Archive for xwobacon

Here is a disputed fact: MLB changed the ball. League brass, on the record, wanted to make the ball livelier but also raise the height of the seams, which would increase drag. The two changes — more bounce, but also more air resistance — would, more or less, offset each other.

The fact is disputed because some of the game’s most intelligent minds — namely, renowned baseball physicists, the very people most capable of determining if the ball is, indeed, different — doubt the ball has changed. It’s imperative I tell you this because they may be right, which would make me (and MLB, for the umpteenth time), well, wrong. Everything that follows assumes the ball has changed. Maybe this meshes with what you’ve witnessed, maybe it doesn’t. This is simply one stupid man’s interpretation of the data available to us thus far.

Early returns suggest MLB accomplished what it set out to accomplish. We can use weighted on-base average on contact (wOBAcon) to describe hitter production on balls in play, aka batted ball events (BBE). The average hitter is slightly less productive in 2021 than in past years, but not egregiously so, as shown below. Also, it’s only April; as the weather warms, so should be the bats. It’s reasonable to expect 2021’s league-wide wOBAcon value to climb a few ticks before year’s end.

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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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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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Using a format similar to the one Paul Sporer recently posted for 2020 Roto player rankings, below is the 2020 ranking of the Top 75 Outfielders for Ottoneu fantasy baseball.  Ottoneu leagues are auction style, but with no salaries listed (league dependent), think of these lists as simplified “snake draft” rankings (“which player would I take before the next”), or a value ranking of players above replacement level for 2020. Players with multi-position eligibility may receive a slight bump in value (2020 positions listed).  You can reference average Ottoneu player salaries here, but keep in mind these salaries fluctuate throughout the winter as rosters shape up towards the January 31st keeper deadline for all leagues.

Previous 2020 Ottoneu rankings:

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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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In 2018, and again earlier this year, I reviewed how different pitch types perform by various measures including swinging strike rate (SwStr%), ground ball rate (GB%), and isolated power (ISO). In the last couple of years I have tried to emphasize heavily the importance of evaluating a pitcher on his component parts — namely, each of his unique pitches, all of which behave differently and can bring resolution to some of pitching’s more enigmatic questions and issues.

If you clicked through those links in the first sentence, you saw how breaking balls and offspeed pitches outperform fastballs by virtually every metric. With the advent of Statcast, we can not only validate my prior work, which relied on PITCHf/x data, but also dig more deeply into how each pitch type behaves according to newfangled Statcast data — namely, how each pitch performs exclusively on balls in play.

This is something I pursued preliminarily using the PITCHf/x data, by measure of ISO, but it doesn’t fully capture total production or damage allowed. Having written about Zack Wheeler the other day and in discussing how the performance of his pitches have ebbed and flowed from 2018 to 2019, I was curious to dig into pitch-specific expected weighted on-base average (xwOBA) on contact (xwOBAcon).

Here’s how every pitch type compares by xwOBA allowed. Keep in mind, xwOBA captures “deserved” total value through not only balls in play but also strikeouts and walks. Year in and year out, fastballs fare worse than the league average, whereas breaking balls and offspeed pitches perform better than average, all to varying degrees.

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