Nate Schwartz (@_nateschwartz) and Kyle Bland (@blandalytics) about ERA estimators and how to apply them.
The discussion starts by talking about why ERA needs to be estimated. There are so many factors that go into run prevention that make it hard to value a pitcher just on ERA. They first dive into FIP, breaking down why it makes sense to remove balls in play from the equation entirely. This starts the focus on strikeouts and walks as the main identifier across the board. The chat moves to xFIP, which adjusts for league-average home run rate. Next, the two talk about SIERA, and how it’s complex compared to the others and is the most predictive. After covering the main ERA estimators, they shift to how xERA is powered by Statcast data, but that doesn’t make it more predictive. Finally, they wrap up by mentioning PLA, Pitcher List’s scaled stat for pitch level value (PLV) and the players that stand out across these estimators.
Articles discussed: The Relative Value of FIP, xFIP, SIERA, and xERA Pt. II, Dan Richards; pFIP: Pitch Height, Launch Angle, and the FIP Framework, Alex Chamberlain; Predictive Classified Run Average, Connor Kurcon; What is PLV?, Nick Pollack
