Jeff pointed me to this article by Nick Berry. It’s kind of fun but of course if you know your opponent will be following this strategy you can figure out how to outwit it. Also, Berry writes that ETAOIN SHRDLU CMFWYP VBGKQJ XZ is the “ordering of letter frequency in English language.” Indeed this is [...]
Archive of posts filed under the Sports category.
Don’t do the King’s Gambit
Tyler Cowen points to this interesting article by Vasik Rajlich and Frederic Friedel. I find these sorts of things fun to read, but the horrible thing is that I just get worse and worse as a player. I seem to have lost the ability to automatically filter out the worst moves. Not that I was [...]
The “hot hand” and problems with hypothesis testing
Gur Yaari writes: Anyone who has ever watched a sports competition is familiar with expressions like “on fire”, “in the zone”, “on a roll”, “momentum” and so on. But what do these expressions really mean? In 1985 when Thomas Gilovich, Robert Vallone and Amos Tversky studied this phenomenon for the first time, they defined it [...]
Of forecasts and graph theory and characterizing a statistical method by the information it uses
Wayne Folta points me to “EigenBracket 2012: Using Graph Theory to Predict NCAA March Madness Basketball” and writes, “I [Folta] have got to believe that he’s simply re-invented a statistical method in a graph-ish context, but don’t know enough to judge.” I have not looked in detail at the method being presented here—I’m not much [...]
Sports examples in class
Karl Broman writes: I [Karl] personally would avoid sports entirely, as I view the subject to be insufficiently serious. . . . Certainly lots of statisticians are interested in sports. . . . And I’m not completely uninterested in sports: I like to watch football, particularly Nebraska, Green Bay, and Baltimore, and to see Notre [...]
The Fixie Bike Index
Where are the fixed-gear bike riders? Rohin Dhar explains:
Where are the larger-than-life athletes?
Jonathan Cantor points to this poll estimating rifle-armed QB Tim Tebow as America’s favorite pro athlete: In an ESPN survey of 1,502 Americans age 12 or older, three percent identified Tebow as their favorite professional athlete. Tebow finished in front of Kobe Bryant (2 percent), Aaron Rodgers (1.9 percent), Peyton Manning (1.8 percent), and Tom [...]
Toshiro Kageyama on professionalism
Following up on our discussion of professionalism (in which Jonathan Chait argued that “the definition of a professional career track” requires pay differentials and the chance to get fired, and I argued the opposite, that a lot of people go into professional careers specifically because of the job security), Austin Frakt pointed me to this [...]
Bayesian Page Rank?
Loren Maxwell writes:
World record running times vs. distance
Julyan Arbel plots world record running times vs. distance (on the log-log scale): The line has a slope of 1.1. I think it would be clearer to plot speed vs. distance—then you’d get a slope of -0.1, and the numbers would be more directly interpretable. Indeed, this paper by Sandra Savaglio and Vincenzo Carbone (referred [...]
Grrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr
In a review of the movie Moneyball, David Denby writes:
45% hitting, 25% fielding, 25% pitching, and 100% not telling us how they did it
A University of Delaware press release reports: This month, the Journal of Quantitative Analysis in Sports will feature the article “An Estimate of How Hitting, Pitching, Fielding, and Base-stealing Impact Team Winning Percentages in Baseball.” In it, University of Delaware Prof. Charles Pavitt of the Department of Communication defines the perfect “formula” for Major League [...]
Rickey Henderson and Peter Angelos, together again
Today I was reminded of a riddle from junior high: Q: What do you get when you cross an elephant with peanut butter? A: Peanut butter that never forgets, or an elephant that sticks to the roof of your mouth. The occasion was a link from Tyler Cowen to a new book by Garry Kasparov [...]
Scrabble!
AT writes: Sitting on my [AT's] to-do list for a while now has been an exploration of Scrabble from an experimental design point of view; how to better design a tournament to make the variance as small as possible while still preserving the appearance of the home game to its players. . . . I’m [...]
Super Sam Fuld Needs Your Help (with Foul Ball stats)
I was pleasantly surprised to have my recreational reading about baseball in the New Yorker interrupted by a digression on statistics. Sam Fuld of the Tampa Bay Rays, was the subjet of a Ben McGrath profile in the 4 July 2011 issue of the New Yorker, in an article titled Super Sam. After quoting a minor-league trainer who described Fuld as “a bit of a geek” (who isn’t these days?), McGrath gets into that lovely New Yorker detail:
One could have pointed out the more persuasive and telling examples, such as the fact that in 2005, after his first pro season, with the Class-A Peoria Chiefs, Fuld applied for a fall internship with Stats, Inc., the research firm that supplies broadcasters with much of the data anad analysis that you hear in sports telecasts.
After a description of what they had him doing, reviewing footage of games and cataloguing, he said
“I thought, They have a stat for everything, but they don’t have any stats regarding foul balls.”
Inventor of Connect Four dies at 91
Obit here. I think I have a cousin with the same last name as this guy, so maybe we’re related by marriage in some way. (By that standard we’re also related to Marge Simpson and, I seem to recall, the guy who wrote the scripts for Dark Shadows.)
A statistician rereads Bill James
Ben Lindbergh invited me to write an article for Baseball Prospectus. I first sent him this item on the differences between baseball and politics but he said it was too political for them. I then sent him this review of a book on baseball’s greatest fielders but he said they already had someone slotted to review that book. Then I sent him some reflections on the great Bill James and he published it! If anybody out there knows Bill James, please send this on to him: I have some questions at the end that I’m curious about.
Here’s how it begins:
Minor-league Stats Predict Major-league Performance, Sarah Palin, and Some Differences Between Baseball and Politics
In politics, as in baseball, hot prospects from the minors can have trouble handling big-league pitching.
The Case for More False Positives in Anti-doping Testing
No joke. See here (from Kaiser Fung). At the Statistics Forum.
Bill James and the base-rate fallacy
I was recently rereading and enjoying Bill James’s Historical Baseball Abstract (the second edition, from 2001). But even the Master is not perfect. Here he is, in the context of the all-time 20th-greatest shortstop (in his reckoning): Are athletes special people? In general, no, but occasionally, yes. Johnny Pesky at 75 was trim, youthful, optimistic, [...]
Baseball’s greatest fielders
Someone just stopped by and dropped off a copy of the book Wizardry: Baseball’s All-time Greatest Fielders Revealed, by Michael Humphreys. I don’t have much to say about the topic–I did see Brooks Robinson play, but I don’t remember any fancy plays. I must have seen Mark Belanger but I don’t really recall. Ozzie Smith [...]
Online James?
Eric Tassone writes:
Chess vs. checkers
Mark Palko writes: Chess derives most of its complexity through differentiated pieces; with checkers the complexity comes from the interaction between pieces. The result is a series of elegant graph problems where the viable paths change with each move of your opponent. To draw an analogy with chess, imagine if moving your knight could allow [...]
As the saying goes, when they argue that you’re taking over, that’s when you know you’ve won
Hey, here’s a book I’m not planning to read any time soon! As Bill James wrote, the alternative to good statistics is not “no statistics,” it’s bad statistics. (I wouldn’t have bothered to bring this one up, but I noticed it on one of our sister blogs.)
Heat map
Jarad Niemi sends along this plot: and writes: 2010-2011 Miami Heat offensive (red), defensive (blue), and combined (black) player contribution means (dots) and 95% credible intervals (lines) where zero indicates an average NBA player. Larger positive numbers for offensive and combined are better while larger negative numbers for defense are better. In retrospect, I [Niemi] [...]