Document Type
Other
Publication Date
2025
Abstract
Clustering player statistics provides new perspectives on identifying elite Major League Baseball (MLB) players. This paper explores the use of k-means clustering combined with principal component analysis (PCA) to segment players based on various performance metrics such as batting average, on-base percentage, wins above replacement, and many others. The analysis examines over a century of MLB data, dating from 1900 to 2023, to uncover meaningful groups and highlight key differences between elite and non-elite players. Results demonstrate how modern statistical techniques can go beyond traditional metrics to provide insights into player performance. These findings have implications for player evaluation, team strategy, and advancing the use of data in sports analytics.
Publisher
South Dakota State University
Rights
Copyright © 2025 Tayven Badger
Recommended Citation
Badger, Tayven, "Capstone II Project : MLB" (2025). Schultz-Werth Award Papers. 54.
https://openprairie.sdstate.edu/schultz-werth/54
