.4817.8275228.6%
Keenan Allen15.5517.7817.116.8116.958800.0%
Brandon Marshall15.9517.2616.3816.5316.749900.0%
Mike Evans16.0717.1415.6916.316.55101000.0%
Alshon Jeffery15.7216.4216.3316.1616.37111100.0%
Jordy Nelson17.4816.3716.9316.80127541.7%
Amari Cooper1516.8115.6715.8316.02131300.0%
Brandin Cooks15.1816.4915.6115.7615.97141400.0%
T.Y. Hilton14.0516.2914.8715.0715.241519-4-26.7%
Sammy Watkins16.3615.714.9115.6615.95161516.3%
Demaryius Thomas14.2416.8215.0415.3715.53171615.9%
Jarvis Landry15.0815.2215.6915.3315.54181715.6%
Randall Cobb14.8915.6414.715.0815.31191815.3%
Jeremy Maclin14.0715.3714.2214.5514.772021-1-5.0%
Donte Moncrief10.6813.713.9112.7712.792127-6-28.6%
Golden Tate13.0515.3714.6414.3514.48222200.0%
Julian Edelman15.6314.3414.2814.7515.032320313.0%
Eric Decker13.0214.1814.513.914.06242314.2%
Kelvin Benjamin12.0913.0912.5911.692529-4-16.0%
Doug Baldwin12.1913.6613.9613.2713.41262600.0%
Michael Floyd10.8713.4412.9112.4112.512731-4-14.8%
Larry Fitzgerald13.0713.8513.813.5713.772825310.7%
Jordan Matthews13.3914.6613.0613.7113.932924517.2%
Tyler Lockett11.0711.1112.2611.4811.653040-10-33.3%
Marvin Jones9.712.9611.9511.5411.63138-7-22.6%
John Brown11.9912.4711.9812.1512.363234-2-6.3%
Emmanuel Sanders12.1812.6813.0412.6312.823328515.2%
DeVante Parker11.7312.5111.0611.7711.993437-3-8.8%
Allen Hurns11.7612.8312.3012.88353325.7%
Michael Crabtree12.0412.6512.8612.5212.713630616.7%
Josh Gordon9.839.519.679.553749-12-32.4%
DeSean Jackson11.3413.411.6612.1412.31383537.9%
Sterling Shepard10.2410.6710.459.943944-5-12.8%
Kevin White10.3310.5110.4210.024045-5-12.5%
Stefon Diggs12.8412.9311.3712.3812.634132922.0%
Willie Snead11.4911.8711.211.5211.74423937.1%
Corey Coleman12.3610.3711.3611.95434212.3%
Torrey Smith11.7613.5910.9512.112.294436818.2%
Tavon Austin12.4110.3911.311.3711.59454148.9%
Devin Funchess9.749.038.549.19.34654-8-17.4%
Michael Thomas8.298.928.618.084758-11-23.4%
Travis Benjamin10.949.639.7910.1210.34484800.0%
Kamar Aiken9.0712.639.4610.3910.46494636.1%
Vincent Jackson11.9210.6710.7111.111.335043714.0%
Markus Wheaton9.118.949.29.089.275155-4-7.8%
Phillip Dorsett7.137.998.667.938.065267-15-28.8%
Mohamed Sanu8.0410.219.499.259.37535300.0%
Tyler Boyd8.738.568.648.505456-2-3.7%
Rishard Matthews8.610.529.329.489.62555059.1%
Laquon Treadwell8.508.198.348.285663-7-12.5%
Sammie Coates8.586.737.668.305772-15-26.3%
Tajae Sharpe9.437.608.519.195861-3-5.2%
Will Fuller8.937.778.359.065962-3-5.1%
Steve Smith12.568.919.410.2910.4760471321.7%
Bruce Ellington6.889.016.567.487.556176-15-24.6%
Mike Wallace9.979.468.669.369.5662511117.7%
Chris Hogan7.967.967.357.767.926369-6-9.5%
Pierre Garcon9.468.919.519.299.4964521218.8%
Dorial Green-Beckham8.476.536.257.097.186580-15-23.1%
Ted Ginn10.067.47.368.278.4666423.0%
Terrance Williams8.498.928.288.578.746759811.9%
Anquan Boldin10.027.837.838.568.726860811.8%
Terrelle Pryor6.012.54.263.816986-17-24.6%
Josh Doctson8.626.047.337.897077-7-10.0%
Breshad Perriman9.585.807.698.12717100.0%
Davante Adams8.997.047.167.737.87727022.8%
Kenny Stills8.627.157.837.878.03736856.8%
Robert Woods8.238.238.138.28.387465912.2%
Jermaine Kearse9.817.317.28.18.237566912.0%
Nelson Agholor5.647.386.896.636.747682-6-7.9%
Jamison Crowder7.418.356.97.557.68777433.9%
Eli Rogers8.313.876.096.087883-5-6.4%
Victor Cruz7.244.846.046.497984-5-6.3%
Chris Conley5.55.845.675.908085-5-6.3%
Kenny Britt7.38.017.667.99817389.9%
Cole Beasley7.627.417.527.86827578.5%
Kendall Wright10.837.967.068.628.7183572631.3%
Jaelen Strong8.626.796.457.297.4847867.1%
Brandon LaFell8.216.247.237.47857967.1%
Danny Amendola7.885.896.897.09868155.8%

The ensembled projections are calculated in two ways. The first is just a straight average, and the second uses transformed z-scores to account for the fact that the projections themselves are in a slightly different range. For simplicity, we’ll use the average projections the rest of the article, but the EnsembleZ projections should be slightly better in the long run.

