About once a week, I am going to take an advanced statistic, and profile and explain it. If anyone wants to see any specific stats explained, just say so in the comments, or on Twitter, and I’ll do my best to get to all of them.
For the first installment I am going to look at two closely related stats, FIP (Fielding Independent Pitching) and xFIP (Expected Fielding Independent Pitching).
These stats are meant as aids for ERA, as they only factor in what a pitcher has control over. The three things that a pitcher has true control over are strikeouts, walks/hit-by-pitches, and home runs. After that, it is in the hands of the defense. The basis of these stats are that one should not debit a pitcher for the actions of his defense, or just pure bad luck.
The origin of FIP comes from one of the pioneers in the sabermetric community, Voros McCracken. McCracken noticed that pitchers ERAs would constantly fluctuate, and he was fascinated by this.
He found that pitchers strikeout and walk rates would stay constant, while a pitchers BABIP (Batting Average on Balls In Play) would fluctuate from year to year, affecting a pitchers ERA.
For an example of this, let’s look at Tim Lincecum.
2008 10.51 K/9 3.33 BB/9 43.9 GB% .304 BABIP 2.62 ERA
2009 10.42 K/9 2.72 BB/9 47.5 GB% .282 BABIP 2.48 ERA
2010 9.79 K/9 3.22 BB/9 48.9 GB% .310 BABIP 3.43 ERA
2011 9.12 K/9 3.57 BB/9 47.9 GB% .281 BABIP 2.74 ERA
So looking at this data, we can see that despite similar GB rates, walk rates, and K rates in the same ballpark, that Lincecum’s ERA and BABIP would jump up and down accordingly. This is where FIP comes in.
The formula, as you can see here, takes into account HRs, BBs, and Ks, and then adds in a constant to put it on the same scale as ERA. Although obviously not perfect, this stat is much more predictive than ERA, while taking into account a pitcher’s true performance.
Lincecum’s FIPs from those years are 2.62, 2.34, 3.15, and 3.17. Although they do jump a bit in 2010 and 2011 due to Lincecum’s lower K rates and higher BB rates those years, it is more consistent from year to year.
While FIP intends to show how a pitcher performed up to that point, xFIP attempts to predict the pitcher’s performance.
Developed by Dave Studeman of The Hardball Times, xFIP takes into account the league average HR/FB rate and multiplying it by a pitcher’s FB%. You can see the formula here.
As you can see in the new formula, the change is how HRs are accounted for, as xFIP regresses the HRs to league average, as they would be expected to do.
Obviously xFIP is assuming that all pitchers are created equally at home run prevention, which is not always true. One stat that tries to counteract that is SIERA, which you can see here.
Well that’s going to be about it for this installment, remember to request certain stats that you would like to see explained, and I will do my best to get to those. For those that do not want to wait for my explanations, Fangraphs.com has an excellent glossary that details most of the commonly used advanced statistics.