Archive for hard%

Hard%, xwOBA, and the De-Juiced Ball

The league-wide hard-hit rate (Hard%) is up. Like, way up, at its highest level by far in the 17 years Baseball Info Solutions has measured and tracked the statistic.

Yet league-wide home runs are down, and way down, too, not in the whole history of the game but at least in the context of the recent Juiced Ball EraTM. Hard-hit rates and power, as measured by home runs or isolated power (ISO), increased steadily and in tandem from 2015 through 2017. You’d expect, then, that if the ball were still juiced in 2018, the league’s highest hard-hit rate ever might produce the highest league ISO ever.

No such luck, though; 2018’s .161 ISO falls a full 10 points short of last year and a tick short of 2016. Which is odd, see, because batters are hitting the ball harder than ever. Since 2015, when sabermetricians first noticed the ball was juiced…

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The Keys to Pitcher BABIP and HR/FB, Perhaps

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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On the Efficacy of Hard Hits to the Opposite Field

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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NL Outfield Power/ISO Buy-Low Candidates

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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