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Wednesday, July 12, 2006

More on wins added

As promised, I'm going to offer a little more detail on the Wins Added system I used this week to evaluate the first half of the baseball season.

Let me throw out some explanation.

First off, I'd consider this to be a beta version of a system I'd like to use for myself in my efforts at covering baseball. As I pointed out in my column, as much as I admire the terrific work done at sabermetric sites like and, I often find myself in a quandary about using their metrics for writing analysis on baseball for the paper.

My primary problem, frankly, is one of space. When I'm addressing an issue, I can't use advanced concepts like VORP or WARP because they require too much explanation. Stat Guy addresses a mainstream audience. In fact, my original reason for writing it was to serve as a conduit between the hardcore statheads and the mainstream baseball fan. At the same time, I don't like to fall back on stalwarts like OPS and, certainly, batting average, etc., because I know their shortcomings all too well.

I've always wanted to see metrics expressed in terms of wins and losses. EQA, RAP, etc. are fine but give me the bottom line: how is it helping my team win or lose?

A big issue is that B.P. -- the industry standard -- is much more concerned with measuring player value as opposed to wins and losses. The concept of replacement value was and is revolutionary. But replacement value is not what I'm thinking of as I scan up and down a register of raw statistics and conventional percentages. I want to know how many wins and losses a player is responsible for. One number. Fielding, hitting and pitching. To accomplish this, I use league averages as my baseline instead of replacement value.

Anyway, that's what I have set out to do. The measurements are crude compared to some other systems but that's what I'm after. Simpler numbers told in a simpler way but yet still tell and important and accurate story.

The final results are expressed in exactly the way I'd like to seem them expressed in, say, Four columns at the end of a traditional statistical grid: hitting wins, fielding wins, pitching wins and, finally, wins added.

The underlying methodology behind the numbers will have to be tweaked, especially the fielding part. It would also be nice to introduce some kind of leverage index as well. As I say, this is the first step of developing a new framework for my own use. Eventually, I want to integrate it with my projection system, PROFITS, which has been locked away for a long time now undergoing major renovation.

HITTING WINS: Hitting Wins Added are calculated as extrapolated runs per plate appearance (XRPA) compared to league-average XRPA times ballpark factor times total plate appearances. Convert runs added or subtracted to Wins Added (10 runs = 1 win).

FIELDING WINS: Uses zone rating. Calculate number of plays made above or below league average for that position. Convert the plays made or missed into runs made or missed. Convert runs into wins. Catchers necessitate a different method. Mine needs work. For the time being, I'm using expected assists versus actual assists.

PITCHING WINS: I calculate extrapolated runs per plate appearance (XRPA) for the batters each pitcher has faced. However, I'm using fullblown DIPS theory, which I know isn't exactly right. This is something that will have to be adjusted. What this means is that instead of actual singles, doubles and triples, I'm using league-average figures, adjusted for ballpark. Homers are also adjusted for ballpark. After figuring XRPA for each pitcher, I compare it to the league average and multiply by total batters faced. This yields runs saved above or below average. Runs are converted to wins.

WINS ADDED: The sum of the other three categories.


Best way to answer that is to say that I think it will work, once I perfect the way I manipulate my underlying data.

At this point, I'd say the system is about 85% of the way there. For example, the sum of the Royals' Wins Added is -13.81. Based on their runs scored and runs allowed totals, you'd expect it to be -12.18. Other teams have a bigger discrepancy. Some have less. There is better correlation with a team's Pythagorean record than with actual record. This is not surprising.

Drop me a line at with suggestions & criticisms. I welcome the input.

The complete table of first-half Wins Added is here.


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