Undervalued WR Candidates by Tier

WR1: Allen Robinson — Positional ADP: 7 — Composite Rank: 5

Allen Robinson looks to be the most undervalued from the upper echelon of WRs, coming in fifth in both ensemble methods but only being drafted as the WR7. Robinson may regress a small amount, but his yards per target over expectation (YPTOE) was actually negative, meaning there’s room to imrpove in that area. He also had the second most air yards, which is a main reason the machine learning model likes him. Air yards are a sticky stat from year-to-year, so if that air yards volume remains and his catch rate improves, we’re talking about a player who could repeat as a top six season-long WR.

WR2: Julian Edelman — Positional ADP: 23 — Composite Rank: 20

Edelman hasn’t been getting much love this offseason for two reasons. First, he’ll have four games without Tom Brady, and second, he’s coming off a foot injury. However, all indications are he will be good to go for Week 1. In this case, Edelman is boosted by the machine learning model, as he fits the mold of a high producing receiver with a very short air yards per target (along with Jarvis LandryRandall Cobb, and Golden Tate, to name a few). The machine likes these receivers’ consistent volume, which is king. While most of the WR2s are fairly valued, if Edelman can mostly reproduce with Jimmy Garoppolo what he did with Brady the last two years, he’s the undervalued man in the bunch.

WR3: Jordan Matthews — Positional ADP: 29 — Composite Rank: 24

Matthews is a guy I’ve come around to after a great Slack chat debate between myself and a few other RotoViz colleagues on if we’d rather have Matthews or Larry Fitzgerald. According to the ensembles, the Matthews crew won — barely! Matthews is poised for a breakout season, and my young WR model also likes Matthews to shine because of his insanely high collegiate market share, which still matters for third year receivers. Oh, he also has some interesting numbers when compared to another guy on this list…Allen Robinson.

WR4: Stefon Diggs — Positional ADP: 41 — Composite Rank: 32

The machine learning model is agnostic to QB play, so the projection for that stands independent of the serious knee injury to Teddy Bridgewater. My best guess is Diggs’ ADP will fall a slight bit, but it will likely be mirrored by slight drops in the RotoViz composite or the fantasyfootballanalytics.net composite. With both ADP and ensembled projections taking a slight hit, Diggs would still be a crazy value. I don’t need to rattle off the myraid of reasons we love Diggs at RotoViz. I’ll just link you to the articles here, here, here, and here.

Oh, and I don’t believe Shaun Hill, or some of the names floated around as trade targets, would hurt Diggs too much. Here’s an AYA graph comparing Bridgewater to Hill and the others.

ShaunHill

Ignore Hill’s 2015 — it came on only seven pass attempts. But his 2014 season with the Rams was when he started eight games and lands smack in the middle of this bunch.

WR5: Steve Smith — Positional ADP: 60 — Composite Rank: 47

We’ve already identified Smith as a value, and he’s going to be the WR1 on this team when all set to return in Week 1. Last year, Smith was on a blistering pace which would have put him 13th in total air yards over a full season. Yet, he hovered right around his career average in efficiency, so he wasn’t boosted unnecessarily by efficiency. If he can retain 60 percent of that volume, and his career average in efficiency, he’ll be a WR4 for a WR5 price.

WR6: Pierre Garcon — Positional ADP: 64 — Composite Rank: 52

While Garcon might not have the most upside in the WR6 tier, he holds the most value according to the ensemble rankings. Garcon saw the 35th most air yards last year and was near league average in both YPTOE and TD rate. The addition of Josh Doctson is concerning, but Docston isn’t expected to be ready for Week 1, and could miss more time than that. Brian Malone expects Garcon to maintain his 20 percent market share, and the models agree. He’s not young, or flashy, but he represents a ton of value.

WR7: Jermaine Kearse — Positional ADP: 75 — Composite Rank: 66

I’m snapping up all the Kearse I can at his current price. Let’s compare his 2015 to that of teammate Tyler Lockett.

KearseLockett

Remarkably similar. He trailed in catch rate and TD rate, but led in YPT. Combining the two, along with game situation, he actually was nearly equal to Lockett in YPTOE. Add in a likely unsustainable TD rate for Lockett, which was the 96th best out of 1350 WRs with 50+ targets in a single season since 2000, and we have a situation where the two are much further apart in ADP than they probably will perform on the field in 2016. Kearse is still the WR2 on the Seahawks depth chart, and has played and practiced as the WR2 in the preseason.

To be clear, I’m not arguing for Kearse over Lockett; I’m just saying Kearse should be much closer to Lockett in ADP.

Other Candidates

You can use the Diff and RelDiff columns to find more undervalued candidates. Some good ones include Emmanuel SandersMichael CrabtreeTorrey SmithVincent JacksonMike WallaceAnquan Boldin, and Robert Woods to name a few.

  1. One of the most accurate projections on the web.  (back)

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