Archive for rate

A couple of weekends ago at BaseballHQ‘s First Pitch Arizona conference, The Athletic’s Eno Sarris and I talked about hitter metrics most descriptive and/or predictive of power. In Eno’s presentation, he included a quip from analyst Hareeb al-Saq:

“Knowing barrels on top of average EV [exit velocity] tells you a lot. Knowing average EV on top of barrels tells you a little.”

Eno was surprised by this finding — that barrel rate is a more beneficial metric than average EV, or even EV on a certain type of batted ball event (BBE), such as fly balls and line drives. Incidentally, this is something Al Melchior and I researched last year for which we reached the same conclusion: barrels, whether as a percentage of batted ball events or plate appearances, correlate more strongly than average, maximum, or fly ball/line drive EVs did to common power metrics such as home runs per fly ball (HR/FB), isolated power (ISO), or hard-hit rate (Hard%).

However, it made more sense to Eno when I articulated that calculating barrel rate is simply the act of isolating a hitter’s most-optimal batted ball events. In other words, the inclusion of launch angle (LA) adds another explanatory dimension to EV. In my head, it’s like having two separate circles — one for EV, the other for LA, each containing every individual batted ball outcome from the season — and overlapping them. The overlapped portion of the Venn diagram signifies barrels, and it changes in size depending on the quality of the batted ball events.

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Back in June, I tried to tackle the age-old question: are foul balls good or bad? I tried to determine the “worth” of a foul ball by grouping plate appearances by their number of foul balls (from zero to four-or-more) and looking at two outcome metrics: strikeout rate (K%) and weighted on-base average (wOBA). Unfortunately, my endeavor turned up mostly duds. There are some interesting nuggets – a pitcher’s wOBA allowed improves by nearly 30 points in two-strike counts if he allows at least one foul ball – but most other splits were meaningless. Similar attempts to quantify the effect of a foul ball on the subsequent pitch were similarly fruitless.

I stepped back from the research to let it breathe. Intuitively, I knew there should be value here – I just wasn’t sure how it would present itself. Then, one day (specifically, June 27), inspiration struck in the form of Bryse Wilson’s third career start, during which he incurred nine swinging strikes but also 20 (twenty!) foul balls on 56 four-seam fastballs, amounting to a 16% swinging strike rate but also an absurd 36% foul ball rate (Foul%). The coincidence of many whiffs and also many fouls struck me as fascinating and extremely relevant to my previous research. It encouraged me to reframe the question at hand:

How does foul ball rate correlate with other measurements of success by pitch type?

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This is, and is not, a Mike Tauchman post. My relentlessly Tauchman-centric brand has been, in the words of beloved pal Sammy Reid, “hotter than the sun’s ass.” Tauchman has become the folk hero Yankees fans didn’t know they needed. I also have become insufferable to everyone within digital arm’s length of my Twitter account.

When I reviewed my bold predictions in July, I lamented Tauchman’s bad-luck strikeout rate (K%). By measure of “deserved” strikeout rate (I regressed the components of every hitter’s plate discipline against their strikeout rates to derive a “deserved” rate), Tauchman had been one of Major League Baseball’s unluckiest hitters.

Despite his recent torrid streak, Tauchman still emerges as one of 2019’s unluckiest hitters. That is why this is, in a sense, still a Tauchman post. But it’s also an Everyone Else post, in that I’m eager to unearth baseball’s luckiest and unluckiest hitters this year.

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At the beginning of last season, I contextualized the swinging strike rate (SwStr%) (and refreshed those numbers after the season concluded). I had seen other analysts call certain pitches “above-average,” “below-average,” “elite,” etc. using the league-average whiff rate as a baseline. This is neither a criticism nor a judgment, as I absolutely did this before I had my statistically-driven epiphany. But understanding the average four-seamer’s or slider’s or cutter’s whiff rate lends additional context to any assertion one might make about the “elite-ness” of a pitch.

More recently, I wanted to convert discrete outcomes by pitch type into fielding independent pitching (FIP) statistics — namely, FIP and xFIP (expected FIP, which substitutes a pitcher’s rate of home runs per fly ball for the league-average rate). Let me warn you now: the results are very imperfect. It took some brute force on my part to get there, but I got there. I would wager that the the extreme (lowest and highest) values are probably a bit exaggerated. Regardless, it’s an interesting table to ingest:

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This year, I’ve been caught up on pitching. I investigated the nuance inherent to swinging strikes, indirectly made a case for completely abandoning the sinker with this piece comparing pitch type outcomes, and (maybe) identified the keys to unlocking pitcher BABIP and HR/FB.

Here, I’ve modeled swinging strike and ground ball rates using only pitch velocity movement. Surely, this work can be improved; my quantitative tool set, while fairly robust compared to the layman, is meager compared to the professional or even hobbyist statistician. Regardless, I think it’s pretty cool, and I hope it adds to the conversation constructively.

Mostly, this serves to satiate my own curiosity. Unfortunately, it may be denser than I expected — few answers are ever quite as simple as you hope them to be, I guess.

Existing Research

I linked to several of my own pieces above. Dan Lependorf wrote about estimating ground ball rates in 2013 at the Hardball Times, although its conclusions have an anecdotal slant. (It thinks about velocity and movement but doesn’t take the requisite steps to bridge the logic.)
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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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The impetus for this post arises from a Tweet by our very own Al Melchior:

Holy BABIP, Willie Calhoun pops out a lot! pic.twitter.com/BDyHtfJsZJ

— Al Melchior (@almelchiorBB) January 3, 2018

This is, in no way, meant to shame Melchior; if anything, he has afforded us a valuable learning opportunity, especially because it became clear to me there likely exists a large swath of FanGraphs users who routinely misinterpret the relatively new Minor League batted ball data. (Through no fault of their own, by the way. The new data didn’t come with a user’s guide or anything. We have been left to our own devices, and it’s easy to assume such clean data comes without warts.)

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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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