Introduction
To start our series diving into sabermetrics, we are going to start with an increasingly popular advanced statistic called FIP, which means Fielding Independent Pitching. But first, sabermetrics is a term originated by Bill James in the 1980s and means simply "the search for objective knowledge about baseball” (Society for American Baseball Research). Sabermetric statisticians wanted to use all the knowledge readily available to them to create a more advanced category of statistics to better analyze players' stats and answer questions in line with the current data evolution of baseball that has taken place over the last few decades.
What is FIP?
FIP is one of the more simple sabermetrics statistics that aims to calculate the effectiveness of a pitcher in preventing home runs (HR), walks (BB), and hit by pitches (HBP), while picking up outs and strikeouts (K).
Singles, doubles, and triples are not included in the calculation because sometimes a hitter is given for a hit as result of a ball put in play that a fielder could have caught in the air, or fielded cleanly on the ground to record the out for a pitcher. For example, lets say a line drive was hit to the right fielder and he dives and completely misses with his glove catching the ball, and the ball rolls to the outfield fence and the batter makes it to third base. He would be awarded a triple because while seemingly the outfielder made an "error", the baseball never touched his glove, thus allowing the batter to take credit for hitting a triple.
The point of calculating FIP is to avoid situations, like the one mentioned above, playing a major role in the pitcher giving up runs and hits that he had no control over and effecting stats like their ERA and BAA (batting average against). Fielding Independent Pitching only focuses on outcomes where any of the eight other players on the field besides the pitcher aren't involved in.
Here is equation for Fielding Independent Pitching (credit to Fan Graphs):
Each component that goes into calculating FIP is given a weighted value that helps make the equation an accurate representation of the pitcher-controlled outcomes from every at-bat. The number of home runs given up is times by the value of 13, this value is then added to the three times sum of the total number of walks and hit by pitches. Then, two times the number of strikeouts is subtracted from the previous value. This is all put on the top of the equation which is then divided by the total number of innings pitched plus a constant.
The constant changes each season and is set to the value that makes the MLB average FIP each season equal to the MLB average ERA. This makes it so that the FIP scale (0 to infinity) is the same as the ERA scale (0 to infinity).
FIP Calculation Example
Let's take a look at Pedro Martinez's (Hall of Fame pitcher in the 1990s and 2000s) stats from the years 1997-2003, which was when Martinez was arguably in his prime and had his best years in terms of FIP.
Row of focus is highlighted in light blue, cells essential to the equation are highlighted in yellow, and then the final FIP value is highlighted in green.
(Stats courtesy of Pedro Martinez's Baseball Reference page)
In 1999, when he had his season low in FIP with a value of 1.39, he only gave up 9 home runs, 48 total BB/HBP, and struck out 313 batters in 213.1 innings pitched. So, by plugging the numbers into the equation we'll see how to arrive at the value of 1.39:
(FIP constant obtained from Fan Graphs)
So, in only looking at the outcomes from an at-bat where Martinez had control over throughout the 1999 season, we can see that a 1.39 FIP is better representation of his individual performance by looking only at balls not put in play on the field. His ERA (earned run average) that year was a 2.07, but with FIP, we see a 0.68 difference between the two values. This difference shows us the magnitude of the effect that balls in play had at inflating the pitcher's ERA.
Closing Thoughts
By covering the ins and outs of FIP, Fielding Independent Pitching, hopefully you would have learned more about a statistic that is being used heavily within the realm of modern baseball data analytics. Furthermore, mostly all sports are shifting to this data-heavy approach to better analyze player's performance in order to help train the player more effectively and reach their potential by utilizing their strengths and improving on their weaknesses.
This article serves as the first of a series of articles that will cover complex sabermetric topics. So, be sure to tune in next Friday, September 22nd for part two!
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