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Monica Abbott led all pitchers of the the National Pro Fastpitch (NPF) league with 17 wins during the 2017 season. Since Abbott's team -- the Scrap Yard Dawgs -- only won 31 games that year, Abbott was apparently worth 55% of her team's wins.
Of course, that can't be right. Although people in softball (and baseball) routinely assign team wins to individual pitchers, it is incorrect to think a pitcher is the sole reason why a team wins (or loses). So that leaves us with a question.
How many wins do pitchers in softball really produce? Similar to what has been done for hitters in softball, this question can be answered by looking at the box score statistics.
The process for pitchers begins with the concept of Fielding Independent Pitching Earned Run Average (FIP ERA). As detailed in Sports Economics, Earned Run Average (ERA) is not the best measure of a pitcher's performance. Because earned runs depend on the fielders around the pitcher, a pitcher's ERA is not simply about the pitcher's performance. The better (or worse) the fielders around the pitcher perform the better (or worse) will be a pitcher's ERA.
The problem with ERA was originally noted by Voros McCracken. And McCracken also devised a better approach. This approach -- as detailed in Chapter Eight of Sports Economics -- was adapted to data from the NPF. The specific model involves regressing a pitcher's ERA on defensive independent statistics -- specifically strike outs, walks, hit-by-pitch, and home runs -- and one defensive dependent measure. This last measure was Hits per Ball in Play (HperBIP), which is calculated as follows:
HperBIP = [Hits – Home Runs]/[Outs + Hits – Strike Outs – Home Runs]
The data employed to estimate this model included every pitcher in the NPF from 2004 to 2018 who had at least 50 innings pitched in a single season. In all, there were 221 pitcher observations. The results -- reported in the table below -- indicate that 89% of the variation in pitcher's ERA is explained by these factors.
To construct the FIP ERA, we simply multiply each pitcher's defensive independent statistic by the corresponding coefficient. We then use the average value for HperBIP in the aforementioned player sample to construct each player's FIP ERA.
The following example for Monica Abbot's 2017 season illustrates the process. In column (2) we have the value of each statistics for Monica Abbott in 2017 and in the third column are the coefficients from the aforementioned model. Multiplying these two columns -- and then summing these values -- gives us Abbott's FIP ERA. As one can see, Abbott's FIP ERA was 1.47; or a bit worse than her actual ERA of 1.17.
Of course, the objective is to measure Abbott's production of wins. To do this we follow the following steps:
Each of those steps were applied to all pitchers in the NPF from 2004 to 2018. What follows are the top 20 pitchers -- in Wins Produced -- from 2004 to 2018.
As one can see, Monica Abbott's 2017 performance ranks 3rd all-time in NPF history. She is also listed five more times in the list of top 20 pitchers. She never did quite as well as Christa Williams in 2005 and Amanda Scott in 2004. But Wins Produced suggests Abbott is likely the greatest pitcher in NPF history.
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