2026-05-21T06:54:20Zhttps://keep.lib.asu.edu/oai/request

oai:keep.lib.asu.edu:node-2001012025-03-31T22:44:47Zoai_pmh:alloai_pmh:repo_items

200101 https://hdl.handle.net/2286/R.2.N.200101 http://rightsstatements.org/vocab/InC/1.0/ http://creativecommons.org/licenses/by-nc-sa/4.0 2025-05 122 pages Ingersoll, Alan Schneider, Laurence McIntosh, Daniel Barrett, The Honors College Computer Science and Engineering Program School of Mathematical and Statistical Sciences With the consistent increase in how hard pitchers are throwing in the MLB, Tommy John surgery has grown more common. Many players have had Tommy John surgery since the first one performed in 1974 on Tommy John, but cumulative research into how players perform before and after the surgery is minimal. This thesis analyzes pitcher performance before and after their Tommy John surgery, evaluating key elements significant in pitching, such as strikeouts, innings pitched, and walks. It explores the relationship of each key data point’s before and after calculation by combining a detailed pitching dataset with a Tommy John dataset. A cluster analysis is performed using a K-Means clustering algorithm, which results in 7 key groups of pitchers when considering their pre- and post-surgery performance. These groups are classified as follows: Heavy-Duty Starters High-Strikeout, Low-Volume Pitchers Inconsistent, Low-Volume Pitchers High-Strikeout, High-Walk, Low-Volume Pitchers Non-Improving Pitchers Inning-Count Flippers High-Strikeout, Low-Walk, High-Volume Pitchers The clustering algorithm used two principal components, derived from a principal component analysis that utilized 10 key variables. These variables were feature engineered using the pitching dataset and the Tommy John dataset. Baseball Pitchers Pitching Tommy John Clustering PCA Tableau Analysis of Pitcher Performance Before and After Tommy John Surgery