Archive for pull

Where the defensive shift and batting average on balls in play (BABIP) intersect intrigues me, but I’ve had a hard time figuring out a way to quantify it without having some sort of access to shift data. Despite advances Major League Baseball has made in measuring and collection data, not all of this information is publicly available or easily accessible, even if you know someone who knows someone (this guy).

But I think I finally had some kind of breakthrough or epiphany or what-have-you. It would be a time-intensive approach — a problem for a lazy person (this guy) — but it would be worth it to, perhaps, chip away at the relatively enigmatic BABIP with only publicly available tools at our disposal.

More than four months ago, I posted an expected BABIP (xBABIP) equation that is not necessarily better than any other that exists but does use strictly publicly available data. Here, I expand.

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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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