Friday, July 28, 2006

The “Value Added Approach” to offense, first described by Gary Skoog in the 1987 Baseball Abstract, credits the batter (and debits the pitcher) with the difference in run potential caused by the plate appearance.

For instance, in the 1982 American League, there was an average 0.500 runs scored per inning. With one out and nobody on, the future potential dropped to 0.265 runs. So if Alfredo Griffin led off an inning with a ground ball out, he would receive the difference, or negative 0.235 runs.

In this 2005
study published on the Retrosheet research page, Tom Ruane thoroughly analyzes and computes value added runs for every player from 1960 to 2004. “Thoroughly” is probably not strong enough – Microsoft Word’s thesaurus also lists “methodically,” “carefully,” “systematically,” “painstakingly,” “meticulously,” “scrupulously,” “comprehensively,” and “exhaustively,” and it probably takes all of them added up to equal the study’s level of detail.

First, Tom generates run potential charts and linear weights values for each league and year. Then, he presents the raw numbers for batters, both season and career. Then, he adjusts for park. Then, he adjusts for position. Then, he carefully explains how it happens that Denis Menke figured to lead the National League in 1970. Then, he compares the method to linear weights, and shows which players varied the most in the two measures, either by hitting well or poorly in the clutch.

Then, he repeats all that for pitchers (except the Denis Menke part).

Tom argues that this method is probably not as good as Linear Weights in predicting future performance, because value added contains lots of luck (clutch hitting and situations faced) that do not repeat from year to year. I agree with Tom, and Value Added is not my favorite offensive stat for that reason.

The hidden surprise is that Tom gives us the 90 sets of full, year-to-year and league-to-league linear weights values (embedded partway through the essay), full park factor data based on the run potentials (linked to in the text), and finally the
90 run-potential tables (again via link). Even if the rest of the study was missing, the free data would be enough to ensure the awesomeness of Tom's research.