Archive for xwoba

At a basic level, nearly every piece at FanGraphs represents an attempt to answer a question. What is the value of an opt-out in a contract? Why do the Brewers continue to fare so poorly in the projected standings? How do people behave in the eighth inning of a spring-training game? Those were the questions asked, either explicitly or implicitly, by Jeff Sullivan, Jay Jaffe, and Meg Rowley just yesterday.

This piece also begins with question — probably one that has occurred to a number of readers. It concerns how we evaluate pitchers and how best to evaluate pitchers. I’ll present the question momentarily. First, a bit of background.

Fielding Independent Pitching, or FIP, is a well-known tool for estimating ERA. FIP attempts to isolate a pitcher’s contribution to run-prevention. It also serves as a better predictor of future ERA than ERA itself. The formula for FIP is elegant, including just three variables: strikeouts, walks, and homers. It does not include balls in play. That said, one would be mistaken for assuming that FIP excludes any kind of measurement for what happens when the bat hits the ball. Let this be a gentle reminder that home runs both (a) are a type of batted ball and (b) represent a major component of FIP. There is, in other words, some consideration of contact quality in FIP.

Expected wOBA, or xwOBA, is a newer metric, the product of Statcast data. xwOBA is calculated with run-value estimates derived from exit velocity and launch angle. Basically, xwOBA calculates the average run value of every batted ball for a hitter (or allowed by a pitcher), adds in the defense-independent numbers, and arrives as a wOBA-like figure. The advantage of xwOBA is that it removes the variance of batted-ball results and uses a “Platonic” value instead.

The introduction of Statcast’s batted-ball data is exciting and seems like it might help to better isolate a pitcher’s contributions. But does it? This is where I was compelled to ask my own, relatively simple question — namely, is xwOBA better for assessing pitcher performance than the more traditional FIP? What I found, however, is that the answer isn’t so simple.

The differences between FIP and xwOBA, as well as the similarities, deserve some exploration.

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Over in the American League, there’s a clear two-horse race between Chris Sale and Corey Kluber for the Cy Young Award. Both are head and shoulders above the rest of the league and both have very strong cases for the honor, depending on what metrics you prefer.

Over in the National League, that isn’t quite the case. Max Scherzer is the clear front-runner at this point, with a host of other pitchers behind him all trying to make an argument why they might have had better seasons. Clayton Kershaw has a lower ERA. Zack Greinke pitches in a much tougher park. Teammate Stephen Strasburg has a lower FIP.

Those are just the stats that measure outcomes, though. Let’s see what Statcast has to say about the sort of contact the other candidates are allowing to see if anybody has a real case against Scherzer.

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Last week, I took a look at Statcast data from 2016 and 2017 and attempted to find contact-management skills among pitchers. The basic conclusion of that study? Pitchers might well have skills to manage contact once the ball hits the bat; if they do, however, neither xwOBA nor Statcast classifications seem to reveal it. Quality of contact didn’t hold up from year to year — i.e. last year’s results on contact aren’t likely to inform much of this year’s results on contact.

In the comments section, however, one reader wondered if in-season results might create a different result. That’s what I’d like to examine in this post. Here we go.

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