Archive for contact%

This month last year, Connor Kurcon of Six Man Rotation set out to quantify the location aspect of command (or “LRP”). By establishing an accounting system that credited and debited pitchers for changes in ball-strike counts based on the attack zone of and hitter’s disposition (take? swing? ball in play?) for every pitch, he effectively created an alternative to Pitch Value (PVal) that rewards optimal movement through ball-strike counts but with much more pitcher and hitter context.

His findings are as you’d expect: Jacob deGrom and Justin Verlander lead the pack, with Gerrit Cole, Max Scherzer, and Clayton Kershaw not far behind. Other budding aces like Jack Flaherty and Mike Clevinger pepper the list, and some pleasant surprises (such as Brendan McKay, Caleb Smith, and, for those still thirsting, Jake Odorizzi) are scattered throughout as well. Out of the bullpen, newly anointed relief ace Nick Anderson led the pack followed by the underrated Emilio Pagán, breakout reliever Giovanny Gallegos, and others.

Near the end of his post, Kurcon includes a subhead dedicated to Kyle Hendricks where he highlights how Hendricks, widely respected as a command artist, fares lukewarmly by measure of LRP. He then reminds us “LRP doesn’t paint the full picture of command.” True that.

Fortunately, Kurcon has left the door open for me to tie up loose ends with find Gs I’ve been meaning to write up for a couple of months now. Never fear, Hendricks is the command artist we know and love — it’s just that he relies heavily on incurring contact in optimal pitch locations. It is a needle very few pitchers can thread, but Hendricks does it masterfully.

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Edit: Further investigation has brought to my attention that the results presented below are slightly askew, although not incorrect. All discussion below regarding hit frequency (BABIP) and contact quality (expected wOBA on contact, or xwOBAcon) should have been framed specifically in the context of non-home run batted ball events. This is significant, because home runs are a big deal, but it’s also insignificant. Allow me to explain.

When we re-include home runs, the relationship between launch angle tightness (stdev[LA]) and contact quality weakens dramatically. I think it comes down to the graph shown in the middle of the post below. Removing home runs narrows the range of productive launch angles, thus making a tighter range of launch angles (confined primarily to line drives) more appealing. When you include home runs, it expands the range of productive launch angles to include productive fly balls in addition to productive line drives. There’s literally more margin for error when we reconsider home runs, making a tighter range of launch angles was valuable.

That doesn’t mean launch angle tightness isn’t important! If anything, removing home runs was a nifty way to demonstrate this fact.

Anyway, I have updated this post with red text to clarify that references to contact quality exclude home runs — and that the findings from this post are technically correct, just through a certain lens.

* * *

Last week, I published some work regarding launch angle “tightness,” aka a hitter’s ability to replicate his average angle as closely as possible as often as possible. Effectively a measure of consistency, I found launch angle tightness (consistency, variance, whatever you want to call it) bore a moderately strong relationship with batting average on balls in play (BABIP).

Truth be told, I began to question my finding almost immediately for reasons I’ll discuss shortly. After inquiries from The Athletic’s Eno Sarris, FantasyPros/PitcherList’s Nick Gerli, and even Cody Asche (this is the mildest of brags) that echoed my internal self-doubting dialogue, I dove into the question further.

Ultimately, the best explanation for the importance of launch angle consistency is to simply elaborate upon launch angle generally. So, consider this a de facto primer on launch angle. It’s probably not the first and certainly won’t (or shouldn’t) be the last. But in the context of my post from last week, it simply makes sense to bring the conversation full circle and wrap it up nicely with a bow. And the final result is gratifying, I hope.

Enjoy (or not, I’m not your dad):

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If there were ever a baseball question that keeps me up it night, it’s this: how do the physical properties of pitches affect batted ball outcomes? Many researchers have tackled the subject with varying degrees of success and elucidation. My attempts have focused primarily on a pitch’s ability to generate swinging strikes and ground balls, the first of which used pitcher-level PITCHf/x data while the more recent of which used individual pitch-level Statcast data.

While modeling whiffs and grounders is interesting (and important, too), something strikes me as much more compelling and confounding: the relationship, if any, between a pitch’s physical properties and its batted ball outcomes, whether described as exit velocity, launch angle, or total base-run value allowed, as measured by weighted on-base average (wOBA) or even expected wOBA (xwOBA).

The ability to prove “contact management” as a legitimate and shared pitcher skill has long eluded the Sabermetric community. Assumptions of a league-average batting average on balls in play (BABIP) and, for xFIP, home runs per fly ball (HR/FB) pervade the common ERA estimators (FIP, xFIP, SIERA) we use to gauge talent and assign value. Those assumptions regarding BABIP and HR/FB imply a pitcher’s inability to control them — and there isn’t much evidence to suggest otherwise.

