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Pitcher Win Values Explained: Part Five
Posted By Dave Cameron On January 14, 2009 @ 5:26 pm In Daily Graphings | 28 Comments
We ended the last post in this series by talking about run environments. Generally, when you hear someone talk about a run environment, you think of either a specific park that has a notable influence on run scoring (say, Coors Field) or an era of baseball where the offensive level was significantly different than it is today (the dead ball era, for instance). In these environments, the game is a bit different, and runs can be either much more valuable or less valuable in helping win a game, depending on the context of the environment.
However, it’s not just in extreme parks or long ago where the run environment varies from the modern norms. Indeed, the run environment of current baseball varies from day to day depending on which pitcher is on the mound. CC Sabathia, through his dominance with the Indians and Brewers last year, created his own traveling low-run environment. When he took the mound, runs became hard to come by. Through his own abilities, Sabathia created a run environment in his own starts that wasn’t that close to the league average run environment of 2008.
This presents an issue. If we were to use the standard league average runs to wins conversion based on a normal run environment, we’d run into problems. By virtue of creating his own run environment, Sabathia has changed the context of the value of runs in relation to wins. All pitchers do this to an extent, and the further away from league average they are, the more they influence their particular environment.
So, if we know that pitchers are changing the relationship between runs and wins in their starts, then we need a dynamic runs to win estimator that adjusts for their individual run environment. This is where the always awesome Tom Tango comes to the rescue, as usual. In talking with him about this, he suggested that we use the formula ((League RA + Pitcher’s RA)/2)+2)*1.5, which would handle the runs to wins conversion in differing environments.
Essentially, that formula averages the pitcher’s FIP scaled to RA with the league average RA, then adds in the constants to create the run to win conversion for a given environment. If we looked at an average pitcher in the AL, for instance, the formula would give us (4.78 + 4.78)/2, which is of course 4.78. (4.78 + 2)*1.5 gives us 10.17, which is the average runs to wins conversion for the AL in 2008.
Now, if we had a pitcher whose park adjusted FIP scaled to RA was 3.00, then the environment would be 3.89 RA, and the runs to wins conversion would be 8.84. As you can see, an excellent pitcher significantly lowers the amount of runs it takes to equal a win. Doing the basic “divide runs by 10″ thing shortchanges good pitchers and overvalues bad pitchers.
So, we’ve built this dynamic runs to wins conversion tool into the win values you see on the page. I know, this is a level of detail that most of you won’t care about and can understandably be tough to wrap your head around, but as we said, we want these win values to be transparent, and this calculation is required if you’re trying to re-engineer the values on the site.
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