Archive for exit velocity

A summarized version of this post was originally presented as part of PitcherList’s PitchCon online baseball conference for charity to support the ALS Association.

Pitch location, especially pitch height, enables pitchers to augment hitters’ launch angles. This is hugely important for pitchers given that hitters exert outsized influence on exit velocities (EVs), while pitchers exert little influence on EV. As such, EV is more predictive of hitter success than launch angles are. Yet EV remains at the mercy of its launch angle counterpart; a 115-mph blast isn’t half as valuable on the ground as it is in the air. A pitcher can improve his chances of inducing those suboptimal launch angles by weaponizing optimal pitch locations.

There’s a corollary to this for pitchers: capital-S ‘Stuff’ is more predictive of pitcher success, yet it’s pitch location that primarily dictates the outcome of a pitch or plate appearance. Max Bay, now of the Astros’ R&D department, once said Stuff makes a pitcher “resilient” to bad locations–it allows more room for mistakes. But mistakes are still made, and for the majority of pitchers, they are made (or avoided) largely through pitch location.

How sensitive, then, is launch angle to pitch height? If we raise or lower a pitch by an inch or a foot, how much can we expect the resultant launch angle to change? How much can we expect rates of ground balls (GB%), line drives (LD%), fly balls (FB%), and pop-ups (PU%) to change? Read the rest of this entry »

Maximum exit velocity (max EV) measures a player’s hardest-hit ball, typically measured within a single season and compared against other players. Our Mike Podhorzer has documented its leaders and laggards. Rob Arthur, one of baseball’s best public analysts and whom I admire greatly, wrote intelligently on the importance of max EV as a projection-buster back in 2018. Max Freeze (real name) blends extremely hard hits (114+ mph) with launch angle to look for possible power breakouts ahead of 2020.

It has been established (by Al Melchior and me, in fact) that max EV, while an effective indicator, is not the or even a superior indicator of hitter power.

That’s not to say max EV is useless, by any means. It is altogether a different breed of metric than, say, barrel rate (Barrel%, either per plate appearance [PA] or per batted ball event [BBE]) or average exit velocity (EV), both to which fantasy baseball analysts refer much more often. The latter two, and many others, are rate metrics that need large sample sizes to become reliable — or, in common parlance, to “stabilize.” (More on that here, from our former and beloved Eno Sarris.)

Meanwhile, max EV is not a rate or average but a singular data point. It can happen at any moment in time — including the very first batted ball of a hitter’s season. This makes it an intriguing addition to the ol’ tool belt insofar as it could become “reliable” (not necessarily in the statistical sense) much sooner than would barrels or EV. Potentially, we could use max EV loosely as a leading indicator of where a hitter’s barrel rate, average EV, or even weighted on-base average on contact (wOBAcon) might eventually settle.

So: what are the merits of max EV?

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I spend a lot of time bothering Connor Kurcon. He’s a smart dude with a certain intuition about baseball and a certain ability to apply that intuition to produce tangible results that invariably reflect his hypotheses. He devised Predictive Classified Run Average (pCRA), an ERA estimator that outperforms the big three (FIP, xFIP, and SIERA). He also created a dynamic hard-hit rate which, to me, was astoundingly clever and a superior accomplishment to pCRA (although maybe he disagrees).

Anyway, like I said, I bother him a lot, he tolerates me, we bounce ideas off each other. The journey starts there, with my incessant annoyance of him, but also it starts here, with this Tom Tango axiom: exit velocity (EV) is the primary predictive element of hitter performance (as measured by weighted on-base average on contact, aka wOBAcon) — significantly more so than launch angle (LA). Some of the inner machinations of Tango’s mind:

That's not that interesting. What I was more interested is its relationship to NEXT YEAR's wOBA.

