Archive for launch angle

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 »

Ominous title, I know, but in all fairness: Jose Altuve sports a paltry .207/.267/.322 (65 wRC+) line. The former consensus 2nd-overall pick who hit .298 with a career-high 31 home runs last year may seem like an unlikely collapse candidate on the surface.

Unfortunately, the cracks began to show last year. For one, Altuve all but stopped running; when he did run, he fared poorly, succeeding in only six of 11 attempts. Moreover, his .298 average, while excellent, was a far cry from his best (.346) and post-breakout five-year peak from 2014 through 2018 (.331). These are the obvious signs of wear.

A lightly critical evaluation might have concluded Altuve would still be a valuable commodity in 2020. Average draft position (ADP) data confirms this suspicion; a post-pandemic-onset ADP of 40.12 (37th overall), per the National Fantasy Baseball Championship (NFBC), ain’t nothing to sneeze at.

Yet my work on launch angle tightness in December, while illuminating and fun to research, shone a spotlight on an interesting and very specific data point: Altuve.

A tight launch angle (small standard deviation) is not always good, and a loose launch angle (large) is not always bad, but by and large the overall trend holds. Perhaps a more effective way to use tightness is to compare it historically for each player. While Altuve never had elite tightness, it was consistent, and he was an elite hitter, and that’s all that mattered. So it alarmed me to see his launch angle loosen up in 2019:

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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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A little more than a year ago, Alex Chamberlain and I looked into what type of impact a slew of Statcast measures had on a pitcher’s overall BABIP rate. Hard-hit rate and exit velocity on ground balls (EV GB) had the strongest correlations, but it seemed unlikely that the latter would have much to say about which pitchers would be best at limiting hits on flyballs in play. In general, it seems that BABIP could be influenced by different factors depending on the type of batted ball.

So let’s test that out. This column is the first in a series of four where I will be looking at the impact of various measures on flyball BABIP and ground ball Avg, both for pitchers and hitters. I’m kicking this off with an analysis of flyball BABIP for pitchers.
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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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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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This is a long one. I appreciate your patience in advance.

Mike Podhorzer, I and sporadic others have — but primarily Mike has — carried the torch on developing ‘expected’ metrics, such as xBABIP (expected batting average on balls in play), xHR/FB (expected home run-to-fly ball ratio) and xK% (expected strikeout rate), all and the rest of which can be found here. For the uninitiated, these xMetrics help describe how a hitter or pitcher should have performed based on various measurements of the events that unfolded and typically are more predictive of future performance than the original metric. They’re not perfect, but, like other advanced metrics, they give us a better understanding of player performance and ability.

Each metric — xHR/FB, xK%, etc. — has formulas for both hitters and pitchers, with the hitter metrics typically having stronger correlations than those for pitchers. Unfortunately, pitcher xBABIP has always eluded us. It’s inappropriate to repurpose hitter xBABIP for pitchers, but it’s because the model coefficients (weights) would be different, not because the theory underpinning the model is flawed.

That’s the problem, though: hard hits, line drives, infield fly balls — these all should affect a pitcher’s BABIP allowed. Our intuition begs it to be true. Yet there’s a resounding lack of evidence that suggest otherwise. The correlation between BABIP and hard-hit rate (Hard%), line drive rate (LD%) and infield fly ball rate (IFFB%), among others, borders on nonexistent:

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There aren’t many players who have had as interesting, or perhaps as volatile, a career arc as Mike Moustakas has. His last few seasons have featured him being demoted to the minor leagues, finally experiencing a breakout, tearing his ACL, and, most recently, the fact that he’s set to be featured in this year’s Home Run Derby. Having already set a new career high in home runs, and currently posting the highest isolated power of his career, it probably isn’t a surprise that we’ll see him participate in the event in Miami.

Even less surprising, though, may be the influx in power that we’ve seen from Moustakas. A player who gradually improved his ability to make contact, with rising Contact% figures that peaked across his 113 plate appearances in 2016, at 86.2, while also focusing more on taking the ball to the opposite field (30.8 Oppo% last year), resulted in him reestablishing value after it looked like he was a lost cause at the hot corner in Kansas City. This year, we’re continuing to see Mike Moustakas evolve, but in a completely different way.

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