Summary: with some calculations based on adjusted yards per attempt, Matt Ryan’s value as a passer in the 2016 season can be shown to be almost 9 points a game more than the average QB.

Mark Zinno is a host on a sports talk show, 92.9 the Game, in the 7pm ET time slot. Often booted out of the slot by Atlanta Hawks games, he nonetheless has been a dogged supporter of Matt Ryan. This isn’t new, btw. Even in years where Matt Ryan wasn’t at his best, he would doggedly argue that Matt Ryan was an elite quarterback, and said repeatedly that compared to an average NFL team, that Atlanta was blessed.

So, we’re dedicating this blog post to Mark Zinno.

It’s hard to understand the scope of what Matt Ryan has done until you look at his adjusted yards per attempt in 2016. Pro Football Reference lists it as 10.1, which is one of the highest I’ve seen, and comparable to Peyton Manning’s 2004 season, where PM’s AYA was 10.2. Looking a little further, you can see that PFR ranks this the 4th best performance in history. Aaron Rogers is in the top 4, and for some reason, so is Nick Foles.

The value in using AYA is that you can build an expected points curve that satisfies all the requirements of the AYA function, and then use the slope of that curve to relate yards to points. Don’t worry, I did that long ago, and the result is documented here. The simple take home is the magic conversion 2.25, which converts AYA from yards to “expected points generated per 30 passes”.

Then, using the 2016 annual data from Pro Football Reference, you can calculate  what the average QB did, by calculating an AYA using the overall season’s statistics.  So the formula is:

(123639 yards + 20*786 TD – 45*415 Ints)/  18295 attempts 

(123639 yards + 15720 “TD” yards – 18675 “Int” yards) / 18295 attempts

120684 yards / 18295 attempts

6.60 AYA to 3 significant digits.

Now things become simpler. Matt Ryan generated 10.1*2.25 = 22.7 points per 30 attempts, while Joe QB generated 14.8 points per 30 attempts. The difference, rounded to a whole number, suggests that Matt Ryan was worth about 8 more points in 30 attempts than the average NFL QB this season.

That doesn’t entirely encompass his per game value. Matt threw 534 attempts  this season for an average of 33.4 passes per game. So his per game value, to the nearest tenth of a point, was more like 8.8 points a game more than the average quarterback.

But if the numbers baffle you, then the simple take home is that Matt’s statistical efficiency in 2016 is comparable to the best single season Peyton Manning ever had.

Possession of a ball in a ball game is a binary act. You either have it or you don’t. That means that the total value of stats associated with possession is also binary. This is true regardless whether the sport splits the value of a turnover in two or not, and notions of shared blame can cause issues when thinking about football. Football isn’t like other sports. Some of its “turnovers”, the punt especially, aren’t as easily quantifiable in the terms of other sports.

As an example of shared blame, we’ll take on the turnover in basketball. The potential value of the shot in the NBA is one point. This is easy to see, because a shot is worth 2 points and a typical NBA shooting percentage is about 50 percent (or a 3 point shot, with a percentage around 33%). That said, the value of the possession is two points, and  the total value of the turnover is also two points.

Wait a minute, you say. The STL stat is generally only valued at 1 point. How can it be two? Well, there are two stats associated with a turnover in basketball. There is the TO stat, and the STL stat. And in metrics like the NBA Efficiency metric, each of  these stats is valued at a point. TO + STL = total value of 2 points. The turnover in basketball is worth 2 points, and thus the possession is worth two points. The sum gets hidden because half of it is credited to the thief, and half is debited from the one who lost the ball.

The value of the turnover is the difference in value between the curves.

The classic description of the turnover in football derives from  the Hidden Game of Football, and because their equivalent points metric is linear and independent of down and to go measures, the resultant value for the turnover is a constant. This isn’t easy to see in traditional visual depictions, but becomes easy to see when you flip the opposition values upside down.

See how the relative distance between the lines never change? By the way, you can do the same thing for basketball, though the graph is a bit on the trivial side.

This curve probably should have some distance dependence, actually.

These twin plots are a valuable way to think about the game,  turnovers, and for that matter, the game of football as a series of transitions between states. For now, by way of example, we’ll use these raw NEP data I calculated for my “states” post. We’ll plot an opposition set of data upside down and show what a state transition walk might look like using these data.

The game of football can be described as a "walk" along a pair of EP curves.

Not that complicated, is it? You could visualize these data two ways: as a kind of “Youtube video” where the specific value for the game changes as plays are executed, and the view remains 2D, or as a 3D stack of planes, each with one graph, each plane representing the game at a single play in the game.

Even in football, though, you could attempt to split the blame for the turnover into two parts: there is the person that lost the ball, and the person that recovers it. So  the value for the state transition from one team to the  next could be split in two, a la basketball, and credit give to the recovering side and a debit taken from the side losing the ball.

So what about  the punt? It has no equivalent in basketball or baseball, and in general, looks just like a single state transition.

The punt, in this depiction, is a single indivisible state transition from one team to the other.

It’s a single whole, and therefore, you can get yourself into logical conundrums when you attempt to split the value of the punt in two.

This whole discussion, by  the way, is something of an explanation for Benjamin Morris and folks like him, who saw his live blog on October 9, 2011. It’s not easy getting this point across using his graphics on his site. My point is more fully developed above, and why I was saying the things I did more evident from the graphics above.

Ben, btw, is an awesome analytics blogger. Please don’t take this discussion as any kind of indictment of his work, which is of a very high quality.

