ERA, probably the single most cited reference for evaluating the performance of a pitcher, comes with a lot of problems. Neil does a good job outlining why in this FanGraphs Library entry. Over the last decade, plenty of research has cast a light on the variables within ERA that often have very little to do with the pitcher himself.
But what is the best way to use fielding-independent stats to estimate ERA? FIP is probably the most popular metric of this ilk, using only strikeouts, walks, hit batters, and home runs to create a linear equation that can be scaled to look like an expected ERA. Then there’s xFIP, which is based off the idea that pitchers have very little control over their HR/FB rate; to account for this, it estimates the amount of home runs that a pitcher should have allowed by multiplying their fly balls allowed by the league average HR/FB rate.
For many people, however, these are too simple. FIP more or less ignores all balls in play completely; xFIP treats all fly balls equally. Neither one correctly accounts for the effects that any ball in play can have; we know that the wOBA on line drives is much higher than the wOBA on pop ups, but we don’t see that reflected in many ERA estimators. The estimators we use also are fully linear, and may break down at the extreme ends; FIP tells us that a pitcher who strikes out every batter should have an ERA around -5.70, which is, well you know, not going to happen.
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