Jose Iglesias has a .507 BABIP this year. This article is not about that BABIP, exactly, but we are starting there. Iglesias entered the season with a .164 career BABIP in the majors, and a .300 BABIP in the minors, and a reputation as the best defensive shortstop in baseball, with a bat that might be just weak enough to support that glove. Finding out Jose Iglesias has a .507 BABIP is like finding out that Chin-lung Hu quietly signed with the Pirates and hit 14 home runs in May. Anyway, like I said, this article isn't about that BABIP.
A year ago, we did a blind BABIP test for a Jake Peavy start; 20 balls put in play, 10 were hits, and you tried to guess which were which based on all the information you could collect up to the point of contact. Gosh, did you ever do terribly. Given a 50 percent chance of guessing the correct answers blindly, you collectively got 52 percent of the answers correct. But maybe that wasn't fair; maybe focusing on the pitcher (who, as we know, controls his BABIP only a little bit) is a doomed exercise. Hitters control their BABIP some bit more than that. So maybe we should be focusing on the batter, looking to see if he's balanced and putting a good swing on the ball or flailing, jammed, late, or on top of the ball. So what happens if we do this from the batter's perspective? Will we be any better? I suspect... well, honestly, I don't know.
Ben and Sam discuss whether a pitcher's body language can cost him strikes, whether it's worth trading for relievers early in the season, a study about perceptions of steroid use, and whether a low BABIP is always unlucky.
A Scutaro hot streak and slump explain why the "good luck" and "bad luck" narratives don't always make sense.
Some players’ stat pages are interesting for any number of reasons. Others are nondescript, save for a single defining stat that stands out so much more than all the others that you quickly come to associate the player with that particular category. Marco Scutaro is a “single stat” guy.
Scutaro’s defining characteristic is that he makes more contact than anyone else. When someone says “Marco Scutaro” 10 years from now, you won’t think about that one time he led the league in sac flies, which his black ink would have us believe was the only time he led the league in anything. You might remember his unusual career arc: a utility guy throughout his 20s who “clearly was put on Earth to be a reserve,” according to Baseball Prospectus 2006, Scutaro bloomed late and became an above-average starter at shortstop in his early- to mid-30s. But mostly you’ll remember that his bat touched the ball on roughly 95 percent of his swings, and that he cut down on his K’s as his career went on while the rest of the league’s strikeout rate rose.
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What can we learn about hitting from a pitcher with five career hits?
As you know, pitchers seem to demonstrate a small amount of control over their BABIPs, and hitters seem to demonstrate a larger amount of control over their BABIPs. Within reason, at least. No active player has a career BABIP below .244, and no active player has a career BABIP higher than .368, unless you lower the plate appearance threshold to something too low to be significant.
But if you lower the plate appearance threshold to something too low to be significant, then you get to include everybody, including pitchers. Your BABIP if you played baseball would likely be null, because you wouldn’t put any BIP. But if you did put some BIP, your BABIP would be something ridiculously low, like .098 or something absurd.
BABIP spikes may be tough to read, but adding another unstable stat to the mix doesn't seem to help.
Last week, industry colleague Michael Salfino penned an interesting article for Yahoo! Sports discussing BABIP and how we might be able to tell legitimately good BABIPs apart from lucky ones (and legitimately bad BABIPs apart from unlucky ones). A couple of you pointed the article out to me and asked for my take on it, so I thought it best to simply write up a post in case others were interested. The article opens with:
The results of the blind BABIP test are in. How did you do? And what can we learn from your answers?
On Friday, many of you took the blind BABIP test. I gave you 18 GIFs, in nine sets of two, each set comprising two batted balls. One was a hit. The other was an out. You guessed which was which, but you couldn’t see the outcome; the GIFs cut off at the frame just as contact was made, or just before contact was made. This was supposed to tell us something. I’ll get to the big result first: We’re the worst at this!
I tallied 82 full sets of answers, which is 738 individual guesses, of which 387 were correct. That is 52 percent correct. Closing our eyes and pointing would theoretically have earned us 369 correct answers. All the wisdom of the 82 of you was worth 18 extra correct answers. So that's the big thing first.
Can you tell which pitches will leads to hits and which will lead to outs without seeing the results?
If we want to evaluate a pitch, there are few things we can focus on. We can look at the qualities of the pitch itself as it moves toward home plate, including movement, pitch type, and location. We can look at the catcher's glove, to see how much it moves from its target. We can look at the batter, to see how balanced he is as he swings at it. And we can look at the result: hit, out, stung, dribbled. I have a theory, which is that we (non-scouts) are mostly unable to make much of the first, second and third ways. That, mostly, we only remember the fourth.
So what follows is an experiment. I don't know what the point of this experiment is or what it will show. I don't know the best way to conduct this experiment. This might be an experiment I revisit in a better form someday in the future. But the experiment is simple, and I think it will be interesting, and I can't wait.
Every bloop, bleeder, and swinging bunt that has contributed toward the Braves setup man's .458 BABIP in 2012.
A few days ago, I got an email from someone who wanted to know why Jonny Venters isn’t dominating people like he did last year. He speculated that there’s something wrong with his stuff, or that his mechanics might be off.
I started formulating an answer even before I looked at the numbers. Well, it’s too small a sample to draw conclusions. Well, Venters was so good in 2011 that it’s unfair to expect a repeat performance. Well, he led the league in appearances last year, so maybe he’s feeling some fatigue.
A look at which pitchers were lucky as a result of their BABIP and LOB%, which were unlucky, and which may have a chance of repeating what looks like a lucky/unlucky 2011
Last season, one of my favorite baseball reads that became useful fantasy knowledge was this piece by Rich Lederer at Baseball Analysts. What he laid out is something that I’ve recommended and used in previous years as a quick and dirty way to look for potential targets at the end of drafts. If you believe in simple regression to the mean, it makes sense to target pitchers that were well below their personal and/or league average, since logic dictates they should do better the following season. As Lederer put it:
Mike continues his investigation of HITf/x data to glean more insights into whether pitchers can prevent hits on balls in play.
In the first part of this study, I used detailed batted ball speed information from HITf/x to examine the degree of skill that batters and pitchers had in quality of contact made or allowed. Here, I will look deeper into the question of why some batted balls fall for hits and others do not.