Archive for ISO

A little more than a year ago, Al Melchior had the brilliant and beautifully straightforward idea of investigating how strongly pretty much ever Statcast metric correlated with various traditional power metrics and compiling them in one post. He asked me to help out, which I was more than glad to do.

Recently, I saw folks talking about this again, and someone asked specifically about the 2019 season. I figured I could refresh the values from the original post quickly enough (certainly a lot more quickly than I did last time), and it would also help bring pertinent information to the fore for folks neck-deep in draft prep.

Spoiler alert: the results barely changed. But! I do feel more confident in this particular set of values, as I nerded out with programming instead of pulling dozens of different queries from the Baseball Savant search function and constantly getting frazzled.

OK, here’s the goods. For 2019 hitters:

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Shortly after the onset of last season, I dug into pitch-level statistics to see how much swinging strike rate (SwStr%), ground ball rate (GB%), and isolated power (ISO) varied by pitch type. I felt inspired after analyzing Madison Bumgarner before the 2018 season and noticed his fastball, once elite, was utterly broken after his dirt bike accident. (See his 2018 player caption and this July post in which I followed up MadBum’s lack of progress.) I felt encouraged by the praise the post received from readers and fellow analysts alike for the clarity it provided. I’d like to think it helped move the needle, even if only slightly, in terms of how we evaluate pitchers.

I wanted to refresh the guts of that post for the 2018 season with additional metrics. There’s not much else to discuss; this’ll be short and sweet. (I’ll toss in some gratuitous high-level analysis following these tables.)

Notes:

• All data is courtesy of PITCHf/x via Baseball Prospectus
• All tables present average rates for starting pitchers only
• Due to pitch tracking/stringing not being perfectly precise, the numbers below are highly accurate but not completely so and may not align exactly with FanGraphs’ batted ball data (for example, Baseball Info Solution strings far fewer line drives than does PITCHf/x)
• Click headers to sort!

Batted ball outcomes by pitch:

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I recently wrote about Statcast measures that were highly correlated with power metrics, such as HR/FB and ISO. The research confirmed that several Statcast measures could be useful tools for identifying undervaled power sources. One factor I ignored in that analysis was pull rate, but in taking a belated look at it, I found that one of the apparently strong relationships gets notably weaker when we control for a hitter’s pull tendencies.

In general, exit velocity on flyballs and line drives turned out to be strongly correlated with ISO for hitters with at least 150 batted ball events in 2018. However, when you isolate the top 10 percent of the sample in terms of pull rate, the relationship is still meaningful, but it’s not quite as strong. That could have implications for how we view two of last season’s top power hitters, Jose Ramirez and Alex Bregman.
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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 Era^TM. 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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As a Twitter dork, I’m exposed to a lot of discussion about swinging strike rates (SwStr%), so much so that it almost feels like it has supplanted xFIP (or other comparable metrics) as a catch-all way to evaluate pitchers. Dude has a 12.5% whiff rate! Sweet. It’s not for naught — swinging strike rate bears a strong correlation to strikeout rate (K%), which comprises substantial portions of the regression equations that underpin the aforementioned xFIP and its counterparts. Swinging strike rate’s correlation to the following metrics (using data from the last five years of 714 pitchers who threw at least 100 innings in a given season):

• K%: r = 0.83
• SIERA*: r = 0.61
• xFIP*: r = 0.55
• FIP: r = 0.50
• ERA: r = 0.40

(*See footnote.)

It also correlates strongly year over year (among 392 player-seasons during the same timeframe in which the pitcher threw 100 innings in the current and subsequent seasons):

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I planned to include Rhys Hoskins in my blind résumés post from Monday, but I couldn’t find any realistic comps for him. Part of the problem is no one does for a full season what Hoskins did for 50 games. Part of the problem, also, is no one does for a full season what Hoskins would be expected to do for a full season, based on his peripherals. It’s a fairly unique skill set (although let’s not conflate “unique” with “the best” or any kind of superlative like that… yet).

Hoskins had himself a real, real nice debut. This isn’t the first time you’ve read about him in the last couple of months and it will be far from the last. Andrew Perpetua, for all intents and purposes, regressed his batted balls from 2017 and he still would’ve had an awesome season. In Eno Sarris’ heart, as well as mine, Hoskins was the runner-up National League Rookie of the Year to Cody Bellinger.

Hoskins had himself a real, real conveniently sized debut as well. His playing exactly 50 games prevents me from arbitrarily choosing a cutoff and having to justify it. A cutoff for what, you ask? Well, Hoskins, in exactly 50 games, posted a .359 isolated power (ISO) while swinging and missing only 7.1% of the time. He struck out a fair deal, but he also walked a ton. Take this snapshot of a season and, as aforementioned, you’ll be hard-pressed to find comps.

