Wednesday, March 05, 2014
Last Saturday in Boston, I was in the crowd for the public unveiling of MLBAM’s latest contribution to Big Data: a camera-and-radar-based system that tracks not only the baseball, but also every player on the field in great detail. I’m sure you’re familiar with PITCHf/x; well, that soon will be gone, replaced by this new system that doesn’t yet have a name (Jay Jaffe suggests OMGf/x, which is probably as good as anything else). Watching MLBAM’s presentation at the MIT-Sloan Sports Analytics Conference, I wrote these words in my notebook in big letters:
NOTHING WILL BE THE SAME
Hyperbolic? I don’t know. Maybe. But watch this demo and you tell me ...
Monday, March 03, 2014
Rather than identifying a single strike zone and giving binary credit for each pitch relative to that strike zone’s borders (i.e., strike or no strike), our model gives partial credit for each pitch based on that pitch’s likelihood of being called a ball or a strike. To determine that, we created a probability map of likely calls… To reflect what is best known about the way the size and position of the strike zone shifts from count to count and batter to batter, we ran individual models for each set of batter and pitcher handedness as well as [type of pitch]. The smoothing parameters of each model were allowed to vary by count, so that while the general shape of the strike zone derived for each variable combination did not change, the width and height of it did (reflecting, for example, a larger strike zone on 3-0 counts than on 1-2 or 0-2 counts). We also accounted for the changing size of the strike zone from season to season (although these yearly changes are much smaller than the other changes we measured).
We also corrected the data in several ways before running these models. First, all pitch classifications were hand-labeled by Pitch Info to eliminate variability in pitch labels… To account for batter height differences, we normalized the height of each pitch by the batter’s height using what is now the standard formula (first published by Mike Fast). We also used the correction scheme that Mike published at BP for correcting the X and Y location of each pitch based on the likely distribution of pitch locations that each pitcher would use against left-handed hitters and right-handed hitters…
Rather than simply give a single credit for each pitch (~.14 runs) as has been done in many previous models, we looked at the count in which each pitch was framed and gave credit equal to the difference in runs between framing or not framing that pitch. For example, a frame in an 0-2 count was counted as more valuable than a frame in an 0-0 count, because a frame in an 0-2 count can result in a large change in run expectancy while a frame in an 0-0 count does not have quite the same impact… The run value for a framed pitch is the run value differential for that count… multiplied by the residual of the probability—in other words, if an 0-0 pitch is called a strike in a spot where it’s normally called a strike just 80 percent of the time, the catcher will get 20 percent of the available value (.08) for a total of .0004 runs credited (which will later be adjusted based on the pitcher and umpire impact). Failing to get a strike on the same pitch would result in a .0016 run deduction…
We empirically determined each pitcher’s value—to isolate it from each catcher’s value—by performing a WOWY (“With or Without You”) analysis… We also made systematic but small changes to the data based on the umpire who was calling each game…
we have regressed career totals to the league average… Because seasonal variability is different from career variability, we also regressed seasonal totals to career totals based on a similar formula…
You can find all of this new framing and blocking information in a couple place on the Baseball Prospectus site.
Thursday, January 02, 2014
Friday, November 01, 2013
Nice collection of videos put together at AZ Snakepit. The guy was amazing all year. A defensive season for the ages, one of the best I have ever seen, and if the Fielding Bibile numbers are to be believed, it was just that.
Posted: November 01, 2013 at 09:53 PM | 3 comment(s)
Friday, September 27, 2013
Wednesday, September 25, 2013
Define “historic”, I suppose…
A few weeks ago we talked about how Andrelton Simmons is having the best defensive season since we started tracking advanced defensive metrics in 2003. It turns out that he has some company this year. Three other players are having historic-level defensive seasons as well—Gerardo Parra, Carlos Gomez and Manny Machado. With one week left in the season, all four of these players are on track to break Brett Gardner’s Defensive Runs Saved mark of 35 runs set in 2010. Here is where they stand this year in saving runs defensively for their teams:
Most Defensive Runs Saved, 2013
Player Position DRS
Andrelton Simmons, Braves SS 42
Gerardo Parra, Diamondbacks LF, CF, RF 40
Carlos Gomez, Brewers CF 37
Manny Machado, Orioles 3B 34
Tuesday, September 17, 2013
A week after last season ended, after the Pirates completed their second consecutive collapse, general manager Neal Huntington met manager Clint Hurdle at his Hampton home.
With little money to spend on free agents, few impact prospects ready for 2013 and many outside the organization calling for a regime change, Huntington and Hurdle discussed how to get more production from their returning roster.
The meeting led to the success behind the Pirates’ first winning season since 1992: an agreement to adopt perhaps the most aggressive, systematic approach to run prevention — from alignment to pitching strategy — in baseball history.
“It’s the conversation where we decided to push all the chips in,” Hurdle said. “It’s the most aggressive presentation and defensive program I’ve ever been around.”
The Pirates experimented with a comprehensive defensive philosophy the past several seasons, but this was different.
Posted: September 17, 2013 at 12:19 PM | 26 comment(s)
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