Archive for xwOBA

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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Trevor Bauer, the National Fantasy Baseball Championship’s No.-9 starting pitcher and No.-31 player overall, has pitched to the tune of a 4.12 ERA this year. All things considered (“things” being, primarily, the juiced ball), Bauer hasn’t been awful. But after compiling a pristine 2.21 ERA in last year’s breakout with equally pristine ERA estimators to boot (3.14 xFIP, 3.21 SIERA), this year’s peripherals (4.35 xFIP, 4.21 SIERA) are far less inspiring, even when adjusted for context.

The easiest way to write off Bauer’s 2018 season as an aberration is, well, to look at everything else he has ever done. He sports a career 3.97 ERA, with just one season (2018) with an ERA under 4.00. The blind squirrel who took an approach as simplistic as this in 2019 would have invariably found a nut.

Such an approach, however, would grossly undersell Bauer’s gains in 2018, which were quite legitimate. Using the most basic of peripherals, Bauer’s swinging strike rate (SwStr%) took the best 3rd-biggest step forward in nominal terms, behind only Patrick Corbin (and his slider) and Gerrit Cole (and his fastball).

Yet 2018 gains do not necessarily beget sustained excellence. Bauer’s narrative is a fairly complex one, so let’s give it proper attention.

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You may or may not have heard that Yu Darvish is back. By any conventional measure, his latest back-to-back starts of six shutout innings, two hits, and seven-plus strikeouts rank among his best in a long time. By measure of “Game Score v2,” which FanGraphs includes in a pitcher’s Game Log, Darvish’s scores of 78 and 79 are his two best starts since 2017. Because some of his better Game Score starts went more than six innings: these are his two best six-inning starts, period. They happened very recently, consecutively, and he didn’t labor through them, either, throwing just 94 and 83 pitches, respectively.

The last two starts were a gift to those who took a leap of faith. Darvish, who walked at least three batters in seven of his first eight starts (33 walks in 36ish innings!) and 10 of his first 13 (44 walks in 66ish innings!), was an absolute mess. He had compiled a 4.88 ERA, 5.19 FIP, 4.49 xFIP, and 4.96 SIERA in 13 starts, the cherry on top being a walk rate (BB/9) of six. Six! Six batters per nine innings. As my 14-year-old self quoting Ron Burgundy might say: “I’m not even mad — just impressed.”

However, from June 10 to July 3 — a five-start window sandwiched between his early-season futility and his recent wizardry — Darvish struck out 33 and walked just five in roughly 31 innings, compiling a 3.68 xFIP and 3.64 SIERA. Read the rest of this entry »

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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Zack Wheeler, prior to about two weeks ago, was an enigmatic starting pitcher on whom many fantasy owners had started to lose faith. (Such faith might again be lost after Sunday’s start, which is another issue all together.) After missing two seasons recovering from Tommy John surgery and related setbacks, Wheeler returned to the mound in 2017 only to be sidelined once more and miss almost the entire second half of that season. The prospects of him succeeding in 2018 were, frankly, not great.

Wheeler, however, came out firing, his four-seamer and two-seamer averaging 95.9 and 96.1 mph, respectively, up from 94.8 and 94.3 mph — a moral victory in its own right. The added velocity helped both pitches play up in way completely unseen the year prior, as measured by expected weighted on-base average (xwOBA) allowed:

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The longer we have Statcast data at our disposal, the more ways we find novel uses for them. What follows is my proposal to use the difference between expected and actual value segmented by batted ball type and venue to determine park factors (and potentially evaluate defensive value, as described in the footnote). Unfortunately, someone smarter than me was already way ahead of me. I’ll get to that in a second.

A typical park factors grid, such as those produced by ESPN or FanGraphs, commonly relies on outcomes — outcomes of plate appearances (ESPN), batted ball categories, or both (FanGraphs). They describe what actually occurred, the way wOBA describes a hitter’s actual production. Conversely, expected wOBA (xwOBA) describes what should have occurred based on a batted ball’s exit velocity (EV) and launch angle (LA). It strips away everything else, holding constant all other environmental factors in order to deliver an otherwise-context-neutral EV/LA-based value.

The difference between wOBA and xwOBA (“wOBA minus xwOBA,” or wOBA—xwOBA for short), therefore, effectively captures all value amassed or lost by other variables. In other words, if wOBA explains what actually happened in a non-neutral environment, and xwOBA explains what should’ve happened in a neutral environment, then the difference between them characterizes the effect of the environment — the ballpark itself.

Unfortunately for me (but fortunately for everyone else), Tony Blengino already did this (which is why he’s a former MLB executive and I’m not). In 2017, he used Statcast data to calculate park factors on the basis of expected outcomes relative to actual league-average production. For all intents and purposes, it’s the same idea.

