Who You Face Matters

The beauty behind the philosophy of advanced analysis is that it seeks to eliminate as much variance as possible. As simple of a thought as this may be, it is one that still eludes the majority of the baseball world, and most of society in a variety of other areas. Our metrics here at Fangraphs do seek to base value on much more that raw numbers. We can not only adjust for league, but also park and era, among other variables.

Unfortunately, there seems to be a limit to this. As much as we know Dan Haren gets unlucky for pitching at Chase Field (or the opposite for Dante Bichette at Coors), or that pitchers in the late 60s were helped by a higher mound (or the opposite thanks to steroids in the late 90s), we still haven’t found a way to, somewhat literally, level the playing field in terms of whom a pitcher or hitter faces. Here’s an example:

Tim Lincecum: 6.4%
Bronson Arroyo: 7.4%
Jonathan Sanchez: 5.3%
Wade LeBlanc: 6.0%
Mike Leake: 8.2%

Those are the HR/FB rates of the pitchers in baseball who have faced the worst opponents in baseball sorted by OPS. Here are some more:

Josh Beckett: 13.0%
Joel Pineiro: 10.9%
Jeremy Guthrie: 8.6%
David Huff: 10.1%
Mitch Talbot: 7.8%
Ben Sheets: 12.0%

Those are the starting pitchers who have faced the best hitters based on OPS this year. I didn’t run a full study, but I would think that the correlation between HR/FB and the OPS of opposing batters is decently high. This is logical and intuitive: better hitters in baseball have better HR/FB rates, so if you face more of them you’re likely to feel the effects (and vice versa). When you see a pitcher have a bunch of years of giving up HR/FB rates either above or below average, you may want to believe it is more of a “skill” than a “trend.” But said pitcher may have just been facing competition the whole time that would dictate the results, and with a little bit of luck added in, it looks like a trend.

But what does it mean? It means we shouldn’t just think of things like HR/FB and BABIP as a pitcher getting “lucky” or “unlucky” based on the quality of the balls in play, but also by the quality of the opponents. Tim Lincecum‘s opponents have an OPS of .675 this year. For reference, that’s about 2009 Randy Winn, who had a wOBA of .302. Josh Beckett’s opponents have an OPS of .767 this year. That’s roughly 2010 Chipper Jones, who has a .349 wOBA.

Luckily, that’s as big of a difference as you’ll generally find. However, sorting out even the most minor differences has some significant value. I don’t have a panacea, but it’s something we should keep in the back of our minds when analyzing players. It often goes overlooked.

Pat Andriola is an Analyst at Bloomberg Sports who formerly worked in Major League Baseball's Labor Relations Department. You can contact him at Patrick.Andriola@tufts.edu or follow him on Twitter @tuftspat

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12 years ago

But how much can you influence the hitters that you face? You expect good pitchers to have low OPS against them, deflating the OPS of hitters they face. In a division like the NL west (where 3 of those pitchers come from), with good pitching all around, I wonder if there is a dampening effect on the OPS of hitters the pitchers face.

12 years ago
Reply to  AK707

One more reason to do away with the imbalanced schedule.