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Flaccoing?

Flaccoing?

In September, I started a post by asking you to make this assumption:

Assume that it is within a quarterback’s control as to whether or not he throws a completed pass on any given pass attempt. However, if he throws an incomplete pass, then he has no control over whether or not that pass is intercepted.

If that assumption is true, that would mean all incomplete pass attempts could be labeled as “passes in play” for the defense to intercept. Therefore, a quarterback’s average number of “Picks On Passes In Play” (or POPIP) — that is, the number of interceptions per incomplete pass he throws — is out of his control.

After doing the legwork to test that assumption, I reached two conclusions. One, interception rate is just really random, and predicting it is a fool’s errand. Two, using a normalized INT rate — essentially replacing a quarterback’s number of interceptions per incomplete pass with the league average number of interceptions per incomplete pass — was a slightly better predictor of future INT rate than actual INT rate. It’s not a slam dunk, but there is some merit to using POPIP, because completion percentage, on average, is a better predictor of future INT rate than current INT rate.

So, why am I bringing this up today, at the start of Super Bowl week? Take a look at where Sunday’s starting quarterbacks ranked this year in POPIP (playoff statistics included, minimum 250 pass attempts):
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More work on POPIP and predicting INT rates

A couple of weeks ago, I wrote about interceptions per incompletion, or POPIP. In that article I showed how a player’s completion percentage is a better predictor of his future interception rate than his actual interception rate. And in this article by Brian Burke, one comment stuck with me:

Griffin has thrown deep, defined as attempts of greater than 15 yards through the air, on only 13% of his attempts, 30th among league quarterbacks. This is also likely the largest factor in his very low interception rate.

That makes sense — quarterbacks throwing short, safe passes should throw fewer interceptions. But this statement is a more important one than you might originally think, thanks to some great research by Mike Clay.

Clay came up with a metric he calls ‘aDOT’ — average depth of target — which measures exactly what you think it does. For each targeted or aimed pass, Pro Football Focus tracks how far from the line of scrimmage the intended target is. What’s makes this stat particularly appealing to me is that it’s very predictable as far as football statistics go. That’s not all that surprising because aDOT is based on a large sample of plays and basically frames how an offense operates.

Clay posted the 10 passers with the largest and smallest aDOT in 2011, which I’ve reproduced below. Note that there are some passes — spikes, throwaways, passes tipped at the line (these are grouped together as ‘other’) — with no target, and therefore are excluded when calculating aDOT. In the far right column, I’ve shown how the player’s aDOT compares to the league average rate of 8.8.

PasserYrAttAimOtheraDOTlgAVG
Tim Tebow20113182863213.3151%
Vince Young2011114111311.6131%
Jason Campbell20111651511410.5119%
Matt Moore20113473281910.4118%
Carson Palmer20113283121610.3117%
Eli Manning20117526985410.1114%
Cam Newton20115174942310113%
Joe Flacco2011605568379.8111%
Ben Roethlisberger2011553529249.8110%
Chad Henne2011112102109.7110%
T.J. Yates2011189171189.6109%
Matt Hasselbeck2011518490288.394%
Drew Brees2011763730338.293%
Blaine Gabbert2011413381328.192%
Alex Smith2011513463508.191%
Tony Romo2011522497258.191%
Ryan Fitzpatrick201156954425890%
Donovan McNabb2011156145117.989%
Colt McCoy2011463434297.888%
Tyler Palko201113512787.484%
Josh Freeman2011551519327.483%

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