Consider this post a refresher on the topic.

Let me call your attention back to a simpler time. If you search “Miguel Cabrera xwOBA” on Twitter, you’ll find, well, not a multitude, but at least a sampling, of Tweets from the summer of 2017 lamenting Cabrera’s (and his teammate’s) bad luck by measure of wOBA—xwOBA:

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Fantasy analysts and enthusiasts alike are still searching for ways to use Statcast’s expected wOBA (xwOBA) metric meaningfully to gain an edge. Unfortunately, beyond leveraging the difference between xwOBA and actual wOBA (what I, and probably countless others, refer to as the “wOBA minus xwOBA differential”), I don’t know yet how else you can use xwOBA effectively. Given already-widespread use of the metric, the minimal edge you can glean will come from interpretation.

I discussed the interpretation of xwOBA multiple times in 2018. In May, I highlighted hitters on whom to buy low because of their extreme/outlier wOBA–xwOBA differentials. In July, I called out xwOBA’s inability to account for what appeared to be the ball becoming un-juiced, thereby overestimating xwOBA across the league. In September, I investigated the predictiveness of xwOBA in-season (that is, the predictiveness of first-half xwOBA on second-half wOBA).

I discussed all of these topics during my presentation at BaseballHQ’s annual First Pitch Arizona forum, especially the former-most. Basically all of the hitters I tabbed as buy-lows outgained their prior performance by substantial margins — all of them, that is, except for Victor Martinez. Could he be considered a miss? Sure, except he was different from the rest of his fellow underachievers: he perennially underperforms his xwOBA. Perhaps the better question, then, is: Why was he a miss?

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I use xwOBA as a leading indicator of good or bad things to come mid-season, for better or for worse. It’d be good to know if such reliance is truly warranted. I further talked myself into the idea when I wrote about several underperforming hitters in early June. Many of the names therein went on some serious heaters afterward, too. It wasn’t as prescient as it was playing the odds: the hitters underperforming xwOBA most extremely through two months always, always (in the Statcast Era^TM) bounce back to some degree.

It’s “predictive,” but not universally so, and only by virtue of common sense, in the same way a pitcher who allows a sub-.200 batting average on balls in play (BABIP) through two months could not reasonably sustain this high level of contact management. (There’s a discussion to be had here about the gambler’s fallacy, but I don’t think it necessarily applies to baseball. For another day.)

In terms of prior work, it’s all Baseball Prospectus‘ Jonathan Judge (only a slight exaggeration): he compared xwOBA to BP’s DRA metric as well as FIP (fielding independent pitching), a much simpler ERA estimator, and showed xwOBA is hardly superior to the field, at least for pitching. However, the article only covered year-to-year, not in-season, correlations.

After our dear and departed (but not dead) Eno Sarris asked Judge if he had looked at in-season correlations specifically, and after our dear and departed (and also not dead) Mike Petriello reinforced the notion that xwOBA could serve as an in-season predictor of regression under certain circumstances, I figured it’s high time I just tackle the question.

So: How predictive is xwOBA of wOBA in-season? For hitters and for pitchers?

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The league-wide hard-hit rate (Hard%) is up. Like, way up, at its highest level by far in the 17 years Baseball Info Solutions has measured and tracked the statistic.

Yet league-wide home runs are down, and way down, too, not in the whole history of the game but at least in the context of the recent Juiced Ball Era^TM. Hard-hit rates and power, as measured by home runs or isolated power (ISO), increased steadily and in tandem from 2015 through 2017. You’d expect, then, that if the ball were still juiced in 2018, the league’s highest hard-hit rate ever might produce the highest league ISO ever.

No such luck, though; 2018’s .161 ISO falls a full 10 points short of last year and a tick short of 2016. Which is odd, see, because batters are hitting the ball harder than ever. Since 2015, when sabermetricians first noticed the ball was juiced…

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There are, like, a dozen articles of this nature written daily — that is, “buy-low” candidates using some kind of xMetric, likely derived from Statcast. That’s fine. I’m not hating on it. This was my modus operandi when I first started writing at RotoGraphs, and it’s how I really started to understand the cyclicality of player performance and the differences between descriptive and predictive metrics.

Speaking of which, I have no desire to rehash the “what xwOBA should really represent” discussion that consumed the sabermetric sphere a week or two ago. (Although, for reference, I’ll link you to Baseball Prospectus, MLBAM’s Tom Tango, and FanGraphs’ Craig Edwards.) Primarily, I want to provide some facts about xwOBA followed by some non-facts about how I use xwOBA to keep my biases in check.

There are two important tenets to xwOBAism. At the player level, wOBA does not always converge on xwOBA…

1. in a given season.
2. over the course of a career.

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