We’ve already addressed the concept behind WAR (Wins Above Replacement) in the abstract and for offensive players in particular. Today, we’ll consider how we quantify runs for pitchers and overview calculating their WAR. ...

Relief pitchers can enter the game in very different situations, and that should be accounted for when evaluating their performance. Coming in to clean up a 12-0 victory (or defeat) is different than coming in during a close, tense game with a lot on the line. We measure this disparity using Leverage Index (LI). ...

ERA+ is a funny, messy statistic. Unlike OPS+, it doesn’t tell us how much better a player is than the league: it instead tells us how much worse the league is than the player. This ignores our intuition and causes unnecessary confusion. Furthermore, it makes it harder to use as a tool for direct comparison: someone with a 200 ERA+ is not twice as good as someone with a 150 ERA+, while that would effectively be true with OPS+ (and similar offensive statistics.) ...

FIP is a cool, clever, and simple statistic to try and normalize how we work with ERA in a way that simply ignores defensive factors. However, we can ignore, or normalize for, at least one other factor to adjust the comparison. ...

Halfway through our pitching posts, let’s take a step back and learn about some miscellaneous statistics that we’ve either barely touched, or entirely ignored. These are all counting statistics that we use to help determine value and ability. While most are not explicitly used when calculating advanced statistics, they have historical cachet and many fans (including me) still enjoy looking at them. ...

Remember many days ago when we talked about how pesky it is to have a ball in play? That’s as true for pitchers as it is for hitters. So, some enterprising folks struck out to roughly measure ERA in a way that ignores balls put in play, instead focusing entirely on the Three True Outcomes. Fielding Independent Pitching (FIP) is a defense-agnostic statistic to compare against ERA, and in some ways is an ideal “predictor” statistic for ERA. ...

That’s right, the “+” statistics exist in the realm of pitching. Park adjustments and a percentage normalization is a natural extension of ERA, and makes it more meaningful for directly comparing pitchers, even across generations. ...

While ERA is a measure of overall pitcher effectiveness, there’s a more nuanced statistic that considers how frequently a pitcher allows runners on base. Walks and Hits per Inning Pitched (WHIP) is easy to calculate, has a cool acronym, and is another factor to consider when measuring how good a pitcher is at keeping hitters from doing their jobs. ...

While ERA is the best way among the simple statistics to compare pitchers, it does not tell the whole story of their season. Historically, there is much interest in the details of a pitcher’s approach and how that influences their ERA. Does a pitcher rely on power and strikeouts, or precision and defense? ...

Let us leave the world of sluggers and slap-hitters behind us, and move 60 feet forward to the pitcher’s mound. I’ve always enjoyed and valued pitching over hitting in my baseball life. As such, the first ten days of hitting required me to learn more than I anticipated, while I feel more prepared to handle this section of Blogmas.1 We’re not starting as simple as possible, but we are starting with the most important of the original pitching statistics: how good are you at preventing runs from scoring? ...