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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My favorite part of this year’s World Baseball Classic, aside from the baseball, obviously, was the television broadcasts’ frequent reference to players’ swing speeds. I was floored, even if only because I didn’t know (but should’ve known) we had the technology capable of measuring it. Regarding Major League Baseball and Statcast’s adoption of such a metric, a little birdy told me I shouldn’t hold my breath. Disappointed, I moved on.

Then yesterday, while fooling around in Baseball Savant’s Statcast database trying to diagnose the misalignment of Miguel Cabrera’s outcomes with his peripherals, I noticed the database query’s “sort by” function offered an option to sort by “estimated swing speed.” A quick Google search indicates to me the Statcast and MLB Advanced Media team(s) has (have) yet to formally announce this; sprint speed has been the more exciting recent development, apparently.

Not to me! I quickly got to work querying the data. I also quickly learned downloading the raw data files that underpin the swing speed summaries previously linked do not include swing speed, which is unhelpful. In other words, swing speed is not communicated to us from Baseball Savant’s organs on a play-by-play basis. I imagine this is by design. So, I was resigned to running a single query that summarized swing speed data at a high level: the average swing speed for every hitter with at least 100 at-bats in a given season, from 2015 through 2017.

Here’s what I found.

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Updated Feb. 25, 2017

Aug. 16, 2016: Updated Alex’s xBABIP equation and added Andrew Dominijanni’s xISO equation.
May 23, 2016: Published.

Jump around in this post:
Hitter metrics: xBABIP | xISO | xHR/FB | xOBA | xK%
Pitcher metrics: xHR/FB | xLOB% | xK% | xBB%

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *Do you frequently use RotoGraphs’ “X” (expected) metrics? Do you wish they were easier to find? Have you ever commented to ask if they could be added to the leaderboards or at least wished they were all located in one spot? If so, you may want to…

BOOKMARK THIS PAGE!

I don’t know if there will ever be a time when FanGraphs has a leaderboard devoted to “X” metrics. The fantasy analysts at RotoGraphs have taken a largely vigilante approach to creating descriptive and predictive expected metrics over the years. Moreover, each metric typically undergoes an iterative process by which we improve it when new data is made publicly available to the authors.

So, this is it. This is my best attempt, on behalf of RotoGraphs’ staff and at the polite and enthusiastic behest of its readers, to centralize the freshest versions of the relevant metrics the RotoGraphs staff most frequently cites. I have also built primitive Microsoft Excel-based calculators for some (but not all) of the metrics that crunch the numbers as long as you provide the appropriate inputs. It should save us all an extra minute or two and preserve our sanity a little bit.

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So, Jackie Bradley Jr. hit another home run last night. It’s his seventh of the season and his sixth since May 5. That’s six home runs in 13 games to go with a .404/.474/.762 line. All the while, he extended his hitting streak to 24 games. It’s pretty crazy. He’s on a tear, for sure.

But I’ve seen some overenthusiastic Tweets about his breakout being legitimate. That, I don’t fully understand. He doesn’t seem like a fundamentally different hitter than the JBJ we saw in 2013. Or 2014. Or 2015.

Obviously, the .271 ISO (isolated power) is impressive for a relatively tiny dude. It’s not far off his .249 ISO from last year, so it looks like it might be something sustainable. His strikeout rate (K%) is way down, too, which is undoubtedly a boon to his triple-slash line. But red flags abound with JBJ, all (or most) of which I will detail here.

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