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The Gut Check No. 401: TE Production Shares, ADP, and Consistency

Matt Waldman examines tight ends that quarterbacks lean on in the passing game, including the return of his consistency data. 

Pairing statistics with insightful context can uncover fantasy value in pre-draft prep. Delivering the insightful context is the difficult part. Three data points that have potential value for fantasy owners are the percentage of targets, yards, and touchdowns that pass catchers earn from their quarterback during the year.

This week's Gut Check column will examine these three statistical outcomes for tight ends between 2014-2016. This information will also be paired with ADP, consistency data, and other salient points that will help readers identify some of the safest options in 2017 fantasy drafts.

ABOUT CONSISTENCY DATA

I began publishing Consistency Data in 2004. What differentiates my examination of "fantasy consistency" from many is that I don't use standard deviation as the method because this stat's purpose is to measure the variance of a process. When I was posting a lot of consistency analysis in the early-to-mid 2000s, the few others who were analyzing consistent performance were valuing low standard deviations because it proved to them that the process is under control. When applied to fantasy football, standard deviation doesn't help us see the type of consistency that a fantasy owner desires from a player.

Standard deviation doesn't take into account what we really want to know: How consistently a player reaches at least the desired amount of fantasy points per game. "At least" are the two key words. Strict adherence to standard deviation as the process punishes players who score a lot more than the baseline target—even when they consistency deliver to that baseline.

For example, here's a look at the 2016's top PPR tight ends with a minimum scoring average of 8 points per game and ranked by lowest to highest standard deviation:

Lowest Standard Deviation Among 2016 PPR Tight Ends (Minimum of 8 fantasy points per game average)

LastFirstGmsFpts/GmElite#1TE#2TESubParSTDevMinMax
Barnidge Gary 16 8.01 6.25% 37.50% 62.50% 62.50% 4.40 0.00 15.00
Ebron Eric 13 11.09 38.46% 53.85% 92.31% 46.15% 4.99 0.00 17.30
Thomas Julius 9 9.12 22.22% 55.56% 55.56% 44.44% 5.16 3.30 17.40
Fiedorowicz C.J. 15 8.93 26.67% 40.00% 66.67% 60.00% 5.70 0.00 20.50
Doyle Jack 16 9.21 18.75% 43.75% 68.75% 56.25% 5.92 1.60 22.80
Witten Jason 16 9.64 18.75% 43.75% 68.75% 56.25% 6.33 0.00 27.40
Brate Cameron 15 11.40 33.33% 46.67% 66.67% 53.33% 6.45 3.80 21.60
Rudolph Kyle 16 13.06 43.75% 75.00% 87.50% 25.00% 6.51 3.20 28.70
Henry Hunter 15 8.81 26.67% 46.67% 60.00% 53.33% 6.56 0.00 20.30
Olsen Greg 16 12.83 43.75% 62.50% 81.25% 37.50% 6.66 2.10 27.10
Gates Antonio 14 10.70 28.57% 57.14% 71.43% 42.86% 6.70 0.00 23.40
Walker Delanie 15 12.54 40.00% 60.00% 80.00% 40.00% 6.83 3.10 27.40
Miller Zach 10 11.96 40.00% 60.00% 80.00% 40.00% 6.86 4.40 27.80
Fleener Coby 16 8.58 12.50% 31.25% 50.00% 68.75% 7.08 1.60 25.60
Pitta Dennis 16 10.68 25.00% 43.75% 62.50% 56.25% 7.27 3.40 30.00
Clay Charles 15 9.08 20.00% 40.00% 53.33% 60.00% 7.41 0.00 28.50
Graham Jimmy 16 12.08 31.25% 62.50% 75.00% 37.50% 7.45 2.10 30.30
Allen Dwayne 14 7.97 21.43% 35.71% 35.71% 64.29% 8.17 0.00 29.20
Bennett Martellus 16 10.51 31.25% 50.00% 56.25% 50.00% 8.27 1.50 30.70
Kelce Travis 16 13.81 43.75% 62.50% 75.00% 37.50% 8.32 1.80 33.00
Eifert Tyler 8 12.30 37.50% 62.50% 75.00% 37.50% 8.77 1.90 25.20
Reed Jordan 12 14.22 33.33% 75.00% 75.00% 25.00% 9.61 1.60 31.50
Ertz Zach 14 13.11 42.86% 57.14% 64.29% 42.86% 9.87 2.40 38.90
Gronkowski Rob 9 10.78 44.44% 44.44% 55.56% 55.56% 11.13 0.00 29.20

Gary Barnidge, Eric Ebron, and Julius Thomas are talented players and certainly options worth consideration for your roster, but their low standard deviation is also a reflection that they lacked great upside to their game last year. At the bottom of this list is Travis Kelce, Jordan Reed, and Zach Ertz, who averaged between 2-5 additional points per game and not only had a higher percentage of elite performances, they also had a lower percentage of subpar efforts. 

This is why I set tier baselines by generating average values for elite, WR1, WR2, WR3, and subpar categories based on the fantasy years in question. This method doesn't punish consistently high scoring options because their massive game in Week 4 blew their tight little standard deviation figure. 

Tight End PRODUCTION SHARES

Before digging into consistency data, let's examine production shares between 2014-2016, including the top 36 tight ends with the highest percentages of targets, yards, and touchdowns in their offenses. These are the columns highlighted in yellow, green, and blue below. 

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