Which is exactly why I set out on a very pseudo-scientific quest to find any of Hoskins’ contemporaries who have done this — this, being the aforementioned 50 games of a .350-ish ISO and a 7%-ish swinging strike rate (SwStr%) — at any point in their careers (or within windows of their careers that I’ve curated). I’m winging it here, plucking names from my brain who have elite power and at least above-average plate discipline (assuming Hoskins might, but it’s not a foregone conclusion) and scouring their careers for similar streaks. Any omitted hitters are a product of my lack of memory or imagination, not of malice. Except for Giancarlo Stanton.

Mike Trout

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My favorite part of this year’s World Baseball Classic, aside from the baseball, obviously, was the television broadcasts’ frequent reference to players’ swing speeds. I was floored, even if only because I didn’t know (but should’ve known) we had the technology capable of measuring it. Regarding Major League Baseball and Statcast’s adoption of such a metric, a little birdy told me I shouldn’t hold my breath. Disappointed, I moved on.

Then yesterday, while fooling around in Baseball Savant’s Statcast database trying to diagnose the misalignment of Miguel Cabrera’s outcomes with his peripherals, I noticed the database query’s “sort by” function offered an option to sort by “estimated swing speed.” A quick Google search indicates to me the Statcast and MLB Advanced Media team(s) has (have) yet to formally announce this; sprint speed has been the more exciting recent development, apparently.

Not to me! I quickly got to work querying the data. I also quickly learned downloading the raw data files that underpin the swing speed summaries previously linked do not include swing speed, which is unhelpful. In other words, swing speed is not communicated to us from Baseball Savant’s organs on a play-by-play basis. I imagine this is by design. So, I was resigned to running a single query that summarized swing speed data at a high level: the average swing speed for every hitter with at least 100 at-bats in a given season, from 2015 through 2017.

Here’s what I found.

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Yesterday, FanGraphs made public its splits leaderboard, which the authors have been able to test and refine in private for some time now. It’s an incredible tool. If you haven’t checked it out, you should. If you haven’t thanked Sean Dolinar for building it, you should. (If you have any preliminary feedback, leave it in the comments and I’ll pass it along.)

There are a seemingly infinite number of ways to cross-cut data in endlessly fascinating ways. Splits by handedness, situation by outs, situation by leverage, situation by defensive alignment (shift or no shift!) — the list goes on. But the thing that most interested me immediately was understanding the implications of more granular batted ball data.

Two tools I once refined/created — xBABIP and xISO — rely almost exclusively on Baseball Info Solutions (BIS) batted ball data. Yet they were limited in their capabilities because of the limited nature of the data: we knew each hitter’s contact quality (hard/medium/pull) and contact direction (pull/center/oppo) but now how the trios intersected. But, ah, the splits leaderboard.

The following tables depict the batting average on balls in play (BABIP), isolated power (ISO), and home runs per fly ball (HR/FB) in 2016 by each cross-section. Read the rest of this entry »

Early last season, I introduced an equation that calculates a hitter’s expected isolated power, or xISO. Since I haven’t discussed it all so far this season, and we’re halfway through June, I figured now is better than never.

Before I proceed: Andrew Dominijanni expanded on my research about a month ago. He incorporated exit velocity, sourced from the new Statcast data hosted at Baseball Savant. The model better explains the variance in the data and has slightly better predictive (year-to-year) qualities, making it the optimal choice.

Andrew’s version of xISO generates a pretty simple calculation, but I’m feeling especially lazy today, and I can find hard-hit rate (Hard%), pull rate (Pull%) and fly ball rate (FB%) all on FanGraphs’ batted ball leaderboards.

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I am humbled by the interest in my expected isolated power (xISO) equation since its inception. A fairly simple but helpful tool, I take solace in hoping it maybe has helped one fantasy owner identify a smart buy-low candidate — or reluctantly cut bait on Carlos Gonzalez. I also appreciate the feedback and recommendations for improvement and expansion. I will take care to consider their implementation when I have more available time.

It’s important to remember that Steamer and ZiPS also provide updated and rest-of-season (RoS) projections for most players. Hitter ISOs aren’t listed on the page listing all projections — only slugging (SLG) and batting average (BA) are included, forcing the user to perform some light arithmetic — but ISOs are listed on each hitter’s personal page. And they are not fundamentally different from the expected ISOs I have calculated for you today (spoiler alert). For reference, I crunched the correlation coefficients of every qualified hitter’s current xISO versus his current Steamer and ZiPS ISO projections:

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