Correlating Curt Schilling’s stat PDF with FIRE

I recently did this with Jim Bowden’s OPSBI and it was too much fun to not do this again.

Curt Schilling invented a stat called “Pitchers dominance factor.” You take a pitcher’s ERA and add it to WHIP*9. Then, you do the same for the overall league. Finally, you subtract the pitcher’s number from the league number. Here’s that in equation form:

(LeagueERA+LeagueWHIP*9) – (PitcherERA+PitcherWHIP*9)

Colin Wyers was expressing his disinterest  for this stat on twitter (@cwyers) and I told him I was looking at correlating it with something, but wasn’t sure what to use. He told me – and I quote – “Correlate it WITH FIRE.” So that’s what I’m going to do.

I invented a stat called Fielding Independent Runs Equivalent (FIRE). It takes the Baseball Prospectus stat SIERA, divides by 9 and multiplies by Innings Pitched:

FIRE = SIERA/9*IP

SIERA is a fielding-independent pitching statistic in the form of ERA. Basically, if a pitcher had a league-average defense, park and luck, what would his ERA be? FIRE is simply converting SIERA to runs.

Here is PDF vs. FIRE for 2010, with only those pitchers who qualified for the ERA title (greater than 162 innings pitched):

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There’s a trend, but the r^2 value is pretty bad.

Here’s 2010 with all pitchers with greater than 49 IP:

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Even worse. Sorry Colin, FIRE doesn’t work well against PDF.

How Good of an Offensive Metric is OPSBI?

Update: Just so everyone is clear: I do not agree with the concept of adding RBIs to OPS. If you are looking for a “quick stat,” I recommend looking at OPS all by itself; it does a fine job. The purpose of this was to simply look at OPSBI and see how well it correlates with a more advanced statistic.

Last night, Jim Bowden put up a post on ESPN.com, wherein he created a new stat called OPSBI. Basically, you take the OPS of a player, multiply it by 1000 and add RBI to it. I can honestly say I’ve never thought of doing this, but let’s see how it stacks up to an advanced hitting metric like Fangraphs batting runs. I took only players with greater than 1500 plate appearances in the past three years. This provides a 133 player sample. Here ya go:

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R^2 of 0.84 with batting runs? Not as terrible as you may think.