New year, new deserved barrels metric. In October, I took a crack at devising a “deserved barrels” metric in which I took the basic components of a barrel — a hitter’s exit velocity (EV) and launch angle (LA) — and determined the capacity in which the components relate to Statcast’s barrel rate metric (barrels per batted ball event, or “Brls/BBE %” on Baseball Savant). I included squared terms (EV², LA²) assuming the relationship is not linear. (A launch angle that’s too steep is detrimental, for example.)

Further offseason research led me to additional insights:

• Launch angle consistency (“tightness”) is important, correlating with higher BABIPs and more repeatable EVs.
• Launch angle grade in general is important, especially for batted ball quality, not just batted ball frequency, as indicated by batting average on balls in play (BABIP).

There exist many measures of contact quality; barrel rate captures how often a hitter produces high-quality contact. (Hard-hit rate functions similarly but ignores launch angle, to my knowledge, making barrel rate arguably superior.) It only made sense, then, that the latter finding above — that launch angle tightness matters to batted ball quality — should be incorporated into my deserved barrels work somehow.

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Last year, I took a stab at developing what might be loosely defined as park factors using Statcast data. (I called them park “impacts” because they lacked the requisite rigor to be true factors, although it’s all semantics, truly.) I sought to use Statcast’s expected wOBA (xwOBA) metric, specifically on batted ball events (BBEs), such that we would have a measure of xwOBA on contact (or xwOBAcon). This metric accounts for exit velocity (EV), launch angle (LA), and little else — which makes it perfect for this purpose.

The difference between actual and expected wOBA on contact indicates the amount of luck, whether good or bad, a hitter might have incurred on a particular batted ball event. In other words, given ‘X’ exit velocity and ‘Y’ launch angle, what is the most common wOBA outcome, and how much did the actual wOBA outcome differ from it?

The beautiful part about xwOBAcon is it strips away all other context. It removes elements that confound other park factor calculations, such as hitter and pitcher quality or even sequencing (vis-à-vis run-scoring). Except for fielding. Can’t control for fielding, unfortunately.

With this approach, we have the exit velocity. We have the launch angle. We have historical results for that particular combination of EV and LA to use as a benchmark. And then we compare.

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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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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 weeks ago, I devised a deserved barrel rate (where barrel rate is calculated as a percentage of batted ball events, or Barrel/BBE) based exclusively on a hitter’s average exit velocity (EV) and average launch angle (LA). To employ such a simple model, I made a broad but accurate assumption: the average hitter’s average EV (or LA) has a distribution of EVs (or LAs) centered around it, and this distribution does not differ dramatically from other hitters’ distributions.

In layman’s terms, the typical hitter’s average launch angle is his — he owns it, and it reflects his swing plane and mechanics — but he is no better than any other typical hitter in repeating his average launch angle. He, like everyone else, will likely vary from the mean by a certain margin of error. I make the same assumption of exit velocity as well. The two variables bear almost zero correlation to each other. In light of this assumption, the best thing a hitter can do is maximize his exit velocity and hopes it coincides with an optimal launch angle.

(Some folks have suggested I include the percentage of balls hit 95+ mph to refine deserved barrels. The notion intrigues me. However, to illustrate a point: if you have two hitters with identical average EVs, would you expect their distribution of EVs to be dramatically different? Probably not. The inclusion of hard-hit rate accepts as fact that one hitter might be better at hitting 95+ mph more frequently — which would also suggest he hits more softly more frequently as well, and with certainty. This doesn’t stand out to me as a repeatable, let alone necessarily desirable, trait.)

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You know the third base pool is loaded at the top when there are three players at the position who returned more value than Alex Bregman did in 2019 5×5 Roto leagues. Yet, on draft day 2020, owners are likely to at least consider making the Astros’ 25-year-old the first third baseman taken, ahead of Rafael Devers, Anthony Rendon and Nolan Arenado. To this point, in the currently-under-way Pitcher List Experts Mocks, Bregman is the only third base-eligible player to be taken within the first 14 picks in all three drafts.

It’s not hard to see why. This season, he maintained his elite contact and plate discipline skills while tacking on 10 home runs, nine RBIs and 17 runs to his 2018 totals. In 2020, he would appear to be primed for another batting average around .290, and with a spot in the heart of the Astros’ order, he could clear the hurdles of 110-plus RBIs and runs yet again.
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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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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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Manny Machado and Adalberto Mondesi, two shortstops drafted inside the top-50 this spring, have had very different seasons to date.

Since signing with the Padres in the offseason, Machado has struggled (.261/.342/.451) to produce at his usual elite level. A level that made him worthy of a 10-year, $300-million contract. Mondesi meanwhile, has picked up where he left off in 2018, hitting for modest power (30 extra base hits) and batting average (.277), to go along with his league-leading 26 stolen bases.

But some of the underlying peripherals suggest that these two shortstops could see their performance trend in opposite directions during the second half of the season.

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Less than a month ago, the Blue Jays traded their primary designated hitter, Kendrys Morales, to the Oakland Athletics. While somewhat surprising given the money owed to Morales, the Jays willingness to retain salary and Oakland’s need to replace the injured Matt Olson facilitated a trade that was completed less than twenty-four hours before Opening Day in Toronto.

While the trade was initially made with an eye towards roster flexibility, it looks as though one big man may be in the process of replacing another in Toronto. After beginning his major league career in September of 2018 with a world-beating hot streak, Rowdy Tellez is picking up where he left off in some potentially important respects. Read the rest of this entry »