Correlation Between No. 1 Receiver and Quarterback Point Totals
Based on that one example, it’d be easy to conclude that there is no correlation between a top wide receiver’s dominance and said receiver’s quarterback’s fantasy potential.
However, one example doesn’t validate a claim, so I decided to record the point total of each of the top 20 receivers in standard fantasy scoring from 2013 and pair their scoring outputs with their quarterback’s (or quarterbacks’, in some cases). I then looked at the data on a scatterplot.
The results were eye-opening.
Contrary to popular belief, there actually is a moderately strong linear relationship between No. 1 receivers and their quarterbacks’ scoring outputs.
|Team||No. 1 WR||WR Point Total||QB||QB Point Total|
|DET||Calvin Johnson||212||Matthew Stafford||267|
|CIN||A.J. Green||203||Andy Dalton||277|
|PIT||Antonio Brown||197||Ben Roethlisberger||248|
|WAS||Pierre Garcon||154||Griffin III/Cousins||230|
|SF||Anquan Boldin||153||Colin Kaepernick||253|
|ARZ||Larry Fitzgerald||146||Carson Palmer||209|
|SD||Keenan Allen||141||Philip Rivers||276|
|NE||Julian Edelman||147||Tom Brady||241|
|BAL||Torrey Smith||132||Joe Flacco||194|
|IND||T.Y. Hilton||132||Andrew Luck||279|
To rationalize the data, I accounted for the fact that Denver and Chicago both had two receivers in the Top 20 (two No. 1 receivers, so to speak). In said rationalization, I recorded Denver’s “No. 1” receiver total as Demaryius Thomas’ scoring output added to half of Eric Decker’s scoring output. I did the same for Chicago, recording Brandon Marshall’s numbers added to half of Alshon Jeffery’s.
The coefficient of determination for the data is .41, which means that the data is far from conclusive.
That said, we found that the ‘linear regression’ line’s equation is y=.681x+133.76, which means that we can find which quarterbacks outperformed having a poor No. 1 receiver and vice-versa. The ‘y’ value represents the expected total point output for the quarterback, while the ‘x’ value represents the receiver’s actual point total.
|QB||Actual Point Total||Expected Point Total||Difference|
The five teams whose collective quarterbacks performed the worst, considering the No. 1 receiver at their respective disposals, were Tampa Bay, Cleveland, Houston, Baltimore and Arizona.
Recall that Tampa Bay, Cleveland and Houston split their quarterbacking duties over the course of the season, so this article is not necessarily an indictment of Mike Glennon, Matt Schaub or any of the other individual signal callers on the 2013 iterations of the aforementioned teams.
That said, Baltimore and Arizona saw almost all of their snaps under center go to Joe Flacco and Carson Palmer, respectively. The two seriously underperformed their expected point totals, if one was to simply base expectation on the performance of the team’s No. 1 receiver.
The five teams whose quarterbacks outperformed their expected totals were, in order, Philadelphia, Denver, Indianapolis, San Diego and Chicago.
Recall that Philadelphia and Chicago each had two quarterbacks see meaningful snaps in 2013, so a wholehearted endorsement of Nick Foles, Michael Vick, Jay Cutler or Josh McCown isn’t necessarily realistic due to a somewhat small sample size.
Of course, the other three listed teams saw, for the most part, one quarterback take snaps. Peyton Manning, Andrew Luck and Philip Rivers each did unreasonably well, considering the output of their respective No. 1 targets. Keep in mind that we adjusted the data to make Manning’s performance come closer to the average, which makes the signal caller’s numbers even more impressive. He, however, will lose Eric Decker and might see a dip in production as he regresses to his mean level of performance.
As for Luck and Rivers, their No. 1 receivers (T.Y. Hilton and Keenan Allen, respectively) are young and improving. Luck will get Reggie Wayne back, which can only help his point total increase.
To qualify the data, I didn’t include tight ends. As such, the paired data of Drew Brees and Jimmy Graham is omitted (Brees would outperform the expected total by more than all but one quarterback, Manning, by the way). The attempt was to simply evaluate quarterbacks and No. 1 receivers.
Though it’s tough to establish a strong correlation with a coefficient of determination less than .5, looking at how well quarterbacks performed relative to their expected totals is a good tool for inference.
Take a close look at Rivers and Luck in your fantasy draft this year, as all signs point to the two quarterbacks performing well in 2014, regardless of what they get from their receiving corps.
Conversely, be aware that having a legitimate No. 1 receiver can help a quarterback, if slightly. When picking between two similar signal-callers, it’s not a terrible idea to bet on the one throwing to a star top target.