FIP: A Simple, Predictive Stat.


 

Many different, complex statistics have been created over the past couple decades that tend to overwhelm the average fan. It’s impossible to memorize all of them, and they all speak at so many different levels that the average fan often doesn’t know which one to listen to. The result: all the stats just get thrown out the window. However, the Fielding Independent Pitching statistic (a pitching statistic abbreviated as FIP) does a fantastic job of capturing the essentials of pitching in one stat, while keeping it in layman’s terms. FIP essentially asks this question: what would a pitcher’s ERA look like based on the stats he directly controls, like walks, strikeouts, hit by pitch, home runs, etc? FIP attempts to take defense completely out of the equation, while adjusting the stat to make it look like an ERA. However, with the above definition in mind, FIP should do an even better job of predicting future performance than judging past performance. For instance, if a pitcher has an ERA of 3.30 one season while posting 4.30 FIP, what can you deduce from those two numbers? 1) he is either extremely good at working in and out of jams and not allowing his base runners to score or 2) he got lucky, and is simply a problem waiting to happen. Of course, there are a fair number of pitchers that fall into the first category and are professional Houdinis, but I would venture to say that a majority of pitchers fall into the second category. Of course, this  analysis can also work inversely; pitchers that have high ERAs but low FIPs could be due for better luck in the following season.

Having a FIP higher than his ERA by a whole point, Jeremy Hellickson is now seeing all those baserunners come back to bite him.
Having a FIP higher than his ERA by a whole point, Jeremy Hellickson is now seeing all those baserunners come back to bite him.

 To test FIP’s ability to predict, I organized all the pitchers in 2012 who posted higher FIPs than ERAs by at least 1/2 a point, to see if they regressed towards their FIP at all in 2013, and the results were startling. All eight of the pitchers who fit the qualifications posted higher ERAs in 2013 than 2012.  The eight: Matt Cain, Kyle Lohse, Jordan Zimmermann, Jered Weaver, Ross Detwiler, Aaron Harang, Jeremy Hellickson, and Jason Vargas. To test the inverse theory, I organized all the pitchers with whose ERA exceeded their FIP by at least 1/2 a point, and theresults all but confirmed the theory. 7 of the 8 players tested posted lower ERAs in 2013 than in 2012, no doubt because they were paying more often for the walks and hits they were surrendering. The eight players: Adam Wainwright, Max Scherzer, Yu Darvish, Ricky Nolasco, Joe Blanton, Jon Lester, Tim Lincecum and Justin Masterson. Note that Wainwright, Scherzer, Darvish, and Lester have all become one of the top pitchers in the game; they do a fantastic job of keeping guys off base.
If you own a fantasy team, consider FIP when looking for pitchers. It could be the difference between a Cy Young and a fluke.

 If you’re interested, here’s the formula for FIP:  ((13*HR)+(3*(BB+HBP))-(2*K))/IP+constant

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2 thoughts on “FIP: A Simple, Predictive Stat.

  1. Reblogged this on Lean Learning and commented:
    I love seeing people combine things we already understand into something new. Baseball fans know that ERA (Earned Run Average) is a pretty good way to evaluate a pitcher’s performance. While most of us can’t recite the formula to calculate ERA, we do know that lower is better. We also know that there is lots of randomness (luck) related to ERA. I’m not sure if I’m ready to sign on to FIP as the next big thing in baseball statistics, but I am intrigued!

  2. Like you said, I don’t think FIP is a household stat yet, but I think it will become one soon. The formula for FIP is complex, but the overall idea is simple: it captures all the numbers that only the pitchers control. It’s simplicity should make it appealing to casual fans.

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