It’s been fun to evolve as someone that has been talking about baseball on the internet since 2006 and with anyone that would listen for as long as I can remember prior to then. Anyone that grew up collecting baseball cards could probably tell you some of the more interesting statistics listed for their favorite players. For pitchers this was almost surely strikeouts or wins or Earned Run Average (ERA) functioning as a sort of triple crown for those that toe the rubber. ERA, in particular, was great because it felt like a solid way to compare guys all the way back to the Deadball Era and asserted the pecking order for who was better than whom. Then we found a better way.
It started with Fielding Independent Pitching (FIP) which focused on the things that pitchers could control. As someone that pitched in high school this made some sense. After all, I wanted the credit if I sat a guy down and if a guy couldn’t convert a two-hopper, well, then how should I bare the blame. Of course, on the other hand it’s hard for me to fault the other guys when I gave up a screamer to the gap so I felt like it all kind of came out in the wash. It also seemed like bull when you’d face a team that didn’t have a fence because what would normally be a double might easily turn into a homer. That’s not fair. So we can agree that things like opponent or the place where you’re playing matter quite a bit when it comes to expectations of performance.
Progress continued with further refinements to FIP like xFIP neutralizing home run luck or SIERA attempting to incorporate batted ball affects. These systems were stronger and another step forward, but while neutralizing certain things it had to make certain assumptions like where the pitcher pitched or that he always faced a league average batter. Things that we know are unrealistic, but we accept them because better is preferable to worse. Then you see the next step taken and you just want to shake the inventor’s hand.
Jonathan Judge ran the numbers (go read this, seriously) for all these things creating the headline-referencing cFIP that accounts for many of the issues above. He adjusts for the batter, pitcher and umpire. He adjusts for the actual park where the play occurred as well as the league when that’s relevant. By getting super granular we’re able to give the finger to sample size issues leading to not only better results for evaluative reasons, but even better we can have a high degree of confidence in cFIPs ability to predict what the future might look like bereft of injuries or luck. This is as close as we’ve ever been to predicting true talent so you’ll have to forgive my excitement.