Archive for Statcast

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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Note: As was the case in a previous analysis of Statcast measures and their correlation with power metrics for hitters, I owe a debt of gratitude to Alex Chamberlain. He did a lot of heavy lifting for this column, running the correlations and discussing interpretations with me.

It won’t be the first or last time, but I did a silly thing on Twitter. In announcing a pick for the Pitcher List Experts Mock, I decided to tout the player I chose by citing one of his achievements, as captured by a Statcast metric.

Fun fact: Trevor Williams has the 3rd-lowest avg. exit velocity allowed on flies and liners over the last two seasons (min. 400 BBE), behind only deGrom and Kluber. Just took him in Rd 21 of the #PLExpertsMock. @JustinMasonFWFB, you’re up!

— Al Melchior (@almelchiorBB) December 13, 2018

(Justin, by the way, made his pick very promptly.)
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I recently wrote about Statcast measures that were highly correlated with power metrics, such as HR/FB and ISO. The research confirmed that several Statcast measures could be useful tools for identifying undervaled power sources. One factor I ignored in that analysis was pull rate, but in taking a belated look at it, I found that one of the apparently strong relationships gets notably weaker when we control for a hitter’s pull tendencies.

In general, exit velocity on flyballs and line drives turned out to be strongly correlated with ISO for hitters with at least 150 batted ball events in 2018. However, when you isolate the top 10 percent of the sample in terms of pull rate, the relationship is still meaningful, but it’s not quite as strong. That could have implications for how we view two of last season’s top power hitters, Jose Ramirez and Alex Bregman.
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Note: Many thanks to Alex Chamberlain, who provided the correlations cited in this column, as well as insights regarding some of the relationships.

As I have searched for ways to spot undervalued power sources over the last few seasons, I have relied heavily on several Statcast metrics that are available on Baseball Savant. I have leaned especially hard on average flyball distance. While the leaderboard typically includes several players who are proven power sources, it has also featured some players who appear to be undervalued. For example, Scott Schebler, Kendrys Morales, Trey Mancini, Tim Beckham and Mitch Moreland all finished in the top 20 percent in average flyball distance in 2017 (min. 50 flyballs), and that gave me a little extra confidence to give them a try in 2018. For stretches, Morales and Moreland paid some dividends, but it was far from a foolproof method.
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Yesterday, I wrote about how sprint speed relates to wOBA minus expected wOBA (wOBA–xwOBA). Today, I summarize my investigation into what factors most readily affect a player’s stolen base success rate (SB%).

This invitation from BatFlip Crazy, embedded in this lengthy Twitter exchange, served as the catalyst for the research. In hindsight, I’m not sure I totally answered the question. Manipulating data from multiple different sources (in this case, Baseball Reference and Baseball Savant) can be exhausting.

I used my final Frankenstein data set, which contained statistics for all players from 2016-18 with at least 100 stolen base opportunities (SBOs) in a given season, to investigate relationships among the following various stolen base metrics:

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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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Long has the relationship between pitcher performance and batted ball metrics been dubious. The Sabermetric community has a solid understanding of why, fundamentally, a pitcher is good or bad. Strikeouts are good. Walks are bad. Hits by pitch are also bad. Home runs allowed are especially bad. So on, so forth. And by no means are batted ball metrics useless. It’s how we know ground balls allowed are superior to fly balls allowed, for example.

The community had hoped, however, that more granular batted ball metrics would help us better explain some of the more nuanced elements of pitcher performance, including those related to luck, such as batting average on balls in play (BABIP) and the percentage of home runs per fly ball (HR/FB). Since their introduction to the public sphere in 2015, and even with the inclusion of more granular Statcast data in 2016, any relationships that might exist between the physics and outcomes for batted balls during an individual pitcher’s season are still poorly explained. The following table depicts the correlations between pitcher BABIP and various batted ball metrics, sorted by the strength of the relationship (all qualified seasons, 2007-17, n = 898):

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