It was just a bit of a lark, looking around, trying to find articles on books. It also was an excuse to look for football blogs, at least the kind that aren’t major corporations in disguise, or pretend somehow that their opinions are “professional”, as opposed to “amateur”. That distinction seems a little foolish to me, unless you’re, say, Jeff Ireland or Tim Newsome.

One of the first to appear on my  radar was a two article selection of books (here and here) from Football Relativity. I like this blog; it’s one I’d like to take seriously in terms of where I want to be in a few months. One thing the food blogging biz has taught me is that patience is everything as a blogger, and time and improvements are measured in months and years. People start coming when your content is good, and then stick around when your content routinely shows up. Persistently adding content is the hard part.

Another football blog I’ve found this way is Takin’ It to  the House. Lloyd Vance makes a living writing and analyzing sports. He also does radio. Though I’d consider him a media professional, his blog feels more quirky, more individual, more hip, less a product than most. His blogroll is definitely cool. His thoughts on good  NFL books can be found here.

Finally, from a blog more about books than football – books are a good thing – we get the following recommendations.

Back in the bad old days, if we wanted data sets for some football analysis, we typed them in ourselves. Later, and perhaps somewhat smarter, we find out that there are tools called spiders that we can use to scrape data off web sites and then put into spreadsheets or databases. I have an example of such a web tool here.

Later we find that people change their web sites routinely, that they use java and javascript to hide the data, that it’s no longer part of the static HTML at all. Part of this new usage is driven by advertising: the people putting up the web site want to know there is a human looking at their stuff, and not a machine.

Sure would be nice if people would simply supply football data in a machine readable form, wouldn’t it? Then you could get some of the advantages Jon Udall speaks about in his article, “Data should be free.

First, obviously, you need data. Then, more interestingly, you need to figure out ways for people to create, share, and collaboratively refine interpretations of the data…. Where else can you find data for these kinds of tools and services to chew on?

Yes, if multiple eyes can look at a single data set, then  you can also take advantage of  the “Cathedral and Bazaar” effect, which suggests that almost any problem becomes easy if enough eyes look at it.

Now, if you’re more the pay for it sort, there are at least three good sources I suggest you look at, and another I’ve found recently that seems intriguing. The three are Football Outsiders, Pro Football Focus, and Advanced NFL Stats. Then there is NFL Data, a web site that appears to be a kind of data reseller. Their FAQ is here.

The truth is,  the business of selling NFL data is a big one. Jaime  Spacco, who in 2001 put up an interesting data analysis presentation, has this to say about NFL data online:

My Dataset is NFL football data for the 2000 season that ended in January, 2001. I gathered the data from and from Statistics for previous seasons are not readily available in digital form, and often are not available free-of-charge. This seems to be because gamblers and fantasy football enthusiasts will pay quite a lot of money for this type of information.

This, of course, was in a relatively innocent period of Internet usage.

Checking the internet, this Infochimps article really only shows one data set of interest, from Football Outsiders, and it costs $30.00 to buy. There are a number of stalled attempts at group projects to create the Great All Encompassing Football Data Set. One such attempt, which lasted for one season, is here.

One of the more intriguing posts is yet another attempt to bring people together for an ambitious data project, and it was posted here. The important info in this link comes from the replies, which actually gives some really good looking data sets.

This leads to the best downloadable data set I can locate, the old Pro Football  Reference data set. They abandoned doing their own and now have a data feed from ESPN. But their old data are available, as a starting point.

Update: a more modern view of this whole topic is provided in this later article here.

For those people interested in the old Sports Illustrated Game Paydirt, and perhaps have charts but no playing field, I wrote playing field software in Perl.

The  Generic American Football Field is a way to play a Paydirt game without having any kind of physical playing field. All you need is a Perl interpreter that can run Tk.

A copy of GAFF is available in the Yahoo Paydirt group.

This is overall, a terrific book. It really does fill a void in the bookshelf of  the football fan, especially those weaned on “A Thinking Man’s Guide to Pro Football“. It has a number of good sections on various plays, players, and coaches, but it’s really a book driven by ideas. I really liked the sections on Don Coryell, the section on the spread option, the section on Jim Johnson’s blitzing defenses, and the very late section called “A-11 and beyond.”

The worst of the sections was the one on the 46. The diagram of the 46 was bad, and the discussion was inconsistent. I have Rex Ryan’s book, and so I can compare what Tim Layden says with a known authority on the 46. On page 189 of the hardback edition, Tim says

The 46 was a 4-3 defense, the base alignment Ryan liked best. But it was much more than a 4-3.

Uhm, no. The 4-3 is a 7 man front. And in the very first paragraph of Rex’s book, it says

Unlike the 4-3 slide and other “pass conscious”  7-man front schemes, the 46 is a fundamental defensive structure of the 8-man front family

Put succinctly, the 46 is a 6-2. Even the diagram  Tim has of the 46 is messed up. This is Tim’s diagram.

This is the closest equivalent from Ryan’s book.

Maybe they match. Maybe they don’t. Maybe the typical football fan reader wouldn’t know or care. But the defensive line shift in Ryan’s book is in the opposite direction of Tim’s and the linebackers are shifted to the strong side in Ryan’s book, not the weak side. Small things, like that, pop up in this discussion.

Overall, it doesn’t surprise me. The 46 is  the least understood defense in pro football.

Despite  these issues, I still think Tim’s book is to be very highly recommended, and a must buy for the serious fan.

I’ve  been writing mock draft software since about 2001. First version was written in C++ and mingw, using the standard template library. It’s part of a Sourceforge project. Second version was written in 2007, using Ruby. It’s also in the same Sourceforge project. My last version has been written using the Catalyst framework in Perl and lives on a virtual server on my home desktop.