Statistically, does a veteran QB matter in the playoffs?

This question came up when I was looking up the last year in the playoffs for seven probable NFC playoff teams. Both New Orleans and Philadelphia last played in the playoffs four years ago, in 2013. And then the thought came up in my head, “But Drew Brees is a veteran QB.” This seems intuitive, but wanting to actually create such a definition and then later to test this using a logistic regression, there is the rub.

There are any number of QBs a fan can point to and see that the QB mattered. Roger Staubach seemed a veteran in this context back in the 1970s, Joe Montana in the 1980s, Ben Roethlisberger in the 21st century, Eli Manning in 2011, and Aaron Rogers last year. But plenty of questions abound. If a veteran QB is an independent variable whose presence or absence changes the odds of winning a playoff game, what tools do we use to define such a person? What tools would we use to eliminate entanglement, in this case between the team’s overall offensive strength and the QB himself?

The difference between a good metric and a bad metric can be seen when looking at the effect of the running game on winning. The correlation between rushing yards per carry and winning is pretty small. The correlation between run success rate and winning are larger. In short, being able to reliably make it on 3rd and 1 contributes more to success than running 5 yards a carry as opposed to 4.

At this point I’m just discussing the idea. With a definition in mind, we can do one independent variable logistic regression tests. Then with a big enough data set – 15 years of playoff data should be enough, we can start testing three independent variable logistic models (QB + SOS + PPX).

 

Welcome Statheads; draft and playoff wins considered.

I’ll note that the Sports Reference blog Statheads now has this blog on their sidebar, something I’m quite  grateful for. Not that you don’t have to fight for readership as an amateur in football  blogging, because you do. But to be spoken of in the same breath as sites like Football Outsiders or Advanced Football Stats is, well,  heady stuff. So to Neil Paine, thank you.

What I’m going to say to readers that are largely team (e.g. Dallas, Atlanta, Green Bay, Pittsburgh, Chicago) fans, you’ll get the best bang for your buck by looking at the tag cloud on the right of the blog and clicking ones that interest you. If you’re one of the football coaches that have drifted here because of Coach Hoover’s recommendations, I’d suggest your best usage of this site would be to follow the  tags “46 defense” and “defensive fronts”. For those for whom algebra isn’t an issue, just read the  general flow of this board. I’m going to try and keep the look visual and fold most of the numerical results behind “more”  tags. The guy who wants to see Rob Ryan or Dom Capers get after the quarterback doesn’t need screen shots of program output, and the guy who does can click on the “more”.

The big splash of the draft, from my point of view, was the trade from 27th to 6th by the Falcons. They netted Julio Jones with the trade, giving up two firsts, one second, and 2 4th round choices in the process. This is because the Falcons felt they weren’t explosive enough, and so had to improve on an offense ranked in the top ten in the league. That their defense was mediocre and the salient feature of the last Super Bowl was that the #1 and #2 ranked defenses met, seemed to be bypassed in the quest for game changing explosiveness.  I suspect trying to become a 21st century Air Coryell certainly has fan and box office appeal, but is it wise over the longer term? I’m reminded of the nursery rhyme:

For want of a nail the shoe was lost.
For want of a shoe the horse was lost.
For want of a horse the rider was lost.
For want of a rider the battle was lost.
For want of a battle the kingdom was lost.
And all for the want of a horseshoe nail.

I’m not convinced there was that much more air based explosiveness to get out of the Falcons offense. Perhaps on the ground, where some rest for Michael Turner might save him from regression to the mean.

JMO, but the focus of the Falcons is classic Parcells style ball control, where yards per carry are far less important than time of possession (this, incidentally, is why Curtis Martin will always be underrated by YPC-heads – Parcells just never cared about his ball carriers YPC). In such an offense, the most important component of  the offense are first downs, not pretty stats. Given how light the Falcons defensive line tends to be, keeping them  off the field as much as possible has to be a serious design consideration for the whole team.

 

Predicting playoff wins in the NFL – what factors are important?

I was, to some extent,  inspired by the article by Benjamin Morris on his blog Skeptical Sports, where he suggests that to win playoff games in the NBA, three factors are most important: winning percentage, previous playoff experience, and pace – a measure of possessions. Pace translated into the NFL would be a measure that would count elements such as turnovers and punts. In the NBA, a number of elements such as rebounds + turnovers + steals would factor in.

I’ve recently captured a set of NFL playoff data from 2001 to 2010, which I analyzed by converting those games into a number. If the home team won, the game was assigned a 1. If the visiting team won, the game was assigned a 0. Because of the way the data were organized, the winner of the Super Bowl was always treated as the home team.

I tested a variety of pairs of regular season statistical elements to see which ones correlated best with playoff winning percentage. The test of significance was a logistic regression (see also here), as implemented in the Perl module PDL::Stats.

Two factors emerge rapidly from this kind of analysis. The first is that playoff experience is important. By this we mean that a team has played any kind of playoff game in the previous two seasons. Playoff wins were not significant in my testing, by the way, only the experience of actually being in the playoffs. The second significant parameter was the SRS variable strength of schedule. Differences in SRS were not significant in my testing, but differences in SOS were. Playing tougher competition evidently increases the odds of winning playoff games.

 

Was sabermetrics, now analytics.

We’ll start on a small, pretty blog called “Sabermetrics Research” and this article, which encapsulates nicely what’s happening. Back when sabermetrics was a “gosh, wow!” phenomenon and mostly the kind of thing that drove aficionados to their campus computing facility, the phrase “sabermetrics” was okay. Now that this kind of analysis is going in-house (a group of  speakers (including Mark Cuban) are quoted here as saying that perhaps 2/3 of all basketball teams now have a team of analysts), it’s being called “analytics”. QM types, and  even the older analysts, need a more dignified word to describe what they do.

The tools are different. There is the phrase logistic regression all over the place (such as here and here). I’ve been trying to rebuild a toolset quickly. I can code stuff in from “Numerical Recipes” as needed, and if I need a heavyweight algorithm, I recall that NL2SOL (John Dennis was a Rice prof, I’ve met him) is available as part of the R language. Hrm. Evidently, NL2SOL is also available here. PDL, as a place to start, has been fantastic. It has hooks to tons of things, as well as their built-ins.

Logistics regression isn’t a part of PDL but it is a part of PDL::Stats, a freely available add on package, available through CPAN. So once I’ve gnawed on the techniques enough, I’d like to try and see if Benjamin Morris’s result, combining winning percentage and average point spread (which, omg, is now called MOV, for margin of victory) and showing that the combination is a better predictor of winning than either in basketball, carries over to football.

I suspect, given that Brian Burke would do a logistic regression as soon as tie his shoes, that it’s been done.

To show what PDL::Stats can do, I’ve implemented Brian Burke’s “Homemade Sagarin” rankings into a bit of code I published previously. The result? This simple technique had Green Bay ranked #1 at the end of the 2010 season.

There are some issues with this technique. I’ll be talking about that in another article.