- Home Archives for August 2013
How to Lie with Statistics
By
John Morgan
Stochastic Football
Statistics make me cringe. Not the upstanding work done by Brian but too much analysis done by too much of the greater sportswriting community. That word, “analysis,” it doesn't help matters. Like many backwards things Western Civilization, modern usage derives from Aristotle. Real analysis, that's a marvel. What I encounter throughout the web would be better labeled: statistical rhetoric: the use of statistics to forward a previously held opinion. There's this great quote by Wayne C. Booth about critical theory—specifically the idea of showing versus telling in fiction writing—and how it disseminated from scholarly critics, down to commercial critics … well, I'll just share it:
“[T]he legitimate defense of the new soon froze into dogma. … [W]hen such rule-making descended further into the hands of unabashed commercial critics, it was simplified to the point of caricature.”
This is the progression: from new to
accepted to—in some skeletal, bastardized form—the mother truckin
law. Wanna be taken seriously? Gotta speak the language. For the
modern sportswriter statistics are jargon, argot and shibboleth all
in one. No wonder an old hat striving for relevance rushed to create
an eponymous, um, effect? despite its obvious bogusness.
Brian attracted me to Advanced NFL
Stats through his work exposing the phoniness of the so-called Curse of 370. His simple, clearly worded argument of why said curse was
cooked up, and either indicative of blithe error or chicanery,
challenged me to be careful and inquiring instead of gullible. So in honor of Mr. Burke and his fine and
reputable site, I now intend the exact opposite. Let us together
learn how to lie with statistics.
published on 8/31/2013
with
7
comments
Why Standard Fantasy Football Rots and How To Fix It
By
John Morgan
My mother-in-law plays a dice game called Farkel. It's a game of simple math, but mostly a game of chance—more War than chess, more cleromancy than game theory. If I were stuck with her in a windowless room without my toothbrush but with a woman named Estelle, if we were stuck for all eternity, perhaps we could determine if her, ahem, extreme prudence or my more reckless style were superior. But as-is there's a lot of guessing, a lot of premature revelry and a lot of empty opining about strategy. If I may: the game sucks. It's an excuse for people of a certain age to get together and drink.
Fantasy football is a 70ドル billion market. I can't believe I just wrote that. That … that number was
not planned.
… give me a second …
23 million people play fantasy
football, and while I could not find an exact number, a not terribly
scientific or thorough accounting of the number of Yahoo public
leagues versus the number of Yahoo private leagues, indicates most
play with standard rules. And many customized leagues are close
enough to standard rules to, for my purposes, be comparable.
Fantasy football played by standard rules is a
rotten game, requiring little skill, that, in its crappiness is a bad reason even to get together and drink. Here's why:
published on 8/24/2013
with
21
comments
Bombast Revisited; A Partial List of My Cognitive Biases
By
John Morgan
Stochastic Football
Bombast Revisited
It's said a joke explained is a joke
ruined. So how about satire? I wrote a loudmouth, accusatory post. It
was fun. My intention: to
appropriate the tools of popular sports punditry: attention
grabbing headline, us vs. them framing, a hectoring tone, abundant
self-assurance, straw men galore, unsourced data and an adamantine
sense of moral authority. And here's what happened: the response was
almost universally negative, and often outright caustic. But the post
netted big traffic: the most since Brian's point/counterpoint on
Aaron Rodger's extension, and more than double any post written since
May.
published on 8/16/2013
with
5
comments
Here's a Thought
By
Brian Burke
If it's ok for coaches to use the preseason to experiment with Peyton Manning in the pistol and risk Jay Cutler in the read-option, then it's ok to give your punt team a game off. Put away the 21 personnel I-formation and go with the 11 personnel on all four downs. Go for it. Be aggressive. Test the limits. It's the preseason. There are three other preseason games and 40 practices for you to hash out roster spots 52 and 53 on your punt and field goal units.
published on 8/15/2013
with
14
comments
Call for Writers 2013
By
Brian Burke
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:
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:
published on 8/13/2013
in
site news
with
0
comments
The Pay-Performance Linear Model
By
Brian Burke
A couple months ago I posed an apparent paradox. Aaron Rodgers' new 21ドルM/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.
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.
published on 8/12/2013
in
basic,
research,
salary
with
13
comments
A HOF Game Preview Without Irony
By
John Morgan
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.
published on 8/02/2013
with
2
comments
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