Writing

Brains on the Line

I was reminded this morning that Mario Manningham, a wide receiver who played for Michigan was rumored to have scored a 6 (out of 50) on the Wonderlic, an intelligence test administered in some occupations (and now pro football) to check the mental capability of job candidates. Intelligence tests are strange beasts, but after watching my niece working on similar problems—for fun—during her summer vacation last week, the tests caught my eye more than when I first heard about it.

Manningham was once a promising undergrad receiver for U of M, but has in recent years proven himself to be a knucklehead, loafing through plays and most recently making headlines for marijuana use and an interview on Sirius radio described as “… arrogant and defensive. When asked about the balls he dropped in big spots, he responded, ‘What about the ball I caught?’” So while an exceptionally score on a standardized test might suggest dyslexia, the guy’s an egotistical bonehead even without mitigating factors.

Most people don’t associate brains with football, but in recent years teams have begun to use a Wonderlic test while scouting, which consists of 50 questions to be completed in 12 minutes. Many of the questions are multiple choice, but the time is certainly a factor when completing the tests. A score of 10 is considered “literate”, while 20 is said to coincide with average intelligence (an IQ of 100, though now we’re comparing one somewhat arbitrary numerically scored intelligence test with another).

In another interesting twist, the test is also administered to players the day of the NFL combine—which means they first spend the day running, jumping, benching, interviewing, and lots of other -ings, before they sit down and take an intelligence test. It’s a bit like a medical student running a half marathon before taking the boards.

Wonderlic himself says that basically, the scores decrease as you move further away from the ball, which is interesting but unsurprising. It’s sort of obvious that a quarterback needs to be on the smarter side, but I was curious to see what this actually looked like. Using this table as a guide, I then grabbed this diagram from Wikipedia showing a typical formation in a football game. I cleaned up the design of the diagram a bit and replaced the positions with their scores:

positions1.png

Offense is shown in blue, defense in red. You can see the quarterback with a 24, the center (over 6 feet and around 300 lbs.) averaging higher at 25, and the outside linemen even a little higher. Presumably this is because the outside linemen need to mentally quick (as well as tough) to read the defense and respond to it. Those are the wide receivers (idiot loud mouths) with the 17s on the outside.

To make the diagram a bit clearer, I scaled each position based on its score:

positions2.png

That’s a little better since you can see the huddle around the ball and where the brains need to be for the system of protection around it. With the proportion, I no longer need the numbers, so I’ve switched back to using the initials for each position’s title:

positions3.png

(Don’t tell Tufte that I’ve used the radius, not the proportional area, of the circle as the value for each ellipse! A cardinal sin that I’m using in this case to improve proportion and clarify a point.)

I’ll also happily point out that the linemen for the Patriots all score above average for their position:

Player Position Year Score
Matt Light left tackle 2001 29
Logan Mankins left guard 2005 25
Dan Koppen center 2003 28
Stephen Neal right guard 2001 31
Nick Kaczur right tackle 2005 29

A position-by-position image for a team would be interesting, but I’ve already spent too much time thinking about this. The Patriots are rumored to be heavy on brains, with Green Bay at the other end of the spectrum.

An ESPN writeup about the test (and testing in general) can be found here, along with a sample test here.

One odd press release from Wonderlic even compares scores per NFL position with private sector job titles. For instance, a middle linebacker scores like a hospital orderly, while an offensive tackle is closer to a marketing executive. Fullbacks and halfbacks share the lower end with dock hands and material handlers.

During the run-up to Super Bowl XXXII in 1998, one reporter even dug up the Wonderlic scores for the Broncos and Packers, showing Denver with an average score of 20.4 compared to Green Bay’s 19.6. As defending champions, the Packers were favored but wound up losing 31-24.

Nobody cited test scores in the post-game coverage.

Wednesday, July 16, 2008 | football, sports  

Derek Jeter Probably Didn’t Need To Jump To Throw That Guy Out

05jeterderek14.jpgDerek Jeter vs. Objective Reality is an entertaining article from Slate regarding a study by Shane T. Jensen at the Wharton School. Nate DiMeo writes:

The take-away from the study, which was presented at the annual meeting of the American Association for the Advancement of Science, was that Mr. Jeter (despite his three Gold Gloves and balletic leaping throws) is the worst-fielding shortstop in the game.

The New York press was unhappy, but the stats-minded baseball types (Sabermetricians) weren’t that impressed. DiMeo continues:

Mostly, though, the paper didn’t provoke much intrigue because Jeter’s badness is already an axiom of [Sabermetric literature]. In fact, debunking the conventional wisdom about the Yankee captain’s fielding prowess has become a standard method of proving the validity of a new fielding statistic. That places Derek Jeter at the frontier of new baseball research.

