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Wide Receivers Who Should Have Scored More: Positive Touchdown Regression Candidates

Due to the variance in wide receiver touchdowns year-to-year and other stats only weakly predicting next-season receiving TDs, regression can be used to identify wide receivers whose 2017 receiving touchdowns didn’t align with their expected touchdown total.

WR Touchdown Regression Equation

You can see my methodology behind this equation here. But the jist of it is:

Expected reTDS = 0.139 + (-0.015 * Total reRTGS) + (0.008 * Total reYDS)

  • For positive touchdown regression: look for WRs with a lot of yards, but few touchdowns.
  • For negative touchdown regression: look for WRs with more touchdowns than their total receiving yards would predict, even if they have a lot of targets.

Results

The following table shows the actual and expected receiving TDs of WRs with at least 65 targets in 2017. A positive difference indicates the WR scored touchdowns at a rate above his expectation. A negative difference indicates the WR scored below expectation.

PlayerreTRGSreYDSActual reTDSExpected reTDSDifference
Antonio Brown1631533910.0-1.0
Julio Jones148144439.5-6.5
Keenan Allen159139368.9-2.9
DeAndre Hopkins1741378138.64.4
Adam Thielen142127648.2-4.2
Tyreek Hill105118378.0-1.0
Michael Thomas149124557.9-2.9
Marvin Jones107110197.31.7
Brandin Cooks114108277.1-0.1
Larry Fitzgerald161115667.0-1.0
A.J. Green143107886.61.4
Golden Tate120100356.4-1.4
Doug Baldwin11699186.31.7
JuJu Smith-Schuster7991776.30.7
Marquise Goodwin10596226.3-4.3
T.Y. Hilton10996646.2-2.2
Mike Evans136100156.1-1.1
Robby Anderson11494176.01.0
Cooper Kupp9486955.7-0.7
Demaryius Thomas14094955.6-0.6
Jarvis Landry16198795.63.4
Stefon Diggs9584985.52.5
Davante Adams117885105.54.5
Ted Ginn7078745.4-1.4
Kenny Stills10584765.30.7
Rishard Matthews8779545.2-1.2
Devin Funchess11184085.22.8
Robert Woods8578155.1-0.1
Jermaine Kearse10281055.1-0.1
Tyrell Williams6972844.9-0.9
Jamison Crowder10378934.9-1.9
Keelan Cole8374834.9-1.9
Dez Bryant13283864.91.1
Nelson Agholor9576884.93.1
Mike Wallace9274844.7-0.7
Sterling Shepard8473124.7-2.7
Alshon Jeffery12078994.74.3
Paul Richardson8070364.61.4
Mohamed Sanu9670354.30.7
Marqise Lee9670234.3-1.3
Amari Cooper9668074.12.9
DeSean Jackson9066834.1-1.1
Danny Amendola8665924.1-2.1
DeVante Parker9667014.1-3.1
Randall Cobb9265344.00.0
Adam Humphries8363113.9-2.9
Sammy Watkins7059383.84.2
Ryan Grant6557343.70.3
Martavis Bryant8460333.7-0.7
Kendall Wright9161413.7-2.7
Michael Crabtree10161883.64.4
Tyler Lockett7155523.5-1.5
Terrance Williams7856803.5-3.5
Eric Decker8356313.4-2.4
Emmanuel Sanders9255523.2-1.2
Brandon LaFell8954833.2-0.2
Pierre Garcon6750003.1-3.1
Josh Doctson7850263.03.0
Jaron Brown6947742.91.1
Seth Roberts6545512.8-1.8
Jordy Nelson8848262.73.3
Jeremy Maclin7244032.60.4
Torrey Smith6743022.6-0.6
Roger Lewis7241622.4-0.4
Corey Davis6537502.2-2.2
Zay Jones7431621.60.4

* Note: a WR underperforming his receiving TDs expectation in 2017 doesn’t mean he’s going to overcompensate with even more touchdowns in 2018 to correct that deficit. Rather, he should score at a rate1 closer to what his opportunity suggests.2