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In 2018, and again earlier this year, I reviewed how different pitch types perform by various measures including swinging strike rate (SwStr%), ground ball rate (GB%), and isolated power (ISO). In the last couple of years I have tried to emphasize heavily the importance of evaluating a pitcher on his component parts — namely, each of his unique pitches, all of which behave differently and can bring resolution to some of pitching’s more enigmatic questions and issues.

If you clicked through those links in the first sentence, you saw how breaking balls and offspeed pitches outperform fastballs by virtually every metric. With the advent of Statcast, we can not only validate my prior work, which relied on PITCHf/x data, but also dig more deeply into how each pitch type behaves according to newfangled Statcast data — namely, how each pitch performs exclusively on balls in play.

This is something I pursued preliminarily using the PITCHf/x data, by measure of ISO, but it doesn’t fully capture total production or damage allowed. Having written about Zack Wheeler the other day and in discussing how the performance of his pitches have ebbed and flowed from 2018 to 2019, I was curious to dig into pitch-specific expected weighted on-base average (xwOBA) on contact (xwOBAcon).

Here’s how every pitch type compares by xwOBA allowed. Keep in mind, xwOBA captures “deserved” total value through not only balls in play but also strikeouts and walks. Year in and year out, fastballs fare worse than the league average, whereas breaking balls and offspeed pitches perform better than average, all to varying degrees.

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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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I’m always reluctant to discuss a player whom we have recently featured at FanGraphs. Indeed, Rylan Edwards noted that, unlike swimming, Keon Broxton is not boring. Jeff Sullivan also recently covered Keon Broxton, ushering everyone on board his respective bandwagon (Broxton’s, not Sullivan’s). It’s a good feature, and its biggest takeaway is the following: Keon Broxton is hitting the ball pretty damn hard.

Broxton has slipped a bit — he no longer holds the top spot, ceding it to Nelson Cruz, Giancarlo Stanton, and some kid named Gary Sanchez. Stanton hasn’t played in two-plus weeks, so it stands to reason that Broxton’s exit velocity has slipped in the last week. That’s fine. As is, it’s still elite.

Except, woah, the strikeouts. Right? That’s alarming. It’s not so alarming that it’s a dealbreaker. Sullivan even brought up the idea of Broxton being a center-fielding Chris Carter. For a team like the Milwaukee Brewers, that would work just fine.

It’s the composition of the strikeouts — in other words, the way Broxton gets to those strikeouts — that kind of blows my mind.

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If you follow me on Twitter, you know how this ends. Statistically speaking, though, you probably don’t follow on me Twitter, so you probably don’t know how this ends. Then again, maybe you really do know how this ends, because when you clicked this link, you probably had to read the title first. Or maybe you didn’t! Honestly, I don’t want to pigeonhole you. Maybe you’re the kind of person who clicks links all willy nilly with zero regard for content. I’m sure SEO folks love you but also lose their minds trying to understand you.

No matter. Let’s pretend you didn’t read the title. Now you’re presented with blind résumés. Can you guess who Players A and B are?

Did you have to cheat? It may actually be more difficult than you thought. You know the names already, but perhaps you got them out of order: Kyle Hendricks is Player A and Tanner Roark is Player B. But look at that! Hendricks and Roark are almost perfectly identical within every metric. Roark even edges Hendricks in xFIP, innings per start, and WAR per start. It’s kind of a big deal, given Hendricks is owned in more Yahoo! leagues than Roark (85% to 78%).

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This kind of post is right in Mike Podhorzer’s wheelhouse. We have a lot of common interests as far as baseball research topics are concerned — namely, xK%, xBB% and xBABIP — but he’s typically the one who periodically updates RotoGraphs with x-leaders and x-laggards.

So, again, this would be the kind of post Pod would tackle: an update on which starting pitchers will likely regress in their strikeout rates (xK%). But instead of using the xK% equation, to which the above paragraph is hyperlinked, I want to focus on a particular metric: zone contact rate, or Z-Contact%.

I’ll be up front about this: I haven’t done much research regarding pitcher zone contact rates and how it sticks from year to year. That’s primarily what this post will entail, and my evidence is largely anecdotal. But it’s important to note that zone contact rate plays a profound role in determining a pitcher’s strikeout rate; the Pearson correlation coefficient between K% and Z-Contact% is -0.72. In other words, K% and Z-Contact% are strongly negatively correlated.

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A couple of weeks ago, I investigated Justin Verlander’s resurgence. I found reasons to validate his hot streak but turned up additional question marks along the way.

One of them was his zone contact rate (Z-Contact%). At 79.7 percent, it would have been the second-lowest of his career by several percentage points (despite not performing “at peak”). However, I realize now, unfortunately, that I must have encountered a glitch in the leaderboards — his Z-Contact% as of August 21 (because the post, despite running the same day as his Aug. 26 start, was published prior to it) was 85.7 percent.

Regardless, it got me thinking what affects a pitcher’s zone contact rate because it correlates very strongly with strikeout rate (R-squared = .594). User DoubleJ speculated about the metric via comment on one of last week’s posts:

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