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SmartData Collective > Uncategorized > Saturday notes: The Frick and curve balls
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Saturday notes: The Frick and curve balls

StephenBaker1
StephenBaker1
4 Min Read
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Vermeer’s ‘Officer and Laughing Girl‘

Yesterday, after lunch in Midtown, I walked up to the Frick Collection. It’s a spectacular collection of European art–like a highly condensed version of the Louvre. The Web site lets you explore and zoom in on the paintings. Check out, for example, the sleeve on this Rembrandt self portrait.

Henry Clay Frick was a coal (coke) baron in Western Pennsylvania and made his money from the steel industry. He ordered the deadly crackdown at the Carnegie mill at Homestead, in 1892. This piqued my interest in the Goya that he bought (below), which features steelworkers.

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Speaking of steel, I’m heading out to Pittsburgh next week for a reporting trip, following a Lunch forum Monday at Penn State.

Goya’s ‘The Forge’

***

In my baseball geeky way, I’m enjoying this statistical study on the effectiveness of fastballs. The conclusion, based on the crunching of millions of pitches and the weighing of hundreds of variables, is that pitchers rely too much on the fastball.

A common example…

Vermeer’s ‘Officer and Laughing Girl‘

Yesterday, after lunch in Midtown, I walked up to the Frick
Collection
. It’s a spectacular collection of European art–like a
highly condensed version of the Louvre. The Web site lets you explore
and zoom in on the paintings. Check out, for example, the sleeve on
this Rembrandt self portrait.

Henry Clay Frick was a coal (coke) baron in Western Pennsylvania and made his money from the steel industry. He ordered the deadly crackdown at the Carnegie mill at Homestead, in 1892. This piqued my interest in the Goya that he bought (below), which features steelworkers.

Speaking of steel, I’m heading out to Pittsburgh next week for a reporting trip, following a Lunch forum Monday at Penn State.

Goya’s ‘The Forge’

***

In my baseball geeky way, I’m enjoying this statistical study
on the effectiveness of fastballs. The conclusion, based on the
crunching of millions of pitches and the weighing of hundreds of
variables, is that pitchers rely too much on the fastball.

A common example. Let’s say a
pitcher is behind in the count, 3-1, to a good hitter. One more ball and the batter
walks. So the batter is expecting a fastball, which is easier for the
pitcher to control. He’s ‘sitting’ on the pitch, in baseball parlance.
And his chance of getting a hit are higher. Cagey pitchers with great
control, like Greg Maddux and Jamie Moyer, built careers from throwing
change-ups and curves in hitter’s counts.

Statistics indicate that others should do the same. The danger of
walking the batter by throwing a bad curve, it appears, is less than
the risk that comes from throwing a fastball he’s ready for. This is
the way pitchers throw to great hitters like Albert Pujols. Now it
seems they should treat every batter like a superstar. (For those eager to dive deeper into baseball math, here’s a post on how outfielders calculate the trajectory of fly balls.)

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TAGGED:statistical analysis
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