Please see Part I for acknowledgments.

Back? Good.

So what do we have so far?

1) We’re attempting to figure out Mariano’s WAR for his postseason innings and then convert that to the number of championships Mariano is worth all by his lonesome.
2) We’ve explained the theory behind WAR and replacement level (though we haven’t gotten into the nitty gritty just yet)
3) We’ve discussed why it’s still all about the leverage, how really smart people have come up with absolute numbers for every conceivable innings-baserunners-outs situation, and how WPA, while shiny and a fun toy, is not as helpful as we would like because it’s a probability stat more than an absolute number.

Fortunately for us, since Fangraphs provides us with LI numbers, it’ll save us a bit of work.

The other key component of WAR is FIP, or fielding-independent pitching. The goal of FIP is simple: figure out how well a pitcher pitches in terms of events that are not dependent on the fielders–strikeouts, walks and home runs.

A little more advanced: the theory here is that things such as singles, doubles and triples, may be affected as much by the way the fielders play as by the way the pitcher pitches. The only events a pitcher directly controls occur when a batter does not make contact with the baseball, or when he hits a home run. Basically, all or nothing.

The formula provided by Beyond the Box Score for FIP is:

(HR*13+(BB+HBP-IBB)*3-K*2)/IP.

This formula will give you an odd looking decimal result; generally speaking you add 3.20 to it to get the FIP.

A note of caution: I was never able to get the formula to add up to the same results given by Fangraphs; since different sources do use different formulas (some don’t account for hit-by-pitches, some don’t account for intentional walks, etc), I’m going to attribute the difference to using a different formula than Fangraphs–since my results were generally close.

What we need to do, then, is to figure out the WAR numbers.

Let me state this as simply as I can: I have no issue understanding the theory behind WAR, as hopefully I have successfully explained above; however, WAR is a very complex calculation that is way beyond the scope of my doing. Fangraphs and Beyond the Box Score give formulas as to how to calculate them for pitchers here and here , but the problem is that every time I tried doing the formula myself, I’d end up with very, very wacky numbers.

Like, 74.

I know Mo’s the Hammer of God, and all, but even the Hammer of God isn’t worth 74 wins all by his lonesome.

To make a very long and frustrating story short, I was saved by an Angel of Mercy (who has asked their identity not be revealed–which, alas, means no revelation of formula. Don’t worry, though, I still can’t figure it out). Said AoM sent me a magical calculator thingy (okay, a spreadsheet), and, well, now it’s just a question of doing the following:

1) Calculating Mo’s WAR for each of his postseasons
2) Adding the totals together.
3) Converting them to a scale that will allow us to compare what he’s done in the postseason with what he’s done in the regular season.

Simple, right?

Alas, this is the part that is incredibly time-consuming.

WAR involves constants in their formulas that change from year to year, and since Mo’s been pitching in postseasons since 1995, that’s a lot of constants to go back and find.

Anyway, before I go into the hard data and the results I got, here are a couple of caveats–so if you want to try this on your own (masochist!), you might choose to adjust accordingly. I’d recommend that if you try (masochist), you use the link to the Fangraphs explanation, which calculates WAR for Felix Hernandez from scratch. The theory’s easy to follow, but because there are so many components to it, just one not-so-hot constant can throw it off. This is, of course, why they pay people to do these things.

1) We need to understand park factors, but this is a simple concept.  Some ballparks favor pitchers and some favor hitters; the park factor is simply a number that attempts to describe whether a ballpark favors hitters or pitchers.  The park factor used for 2009 is 0.975, which would make Yankee Stadium (and here you will laugh) a slight pitcher’s park–A neutral park that favors neither pitchers nor hitters will have a park factor of 1. What’s that you say? That can’t be right?

Well…yes. And no.

ESPN has a handy-dandy Park Factor sheet. Now you’ll see for 2009 the New Yankee Stadium is actually middle of the pack and registers 0.965. So why increase it to 0.975? New Yankee Stadium has only been around a year, which is a very small sample size.  The extra 0.010…let’s just say it’s a nod to all those home runs.

For all the years before 2009, I took an average of park factors from 2001 to 2008 and came up with 0.962.  Don’t worry–2005 had a park factor of 1.4+, but this was balanced by an absurdly low 2004.  Our 0.962 constant is really right there in the median.

(Going backwards, the numbers we use are 1.040+0.987+0.877+1.403+0.694+0.933+0.957+0.805)

2) The leverage used is a modified gmLi (see above) that gives the pitcher some credit for the leverage of his situation, but not all–basically it says that it’s not Mo’s fault that Brian Bruney left the bases chucked with no one out, but if Mo puts his own runners on base and then lets them score, he’s gotta be accountable for that, too.

3) The FIP stats are from Fangraphs.

4) As for the constants in the formulas? People get paid to figure those things out. I rely on the AoM’s Magical Calculator Thingy.

So how does one use the Magical Calculator Thingy?

One needs five pieces of data:

1) The pitcher’s FIP,
2) The league’s RA (this is the average runs per game per team. Here we’re going to use the postseason).
3) The park factor-we’re using the 0.975 constant for 2009 and the average of 0.962 for every other year.
4) The pitcher’s innings pitched.
5) The modified gmLI

Since numbers 1, 2, 4 and 4 will change season to season, the calculations have to be done separately for each season.  Depending on the park factor you use, this number can also vary.

As I’ve said before, calculating FIP is itself complex, but we can cheat and just look at advanced pitching stats from Fangraphs. Like many of the advanced stats, can use various formulas depending on the publication you are reading.

Anyway, the bottom line here is that we use Fangraphs’ Data because super smart people have already done this for us.

Doing the RA isn’t hard, but it IS tedious.

RA is simple–it’s just the total runs scored, divided by games played, and then divided again so you get an average runs scored per team. This takes into account all runs, not just earned runs–since earned runs are a somewhat sketchy stat. It’s easy enough to find the RAs for a particular season, since ESPN handily lists average runs at its stat page, but the numbers haven’t been done for the postseason.

What does this mean?

We’ve got to total every run scored by every team in every game in the ALDS, NLDS, ALCS, NLCS and World Series, and then divide that by the total games played in the postseason, and then divide again to get the number per team. Normally you just used it for the AL or the NL, but I like to consider the postseason a league in its own right.

Again, this isn’t hard, it’s just tedious.

Innings Pitched–this one’s easy. Google “mariano rivera stats” and click on any of the links. Since WAR is heavily dependent on innings pitched in terms of value, relievers’ PREWAR (postseason reliever WAR, don’t look at me like that, I just think it sounds cool) will be low–anything over 1 being positively insane.

To compare it to what a WAR would be at the same FIP over a full season, we’ll also plug in the numbers not just for postseason IP, but also for 70 IP, which is about what a closer will pitch over a full season.  This will give us an idea of how valuable Mariano would be if he pitched at that same level over the course of a regular season (I’ve explained it more after we get our results).

When we get our numbers and we’re doing our aggregate totals, we’ll use both the PREWAR numbers and the converted PREWAR numbers, so if you want to try some fancy stats work on your own, you can go right ahead and do so.

So, let’s get to the nitty-gritty.

Well.  In a litle bit.  Check back at 2 PM…