Event Title

The Effect of Pitch Usage on the Whiff Rates of Major League Baseball Pitchers

Advisor(s)

Christopher Jones, Chicago Cubs, IMSA Class of 2002

Location

Room A151

Start Date

26-4-2019 10:05 AM

End Date

26-4-2019 10:20 AM

Abstract

With the rise of the Sabermetrics era in modern Major League Baseball, the sport has shifted toward statistics and quantitative data science as a way to measure performance and predict future outcomes. Specifically, one area that has not been studied in depth is the relationship between pitch usage and whiff rates (analyzing how much different pitches are thrown compared to the success rate of pitchers). To compare the relationship for each pitch, we used data for all Major League starting pitchers from 2018. Using the statistical programming language R, a year-long average was found for each pitcher, and the changes in the usage rates for each month were compared to the changes in whiff rates. Currently, we have not found much evidence that there is a significant correlation, although all correlation values were positive. In further analysis, we studied horizontal movement, vertical movement, and velocity for each pitch as a way to analyze the quality of the pitch compared to pitch usage and whiff rate. The correlation values for changeups were the highest overall, but for the most part, there are no league-wide trends which were worth considering.

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Apr 26th, 10:05 AM Apr 26th, 10:20 AM

The Effect of Pitch Usage on the Whiff Rates of Major League Baseball Pitchers

Room A151

With the rise of the Sabermetrics era in modern Major League Baseball, the sport has shifted toward statistics and quantitative data science as a way to measure performance and predict future outcomes. Specifically, one area that has not been studied in depth is the relationship between pitch usage and whiff rates (analyzing how much different pitches are thrown compared to the success rate of pitchers). To compare the relationship for each pitch, we used data for all Major League starting pitchers from 2018. Using the statistical programming language R, a year-long average was found for each pitcher, and the changes in the usage rates for each month were compared to the changes in whiff rates. Currently, we have not found much evidence that there is a significant correlation, although all correlation values were positive. In further analysis, we studied horizontal movement, vertical movement, and velocity for each pitch as a way to analyze the quality of the pitch compared to pitch usage and whiff rate. The correlation values for changeups were the highest overall, but for the most part, there are no league-wide trends which were worth considering.