Date of Award
2025
Document Type
Thesis
Degree Name
Bachelors
Department
Natural Sciences
First Advisor
Skripnikov, Andrey
Second Advisor
Loveland, Rohan
Area of Concentration
Computer Science
Abstract
This thesis examines the evolution of Major League Baseball (MLB) hitting strategies from 1950 to 2010 using statistical analysis and machine learning techniques. The study investigates changes in player performance metrics to determine how hitting styles have evolved, specifically focusing on distinguishing "power hitters" from "contact hitters." Principal Component Analysis “(PCA)”, t-distributed Stochastic Neighbor Embedding, and clustering methods, such as K Means Clustering, were applied to historical MLB data from Baseball Reference to reveal underlying trends and shifts in player roles over time. An interactive dashboard was developed utilizing Streamlit to visualize these trends dynamically. This was done by incorporating a year-by-year and decade display of stats, decade hitting trend comparison, contact hitting versus power, utilizing a PCA, and a player comparison allowing the user to look at players from 1950-2010 that fall under the contact designation or power designation and compare their stats to see what differentiated them.
Recommended Citation
McIntosh, John, "Analyzing the Evolution of Major League Baseball Hitting Strategies (1950–2010): A Machine Learning and Interactive Dashboard Approach" (2025). Theses & ETDs. 6696.
https://digitalcommons.ncf.edu/theses_etds/6696