Monday, August 11, 2014
Atlanta’s Jason Heyward tops the NL’s SABR Defensive Index™ rankings through games of August 3, 2014, while Alex Gordon leads a trio of Kansas City outfielders who all rank in the top 10 of the AL SDI rankings.
The SABR Defensive Index is used to help select the winners of the Rawlings Gold Glove Award® and Rawlings Platinum Glove Award™, presented by SABR.
Posted: August 11, 2014 at 04:14 PM | 20 comment(s)
Tuesday, August 05, 2014
Even the Mets, who watch Juan Lagares play every day, are sometimes amazed by the outfielder’s capabilities.
Lagares put on another defensive show in Monday’s loss to the Giants, throwing a runner out at home plate in the seventh inning to stop the bleeding during a potentially huge rally.
With one run already home and two men on base, Pablo Sandoval hit a sharp single to center field. Lagares charged, took a quick extra step and fired home, putting his entire body’s momentum behind the throw. It sailed about a foot to the third-base side of home plate, allowing catcher Travis d’Arnaud to catch it on the fly and apply the tag.
“There are a lot of good outfielders in this league, but Juan Lagares ranks right with anybody in this league defensively,” manager Terry Collins said. “He’s got a great arm.”
Tuesday, July 22, 2014
Lefthanded hitters have lost 22 points on their batting average on balls in play to rightfield this year alone – and 85 points in nine years.
Good material with some good numbers in here. But it’s questionable that this is the solution:
Support of an “illegal defense” rule – or at least the consideration of it – is gaining some traction in baseball. Such a rule might stipulate, for instance, that you cannot have three infielders on one side of second base. A shortstop would be able to shift as far as directly behind second base on a lefthanded hitter, but no farther.
Thursday, June 12, 2014
Some good plays here. As great as they are, all outfield arms must line up behind Ellis Valentine.
Posted: June 12, 2014 at 08:34 AM | 20 comment(s)
Wednesday, March 12, 2014
Sunday, March 09, 2014
Last week, Jeff Zimmerman at the Hardball Times released infield shift data for 2013. These data considered a shift to be on when the defense had three players on the same side of the infield (as in the Jays-Red Sox example above). The data that were made available included how often teams had a shift on when hitters put the ball in play, and the players that hit into the most shifts. Here, I am focusing on the former. Keep in mind that these data exclude home runs, strikeouts, walks (because those plate appearances do not end with a ball in play). Using these data, we can estimate the number of runs a team saved (or gave up) as a result of using the extreme infield shift.
Posted: March 09, 2014 at 09:16 PM | 15 comment(s)
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.
for his generous support.
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