Positive Touchdown Regression Candidates

1. Julio Jones: Actual: 3; Expected: 9.5; Difference: -6.5

Jones’ -6.5 TD difference is the third largest underperformance over the past decade, behind only Calvin Johnson‘s -7.9 and Andre Johnson‘s -6.5 2012 seasons. Scoring just three TDs on 1,444 receiving yards is an even greater outlier considering he saw 19 red-zone targets (eighth among WRs). Additionally, the WR TD expectation equation suggests that when given an equal number of targets, a WR with a higher yards per target (YPT) is expected to score more TDs — Jones ranked eighth in YPT (9.8). If Jones and Matt Ryan (whose 2017 TD rate of 3.8 percent was well below his career average of 4.6 percent) both positively regress in the touchdown department, Jones could be a massive value at the Rounds 1-2 turn.

2. Michael Thomas: Actual: 5; Expected 7.9; Difference: -2.9

Thomas’ underachieving touchdown total is highly correlated with Drew Brees’ low touchdown total. Brees threw his fewest touchdown passes (24) over the last 15 seasons, while still leading the league in yards per attempt (8.1) and completion percentage (an NFL record 72 percent). Even if the Saints continue to have a strong running game, their 23 rushing TDs should negatively regress, as they had five more rushing scores than any other team. And even if the Saints defense remains strong, we shouldn’t expect them to play with a lead during  55.5 percent of game time (third) or a multi-score lead during 28.5 percent of their offensive snaps. More negative or neutral game scripts would lead to an increase in pass attempts, as the Saints’ pass-to-run ratio dipped from 59 percent in neutral game scripts to 42 percent when playing with a multi-score lead.

3. Adam Thielen: Actual: 4; Expected: 8.2; Difference: -4.24

Despite nearly identical targets and yards, Thielen scored just one TD before the Vikings’ Week 9 bye, but found the end zone three times in the second half of the season. While Case Keenum played well, new QB Kirk Cousins is still an improvement. Expect Thielen to score at a rate closer to the second half of  2017.

4. Pierre Garcon: Actual: 0; Expected: 3.1; Difference -3.1 and Marquise Goodwin: Actual: 2; Expected: 6.3; Difference: -4.3

I’m not projecting a 32-year old Garcon recovering from a neck injury with a career high six receiving TDs or a 5-foot-9-inch, 183-pound Goodwin with eight receiving TDs in five NFL seasons to suddenly catch double-digit scores. But the 49ers threw just 15 passing TDs in 2017, and they look to be an ascending offense captained by Jimmy Garoppolo.

5. DeVante ParkerActuaL 1; Expected: 4.1; Difference: -3.1

Parker hasn’t lived up to his first-round draft pedigree, but should see positive touchdown regression and increased opportunity with Jarvis Landry‘s 161 targets (27 percent target share) up for grabs.

Bonus: Status Quo Candidate

1. Antonio Brown: Actual: 9; Expected 10; Difference -1

Brown had the highest receiving touchdown expectation (10) in 2017, and of the 14 WRs to score eight or more receiving touchdowns, Brown is the only one that actually underperformed his expected touchdown total. Therefore, Brown (the No. 1 WR in points per game) should have actually scored more points. Since 2013, Brown has averaged 10.4 TDs per year, and a ridiculous 10.9 expected TDs per year.

Conclusion

Regression isn’t the end all be all, as we must account for opportunity changes, team situation, and Average Draft Position (ADP). But regression is a quick way to identify WRs who over- or underperformed in the touchdown department. Stay tuned for a look at which players overperformed in 2017, and will likely see their touchdown totals decrease in 2018.

  1. Rate being based on the regression equation involving total targets and yards, not the traditional touchdown rate of touchdowns per target.  (back)
  2. To predict 2018 receiving TDs, we also must account for how that opportunity changes from 2017 to 2018.  (back)

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