Author Archive

Which Statcast Measures Correlate Best? 2019 Refresh

A little more than a year ago, Al Melchior had the brilliant and beautifully straightforward idea of investigating how strongly pretty much ever Statcast metric correlated with various traditional power metrics and compiling them in one post. He asked me to help out, which I was more than glad to do.

Recently, I saw folks talking about this again, and someone asked specifically about the 2019 season. I figured I could refresh the values from the original post quickly enough (certainly a lot more quickly than I did last time), and it would also help bring pertinent information to the fore for folks neck-deep in draft prep.

Spoiler alert: the results barely changed. But! I do feel more confident in this particular set of values, as I nerded out with programming instead of pulling dozens of different queries from the Baseball Savant search function and constantly getting frazzled.

OK, here’s the goods. For 2019 hitters:

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Quantifying the Benefit of Spray Angle to xwOBA

Expected weighted on-base average (xwOBA) is one of Statcast’s most important additions to the Sabermetric sphere. It’s a simple premise — estimate a hitter’s deserved production based, simply, on his combinations of exit velocity (EV) and launch angle (LA) — with robust implications and applications. It’s remarkable how powerful the metric is with just two inputs.

However, the metric is not without its faults (or complaints from those who use it). Its simplicity is beautiful but inherently and knowingly lacking, accounting minimally or not at all for:

  1. spray (lateral) angle (touched upon here),
  2. a player’s foot speed (discussed more thoroughly here),
  3. park factors, and
  4. opposing defense.

None of this necessarily serves as an indictment of xwOBA. The number of inputs you include affects the purpose you want it to serve. That is, do you want it to be descriptive or predictive? How about both? Maybe defense shouldn’t be included, then, if we can’t reasonably expect a hitter to face the same caliber of defense each year, something that is out of his control.

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Upgrading a Deserved Barrel%

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:

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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Converting ADP to Auction Values

Oftentimes, I write out of inspiration. This time, I write simply to write, because the subject happened to creep up into my thick ol’ skull without provocation, which I guess is a type of inspiration in and of itself but not wholly what I had in mind. No one specifically needs this post right now, or maybe everyone does. I don’t know.

Something I do see and have seen before, however, with frequency, are mentions of such-and-such player rising or falling in the ranks, usually by virtue of average draft position (ADP). ADP is a measure of a player’s rank by aggregating data for a whole boatload of snake drafts. It’s a good way of assessing a player’s market value.

The problem with ADP is, unless you have completed research nearly identical to this, you can’t possibly be expected to know how a player’s ADP rank might equate to a dollar value at auction. Having this knowledge, this intuition, is arguably helpful in understanding how much you’re staking on any particular player. Moreover, changes in ADP become easier to digest. Possibly. For me, it does. If you’ve never participated in an auction draft before, maybe it doesn’t.

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2019 Statcast Park Factors (and the Importance of Spray Angle)

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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Let’s Talk About Launch Angle Generally

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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Let’s Talk About Launch Angle “Tightness”

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).

(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 EraTM? 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?

Tightest Launch Angles – Statcast EraTM
Hitter Name BBE stdev(LA) EV
Joey Votto 2,148 21.8 88.5
Nick Castellanos 2,132 22.0 88.7
Freddie Freeman 2,054 22.4 89.8
Miguel Cabrera 1,692 22.6 92.1
Joe Mauer 1,738 22.7 89.6
Brandon Belt 1,723 23.0 87.4
Matt Carpenter 1,881 23.0 88.7
J.D. Martinez 1,938 23.2 91.3
Justin Turner 1,894 23.2 89.5
DJ LeMahieu 2,452 23.6 90.2
Mike Trout 1,860 23.6 90.5
Michael Brantley 1,839 23.7 88.7
Eugenio Suarez 1,872 24.1 87.9
Matt Kemp 1,672 24.3 88.4
Daniel Murphy 2,068 24.4 88.7
stdev(LA) = Standard deviation of launch angle
Top 15 of 120 hitters with 1,600 batted ball events (BBEs) since the beginning of 2015.

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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2020 Too-Late #2EarlyMock Draft Review

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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2019 Deserved Barrel% – Full List

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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Devising a Deserved Barrel%

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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