Call for Writers 2013

It's time to report for training camp.

This season Advanced NFL Stats is planning to add a small number of additional contributors. I’m looking for smart, articulate thinkers to bring a fresh perspective to the world of NFL analytics. Previous analysis or blogging experience is preferred, but not required.

Those interested should email me directly (see the About - Contact/FAQ menu link) no later than August 25th. Include the words 'Call for writers' in the subject line, please. Your email should include a brief introduction and links or attachments of two or more examples of your analysis and writing.  If you have no previous experience, you can send ‘demo’ drafts of the kind of analysis you’d like to do.

In particular, I’m looking for contributors to ‘own’ a regular weekly assignment. For example:

The Pay-Performance Linear Model

A couple months ago I posed an apparent paradox. Aaron Rodgers' new $21M/yr contract was either a solid bargain or a disastrous ripoff depending on how we analyze the data. By only flipping the x and y axes of a scatterplot, we can come to completely opposite conclusions about the value of a QB relative to what we'd expect for a given salary or for a given level of performance. Much of this post is derived from the many insightful comments in the original. Please take the time to read them, especially those from Peter, X, Phil and Steve.

By regressing salary on performance (adjusted salary cap hit on the vertical (y) axis and Expected Points Added per Game (EPA/G) on the horizontal (x) axis), Rodgers' deal is insanely expensive by conventional standards. But by regressing performance on salary, his new contract is a bargain.

Which one is correct? That depends on several considerations. First, there are generally two types of analyses. The one I do most often is normative analysis--what should a team do? The second type is descriptive analysis--what do teams actually do? The right analytic tool can depend on which question we are trying to answer.

The reason that we saw two different results by swapping the axes is that Ordinary Least Squares (OLS) regression chooses a best-fit line by minimizing the square of the errors between the estimate and the actual data of the y variable. OLS therefore produces an estimate that naturally has a shallow slope with respect to the x axis. When we swap axes, the OLS algorithm is not symmetrical because of that shallowness.

A HOF Game Preview Without Irony

Stochastic Football

 

Plans and Gambles Among Former Etruscan Pirates

By team efficiency Miami finished on the low-end of average in both offense and defense. Any other year, rookie quarterback Ryan Tannehill's performance would have been thought promising. He wasn't good, but typically rookie quarterbacks are not good, and he wasn't so bad as to seem unsalvageable. Rummaging through the last 13 years of data: Carson Palmer, Eli Manning, Jay Cutler, Joe Flacco and Matthew Stafford all performed comparably or worse than Tannehill. Two things work against Tannehill: he was bad in a season when three other rookie quarterbacks were very good to excellent, but that's more a matter of perception. And he's old. At 25, he's but months younger than Stafford and Josh Freeman. He's not comparing brands of glucosamine chondroitin with Brandon Weeden, but he's not a baby face still growing into his body. As an athlete, Tannehill's arrived.

Optimizing a Swim Meet: Traveling Salesmen and Asexual Mutants

You wouldn't think there is much to swimming analytics. Compared to sports like baseball or football, swimming is extremely deterministic. Swimmers tend to have a certain speed in each stroke, and they vary only slightly around that tendency from meet to meet. There are no interactions with teammates, collisions with opponents, or bouncing balls to worry about. But it turns out that there's more to aquametrics than meets the eye.

My kids are on a summer swim team in the local league. It's a great activity--exercise, a bit of healthy competition, and all four of my kids and step-kids are on the same team for one season out of the year. It's a lot of fun for everyone.

It's complicated, though. There are five age groups for both boys and girls for a total of ten competition groups. There are 4 strokes (fly, back, breast, and freestyle). Each swimmer is assigned to one of three classes for each stroke. The A class has the faster kids, the B class the next faster kids, and the C class has the rest. First, second, and third place in each stroke-class (for each age/gender group) earn points for the team. The A, B, and C classes all count equally, in the spirit of the league. It's 5 points for a 1st, 3 for a 2nd, and 1 for a 3rd, regardless of class. This way, nearly everyone's performance can affect the outcome of the meet.

There's only one strategic variable in the meet. Each swimmer can only swim in 3 of the 4 stroke events in the meet. In other words, each swimmer has to skip one of the four strokes for the day. The manager of each team seeds the meet a couple days beforehand. You'd think that the best strategy is to have each swimmer participate in their 3 best strokes. But that's not the case.

Do Those Who Deny Advanced Statistics Even Watch the Game?

Stochastic Football

We understand your hostility. Maybe we're dangerous. Would it comfort you if I guaranteed that we are dangerous? And maybe we're dangerous to you. Do you prize your memory, your experience? Do you have knockdown, drag-out fights about what just happened, what you saw, what you know? Are you infected with certainty? Does this, this quote to follow, does it resonate within you, move you to potency, make you want to pressure wash something?

Inaction breeds doubt and fear. Action breeds confidence and courage. If you want to conquer fear, do not sit home and think about it. Go out and get busy. —Dale Carnegie

Joint Statistical Meetings

I've been invited to speak at the Joint Statistical Meetings this year, an annual event hosted primarily by the American Statistical Association. It will take place August 3-8 in Montreal. The theme of the talk is Big Data in Sports. My talk is scheduled for Monday the 5th. So if you'll be in Montreal for the conference, please come by and say hi.

Gagnez le jour, Alouettes!

Exploring the Causes of a Sack Pt. 1

A guest post by David Giller. Born and raised in Swampscott, MA, David attended Vanderbilt University where he was the starting longsnapper for the Commodores. He graduated summa cum laude with a degree in economics/corporate finance. David currently works as a business analyst for a Bain Capital Ventures portfolio company.

I would first like to thank Brian for his suggestion to post my study here in an effort to spark some interesting conversation and obtain some valuable takeaways. My post contains the results of a recent study I put together which focuses on the causes of sacks in NFL games. Although it is fairly detailed, I believe there are still areas of further development, some of which have been explored in an appendix to this initial study and will be coming in the second installment of this post.

As a disclaimer, the number of sacks were provided from an official source; however, the timing of the sacks, count of offensive blockers/defensive rushers was determined from my individual film study.The full piece is attached in a link, however, I have included some highlights below.

Point / Counterpoint on Rodgers' Extension

Today we're going to try a new format here at ANS--a debate between me and myself on the market value of Aaron Rodgers' recent contract extension. Rodgers recently signed a deal adding 5 years to his current contract. This will pay him roughly $21M per season over the next 3 years. See if you can figure out which Brian has the right idea and why they get different results.

Brian 1: Rodgers' new deal is a fantastic bargain. He's one of the truly elite QBs in the league today, and guys like that don't grow on trees. But more scientifically, just look at this super scatterplot I made of all veteran/free-agent QBs. The chart plots Expected Points Added (EPA) per Game versus adjusted salary cap hit. Both measures are averaged over the veteran periods of each player's contracts. I added an Ordinary Least Squares (OLS) best-fit regression line to illustrate my point (r=0.46, p=0.002).

Rodgers' production, measured by his career average Expected Points Added (EPA) per game is far higher than the trend line says would be worth his $21M/yr cost. The vertical distance between his new contract numbers, $21M/yr and about 11 EPA/G illustrates the surplus performance the Packers will likely get from Rodgers.

(This plot includes for all free-agent or veteran extensions since 2006. Cap figures are averaged for each player's career and, to account for cap inflation, are adjusted for overall league cap ceiling by season. Only seasons with 7 or more starts were included.)