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.
So naturally when I’m handed a bunch of really awesome data I want to play around with it and you, the lucky audience, get to benefit from my flaws as a human being. First off, go download this because it will retain all the formatting and give you a ton of really easy sorts or your own calculations or whatever you want to do. Seriously, it’s yours. If you don’t want to do that then you can use this table below to go through the tabs and whatnot to see how everything breaks out:
This workbook starts by looking at the players, and I’ll get there, but first I think it’s easier to focus on the teams over the past four years. Over this time frame the Tigers have been incredible pitching nearly 10% better than average with 2013 and 2012, respectively, being the two best team-seasons of the 120 possible. The Rays check in at second, overall, while boasting the 3rd best team-season with their performance in 2012. Know that last year’s performance ranked 7th best so at least we had something fun to watch other than the Rays scoring runs. The Rays are closer to the third place Yankees than the first place Tigers with the Yankees showing some pretty solid consistency year to year. You can peruse the rest because here’s where I want to focus on the Rays:
I’ve created the statistic “Runs” based on this stuff so that we can more easily compare starters and relievers. Basically we’re comparing each cFIP to average (100) and then accounting for the number of batters faced. These aren’t actually runs, but let me know what you come up with and we can roll with that. The formula for, say, David Price’s best-in-sample 2011 would be ((74-100)/100)*918. It’s simple and it works so let’s roll with it. Price has been the team’s best pitcher over the last four years, but that Shields guy was only a notch or so behind, though it is interesting to see his cFIP gradually increase year over year (79/82 with Rays and then 91/96 with Royals). Pick through this stuff and when you get to the bottom feel free to smirk over the fact that the Rays got two somethings for the pretty lousy pitcher known as Jeremy Hellickson.
We can flip over to the pitchers now, but you should probably have a solid handle on what we’re looking at now so I’d leave you to it. Some surprises for me were Ernesto Frieri being better than you probably thought last year and incredible the two years prior. Also, Joel Peralta might be a bigger loss than we thought, homers and half hour innings included. Obviously, Kershaw is a freak combining excellence with bulk and you’ll see many other familiar names right there with him. I’d love to hear who some of your surprises are or who stands out to you as particularly over/under-rated.
Once teams get a little closer to paring down their rosters I want to apply this stuff to each team’s upcoming staff to get a feel for how teams rank and compare. Judge’s cFIP couldn’t have come at a better time with fantasy leagues drafting now and for the next few weeks so I hope that you find this stuff useful. In the meantime, please feel free to use this stuff as a cheatsheet. I think most fantasy players underrate elite relievers and hopefully this “Runs” metric using the cFIP data gives you an idea of how to combine those two communities.