This article aims ONLY to examine Colin Kaepernick’s career passing statistics, and how they compare to rest of the NFL’s “starting” QBs (quarterbacks who have started at least a game this year). To explore this question, I employed a Principal Component Analysis (PCA) which incorporated a QB’s career Rating, TD per game, Interceptions per game, Yards per game, Pass completions per game, and Pass attempts per game.

*For a brief description of Principal Component Analysis (PCA), please read the end of this article.

The first principal component (PC1) explained over 65% of the variance between 2017 starting QBs in the NFL. I assumed that experience causes quite a bit of variability in this, so I plotted PC1 vs Career Games played. This plot revealed an interesting inverse relationship, suggesting that lower PC1 scores are typically registered by the most successful QBs in the NFL. Specifically, a PC1 of < 0.0 appears to be a reliable indicator of a successful QB. I know that is a qualitative description within this quantitative assessment, but I’m having difficulty describing this. Think of this as a tier of QB who is in no danger of losing his job (if that makes sense…).

There are 23 QBs who scored a PC1 below 0.0:

Drew Brees

Matthew Stafford

Aaron Rodgers

Tom Brady

Matt Ryan

Kirk Cousins

Phillip Rivers

Carson Palmer

Deshaun Watson

Derek Carr

Ben Roethlisberger

Carson Wentz

Jameis Winston

Eli Manning

Blake Bortles

Andy Dalton

Dak Prescott

Jay Cutler

Joe Flacco

Russell Wilson

Trevor Siemian

Cam Newton

Marcus Mariota

And 15 QBs who scored a PC1 aboce 0.0:

Mike Glennon

Ryan Fitzpatrick

Alex Smith

Case Keenum

Jared Goff

Josh McCown

Colin Kaepernick

Brian Hoyer

Matt Cassel

DeShone Kizer

Tyrod Taylor

Jacoby Brissett

Mitchell Trubisky

Kevin Hogan

Brett Hundley

Interpret this list as you would like, but it is a rather convincing indicator of success as a QB.

While he does not score in the upper tier of starting QBs, Colin Kaepernick scored 30th out of 38 QBs. This suggests that from a purely statistical perspective, he has performed as well as QBs like Brian Hoyer and Josh McCown.

* Brief description of PCA:

The purpose of Principal Component Analysis (PCA) is to represent a data set containing many variables with a much smaller number of composite variables, or principal components. Think of the QB Rating (QBR). It is a composite variable in that it incorporates a number of other variables (Completion %, Yards, TDs, etc). PCA differs in that it places no bias on which variables it incorporates into the principal component. PCA only chooses the most compelling co-variation among variables, or the variables which explain the most variance between the sample units (i.e. Players).