Archive for batted ball

Long has the relationship between pitcher performance and batted ball metrics been dubious. The Sabermetric community has a solid understanding of why, fundamentally, a pitcher is good or bad. Strikeouts are good. Walks are bad. Hits by pitch are also bad. Home runs allowed are especially bad. So on, so forth. And by no means are batted ball metrics useless. It’s how we know ground balls allowed are superior to fly balls allowed, for example.

The community had hoped, however, that more granular batted ball metrics would help us better explain some of the more nuanced elements of pitcher performance, including those related to luck, such as batting average on balls in play (BABIP) and the percentage of home runs per fly ball (HR/FB). Since their introduction to the public sphere in 2015, and even with the inclusion of more granular Statcast data in 2016, any relationships that might exist between the physics and outcomes for batted balls during an individual pitcher’s season are still poorly explained. The following table depicts the correlations between pitcher BABIP and various batted ball metrics, sorted by the strength of the relationship (all qualified seasons, 2007-17, n = 898):

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FanGraphs’ leaderboards are like iron ore mines. They abound with potentially valuable commodities, but sometimes it takes effort to unlock the potential and extract the value.

There’s only so much you can write about a certain subsection of players (National League outfielders) without beating a dead horse. Alas, I’ve tried to find peculiar reasons to write about particularly interesting hitters. And, ah, FanGraphs’ leaderboards, they’re helpful in this regard, especially when broken down by splits.

Baseball Info Solutions’ (BIS) opposite-field batted ball data are no exception, as exemplified by this table of opposite-field hard-hit rate (Hard%) leaders among NL outfielders:

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Man, I am having a blast with the Baseball Info Solutions batted ball data that was recently added to the batted ball leaderboards. Sure, there are reasons to complain: the batted ball spray and contact quality statistics lack context, leaving you in the dark about how spray and contact intersect. For example, there’s Hard%, and there’s LD%, but how many of a hitter’s balls in play are hard line drives? (You can actually find this data on individual player pages under the “Splits” tab — just not on the leaderboards.)

Just because the available data aren’t as granular as one might wish they were doesn’t make them worthless or unusable. Yesterday, I demonstrated that we can still achieve small gains in our understanding of batting average on balls in play (BABIP) using the new data.

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You may have noticed that FanGraphs now feeds batted ball data, courtesy of Baseball Info Solutions, into its leaderboards. The day the data appeared, my mind buzzed with ways they could be useful in improving our understanding of a hitter’s batting average on balls in play (BABIP).

Mike Podhorzer already augmented previous attempts at devising an equation for expected batting average on balls in play (xBABIP) for hitters by incorporating elements of a hitter’s power, speed, plate discipline and batted ball tendencies. So, with fresh numbers in hand, I embarked on a journey to further improve the ever-evolving xBABIP. However, I sought to do so by using only batted ball data. Basically, I intended to develop a convenient xBABIP equation, one that can be computed using almost entirely variables found on the same page.

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I’m a dweller in general. In slow drafts, I am the ultimate dweller. In the past, I would have ten+ tabs open at a given time when my draft slots were approaching: FanGraphs player profiles; RotoGraphs consensus rankings; Brooks Baseball Player Cards; BP’s PITCHf/x leaderboard; Rotoworld player news; the list goes on.

Then, prior to draft season, Jeff, Eno and I worked on these Arsenal Scores so that you can compare pitcher repertoires. You now have a go-to pitcher matrix if you’re dwelling on a cluster of pitchers.

Last week, Jeff Zimmerman furnished this glorious Hitter Analytics post, which provided us with a go-to hitter matrix when we’re dwelling on a cluster of hitters.

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Let’s start with a multiple choice question.

1) Russell Martin has been lucky on the following balls-in-play type(s):

a) Grounders

b) Liners

c) Flies

d) at least Grounders and Flies

If you chose a) grounders, you would be wrong. If you chose d) flies, you would be wrong. If you chose b) liners, well… you could be partly right, however, the correct answer is d) at least grounders and flies. You could argue that there should be an e) option, ‘all of the above.’

According to Zach Sanders’ End of Season Catcher rankings, Russell Martin bamboozled his way into the top 10 at #7 overall and produced what would have been his 2nd best fantasy season if he approached 150+ games.

By bamboozled, I mean BABIP’ed.

Look at his 2014 ground ball, fly ball and line drive-related BABIP’s on each individual balls-in-play type in 2014 relative to his career; relative to 2013 (still with the Pirates); and relative to the mean if we considered his career rates as a one year performance:

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