Not surprisingly, the performance of the 5% hardest batted balls is what indicates talent in the following year, and by a huge amount. If you've been following my barrels thread.. pic.twitter.com/hvLWr0rCSr

— Tangotiger 🍁 (@tangotiger) February 27, 2020

I won’t speak for Kurcon, but I think this finding helped guide his work on the dynamic hard-hit rate. I also think it inspired his foray into replicating this effort for pitchers or, at the very least, his attempts to determine the most predictive element of pitcher performance. Which leads us to this tweet that (spoiler alert) is actually not stupid at all:

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Yesterday, I finally followed up on a note written on my white board for months: “sd(LA) –> BABIP?” The results from my research: the tightness of a hitter’s launch angle is moderately positively correlated with his batting average on balls in play (BABIP). I measure “tightness” in terms of variance. The narrower the distribution of his launch angles, the tighter. The wider, the looser. There is also weak evidence to suggest a tighter launch angle correlates with more consistent exit velocity (EV).

random finding of the day:

r² = 0.24 between BABIP and stdev(LA)

that is, tighter avg launch angle ➡️ higher BABIP. bat control and all that.

also: tighter launch angle ➡️ more repeatable exit velo to a small extent (r² = 0.14)

— Alex "Oxlade" Chamberlain (@DolphHauldhagen) December 5, 2019

(Turns out Brock Hammit, who is part of the Brewers’ player development team, investigated this very idea in June. Small world! Great minds! All that good stuff.)

As noted in my Tweet, the crux of the finding hints at something previously quantifiable only by the eye test: bat control. In effect, it’s a quantification of the hit tool — to me, the most interesting possible application. Would it surprise you to learn that Joey Votto has the tightest launch angle in the Statcast Era^TM? Followed by hitting savants both current and former, such as Freddie Freeman, Miguel Cabrera, Joe Mauer, Mike Trout, Michael Brantley — and maybe less-expected and arguably underrated names (underrated exclusively in the greater “hit tool” discussion) like Justin Turner, Daniel Murphy, J.D. Martinez, and DJ LeMahieu?

These hitters all have or had outstanding contact skills, superb batted ball efficacy, or both. If you click through to any of their player pages, you’ll encounter routinely elevated BABIPs.

Is there more to this than meets the eye? I’m not sure. Obviously all of this here is but a small part of a much bigger puzzle and should be used in conjunction with, and not in place of, our existing knowledge about player performance. I wouldn’t consider this the be-all, end-all of BABIP analysis by any means, although I do think it’s significant.

That said, here are three potentially pertinent applications of this knowledge:

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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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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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The Giants are not exactly one of the tougher matchups for a pitcher, and especially not for a lefty, as they rank dead last in wOBA against southpaws. Even so, I did not see Jason Vargas tossing a complete-game shutout against them on Wednesday night.

Given the lack of resistance we have come to expect from the Giants’ lineup, that start alone probably would not have made me take notice of Vargas as a fantasy option going forward. Wednesday’s outing in combination with his previous start — a seven-inning, one-run affair on the road against the Dodgers — does give me reason to pause. Looking even further back, Vargas had been effective over a five-start run that was interrupted by an IL stint for a strained hamstring. During that stretch, he posted a 2.74 ERA and a 1.22 WHIP, and though he did not last more than 5.1 innings in any of those starts, he recorded three game scores above 50 and never fell below a 45 game score.
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In the aftermath of last Thursday’s trade that sent J.T. Realmuto from the Marlins to the Phillies, I’m started to look into how much of a hit Jorge Alfaro’s fantasy value would take going from Citizens Bank Park to Marlins Park.

The exercise turned out to be a convoluted mess. Ultimately, it led to a finding that could prove useful in identifying players who are due for spikes or dips in their power numbers.
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Note: As was the case in a previous analysis of Statcast measures and their correlation with power metrics for hitters, I owe a debt of gratitude to Alex Chamberlain. He did a lot of heavy lifting for this column, running the correlations and discussing interpretations with me.

It won’t be the first or last time, but I did a silly thing on Twitter. In announcing a pick for the Pitcher List Experts Mock, I decided to tout the player I chose by citing one of his achievements, as captured by a Statcast metric.

Fun fact: Trevor Williams has the 3rd-lowest avg. exit velocity allowed on flies and liners over the last two seasons (min. 400 BBE), behind only deGrom and Kluber. Just took him in Rd 21 of the #PLExpertsMock. @JustinMasonFWFB, you’re up!

— Al Melchior (@almelchiorBB) December 13, 2018

(Justin, by the way, made his pick very promptly.)
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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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