Well put. Mr. Jeter defended himself by saying:

“Maybe it was a computer glitch”

What I like about the article, aside from a objective and quantitative reason to dislike Jeter (I already have a quantity of subjective reasons) is how the article frames the issue in the broader sports statistics debate. It nicely covers this new piece of information as a microcosm of the struggle between sabermetricians and traditional baseball types, while essentially poking fun at both: the total refusal of the traditional side to buy into the numbers, and the schadenfreude of the geeks going after Jeter since he’s the one who gets the girls. (The article is thankfully not as trite as that, but you get the idea.)

I’m also biased since the metric in the paper places Pokey Reese, one of my favorite Red Sox players of 2004 as #11 amongst second basemen between 2000-2005.

And of course, The Onion does it better:

Experts: ‘Derek Jeter Probably Didn’t Need To Jump To Throw That Guy Out’

BRISTOL, CT—Baseball experts agreed Sunday that Derek Jeter, who fielded a routine ground ball during a regular-season game in which the Yankees were leading by five runs and then threw it to first base using one of his signature leaps, did not have to do that to record the out. “If it had been a hard-hit grounder in the hole or even a slow dribbler he had to charge, that would’ve been one thing,” analyst John Kruk said during a broadcast of Baseball Tonight. “But when it’s hit right to him by [Devil Rays first-baseman] Greg Norton, a guy who has no stolen bases and is still suffering the effects of a hamstring injury sustained earlier this year… Well, that’s a different story.” Jeter threw out Norton by 15 feet and pumped his fist in celebration at the end of the play.

In other news, I can’t believe I just put a picture of Jeter on my site.

Monday, July 14, 2008 | baseball, mine, sports  

Doin’ stats for the C’s

A New York Times piece by the Freakonomics guys about Mike Zarren, the 32-year-old numbers guy for the Boston Celtics. While statistics has become more-or-less mainstream for baseball, the same isn’t quite true for basketball or football (though that’s changing too). They have better words for it than me:

This probably makes good sense for a sport like baseball, which is full of discrete events that are easily measured… Basketball, meanwhile, might seem too hectic and woolly for such rigorous dissection. It is far more collaborative than baseball and happens much faster, with players shifting from offense one moment to defense the next. (Hockey and football present their own challenges.)

But that’s not to say that something can be gained by looking at the numbers:

What’s the most efficient shot to take besides a layup? Easy, says Zarren: a three-pointer from the corner. What’s one of the most misused, misinterpreted statistics? “Turnovers are way more expensive than people think,” Zarren says. That’s because most teams focus on the points a defense scores from the turnover but don’t correctly value the offense’s opportunity cost — that is, the points it might have scored had the turnover not occurred.

Of course, the interesting thing about sports is that at their most basic, they cannot be defined by statistics or numbers. Take the Celtics, who just won the first round of the playoffs. Given their ability, the Celtics should have dispensed with the Hawks more quickly, rather than needing all seven games of the series to win the necessary four. The coach in the locker room of any Hoosiers ripoff will tell you it doesn’t matter what’s on the stat sheets, it matters who shows up that day. It’s the same reason that owners cannot buy a trophy even in a sport that has no salary cap. Or, if you’re like some of my in-laws-to-be (all Massachusetts natives), you might suspect that the fix is in (“How much money do those guys make per game?”) Regardless, it’s the human side of the sport, not the numbers, that make it worth watching. (And I don’t mean the soft-focus ESPN “Outside the Lines” version of the “human” side of the sport. Yech.)

In the meantime, maybe the Patriots or the Sox are hiring…

(Passed along by Andy Oram, my editor for vida)

Monday, May 5, 2008 | sports  
Book

Visualizing Data Book CoverVisualizing Data is my book about computational information design. It covers the path from raw data to how we understand it, detailing how to begin with a set of numbers and produce images or software that lets you view and interact with information. Unlike nearly all books in this field, it is a hands-on guide intended for people who want to learn how to actually build a data visualization.

The text was published by O’Reilly in December 2007 and can be found at Amazon and elsewhere. People who have purchased the book can find the examples here.

The book covers ideas found in my Ph.D. dissertation, which is basis for Chapter 1. The next chapter is an extremely brief introduction to Processing, which is used for the examples. but applies them to a series of examples, first starting with a simple mapping project (Chapter 3) to place data points on a map of the United States. Of course, the idea is not that lots of people want to visualize data for each of 50 states. Instead, it’s a jumping off point for learning how to lay out data spatially.

The chapters that follow cover six more projects, such as salary vs. performance (Chapter 5), zipdecode (Chapter 6), followed by more advanced topics dealing with trees, treemaps, hierarchies, and recursion (Chapter 7), plus graphs and networks (Chapter 8).

This site will be used for follow-up code and writing about related topics.