Baseball is the sport where the referee plays the biggest role in DFS outcomes. Understanding which umpires are a part of the better and worse pitching environments might be a narrow area for creating an edge, but one in which we felt warranted some research and tool development. The purpose of this app is to develop and understanding of the hierarchy of umpires and how they contribute to pitcher game environments through associated strikeout frequencies.
App User Guide
- Umpires input: In this input, select the umpires that you would like to view in the app output to the right. You can view all umpires by selecting "All", or only a select set of umpires by deleting all and selecting your desired umpires from the dropdown menu.
- Columns input: In this input, select the columns that you want to display in the output. The "Actual K" and "Actual K%" are the total number of strikeouts (called and swinging) and the strikeout rates (No. of Ks divided by No. of plate appearances) observed by each number. The "Pred K" and "Pred K%" are the predicted or "expected" raw total and strikeout rates observed by each umpired, controlling for the K% of the pitchers and batters whose plate appearances they observed. The "Net K%", which is the focal column of this app, is the difference in "Actual K% and "Pred K%" - that is, how much higher of a strikeout rate did an umpire observe than we expected them to observe based on the pitchers and batters whose plate appearances they observed.
- Data download button: Clicking this button will download a data file called "ASA MLB Umpire Net K%.csv". This file is a full dataset of the umpires and their Actual K, Pred K, PA, Actual K%, Pred K%, and Net K% metrics. The dataset isn't any different from what is viewable in the app output window, but the .csv file could be utilized in your own projection algorithm or outside research.
- Columns output: These are the columns that are specified in input (3). The table is defaulted to sort alphabetically by umpire, but you can sort on any column by clicking the arrows next to the column name.