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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The past three years, our Justin Mason has organized too-early mock (#2EarlyMock) drafts ahead of the next fantasy baseball season. The 15-team snake drafts have taken place each September, which means I’m recounting this about two months too late. However, with some early offseason developments and the release of Steamer’s 2020 projections, the wait at least offers the benefits of both hindsight and foresight.

There’s no such thing as average draft position (ADP) data in September, so we rolled into these drafts blind to everything but our own recency biases. The dynamic is compelling, if frequently odd, and can be difficult, frustrating, but ultimately enthralling to endure. Ideally, my commentary here will not painfully boring and might provide insight into my “process” on a microcosmic level.

My draft was not without fault, but I do feel good about it. I’d like to think that means something, as someone highly critical of his drafts and rarely feels truly “good” about a roster I’ve compiled. I don’t play in many deep leagues, so 15-team drafts routinely jack me up. Somehow, I feel like not having ADP information actually benefited me; I feel like I scripted my draft more cogently than usual. But also, it’s fairly clear where I made suboptimal decisions. Overall, I don’t think it turned out half-bad, especially for a 15-teamer.

The results of my draft follow, and the minimum (“Min”) and maximum (“Max”) pick information comes from Smada’s ADP information compiled from all six #2EarlyMock drafts.

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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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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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Last offseason, over brunch at some restaurant in Phoenix, Brad Johnson and I, in coordination with FanGraphs’ Powers That Be, revived a recently deceased series about prospects. We had to attribute to it a new name, but its purpose remained steadfast: to identify intriguing but unheralded Minor League talent. This is the corner of fantasy baseball in which Brad and I thrive. I’m not one for series — I never thought I had any good ideas — but I had always wanted to do something like this, but for fantasy purposes.

Alas, Peripheral Prospects was born, a phoenix from the ashes. (A good metaphor, this, because in Phoenix we ate at a restaurant with ashtrays.) We published every Monday steadily for months until the grind of the season wore us down. Just because Cody Bellinger tailed off the in second half doesn’t mean we remember his 2019 season as a bust. I’d like to think our weak finish sullies not our fine season. Several WARs, at least.

Here, I’d like to highlight some of my favorite peripheral prospects of 2019. (Brad will separately highlight his own, for I can ascertain why he picked the players he picked but it is better that he articulate his rationale on his own terms.) Let’s not waste any more time. Here are my 10 favorite peripheral prospects, sorted ascending according to their appearance in the series.

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If only my fantasy teams performed half as well as my bold predictions this year.

Not that I had an entirely awful season in 2019. I entered my first two National Fantasy Baseball Championship (NFBC) Online Championship leagues (12 teams, 30 rounds, standard 5-by-5 rotisserie categories) and won one of them. (I came last in the other.) I won a second league (a 12-team auction) and had good but futile runs in several others. Still, 2019 felt like a bit of a letdown.

So, again: at least I have my bold predictions to fall back on. Over the years, I find I’ve become increasingly adept at late-round draft strategy (while becoming increasingly inept at early-round strategy, or something like that). My bold predictions are honest assessments of guys I love. Moreover, I intend for them to be actionable; that is, Ronald Acuña Jr. goes 40/40 would have been an extremely impressive prediction, but he was already a 1st rounder. How much does that move the needle?

As in past years, I have forgotten half my predictions, so I’ll be just as curious to find out what they were as I will be to find out if they’ve succeeded. Let’s dig in.

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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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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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