Harrison Rubin
Harrison
Rubin
Data Analyst at Prep Baseball
MS Data Science, Vanderbilt University
Harrison Rubin
Data Analyst at Prep Baseball
MS Data Science, Vanderbilt University
Harrison Rubin is a Data Analyst for Prep Baseball and a student in Vanderbilt University's Master of Science in Data Science program, with anticipated graduation in Spring 2027. He holds a Bachelor of Arts in Economics from Northwestern University and brings six years of experience across Baseball Operations and Analytics departments in professional and collegiate baseball, including stints with the Arizona Diamondbacks, Vanderbilt Athletics, the MLB Commissioner's Office, Harvard University Baseball, and the Cape Cod Baseball League.
His technical toolkit includes R, Python, and SQL, alongside baseball-specific technologies such as TruMedia, Synergy, and eBIS. He focuses on building tools and models that translate raw data into actionable insights — from player evaluation systems to machine learning models used in game preparation and roster decisions.
Master of Science in Data Science
August 2025–May 2027 (Anticipated)
Machine Learning · Data Management Systems · Probability & Statistical Inference · Exploratory Data Analysis · Principles of Programming & Simulation
Bachelor of Arts in Economics
Minors in Data Science & Business Institutions
September 2019–March 2023
Data Science with R · Data Visualization · Applied Econometrics · Labor Economics · Political Economics · Database Management & Information Processing
Data Analyst
Analyzes performance data from amateur baseball events to define and refine player evaluation metrics. Collaborates with the Data Operations and Product teams to build internal and external tools, including a Bayesian hierarchical pitch classifier built on High School TrackMan data.
Analytics Intern — Roster & Finance
Delivered short and long-term data analysis for coaches and Athletic Department staff using R, with contributions to a men's basketball player projection model used to analyze transfer portal targets.
Associate, Baseball Research & Development
Built automated systems to grade players' Arm, Accuracy, and Speed via the TruMedia API. Completed a research project examining the relationship between force plate test metrics and on-field performance outcomes, and coordinated advance scouting preparation for the Major League coaching staff.
Director of Baseball Analytics
Led all analytics efforts for a Division I program — pitch grade modeling, slow-motion camera integration, and management of a seven-person undergraduate analyst team. Formed a partnership with the Harvard Undergraduate Sports Lab for biomechanics and physics research.
Head of Data Modeling
Developed machine learning models for pitch grading, xwOBA, and xWhiff, and built individualized hitting dashboards for position players and coaching staff. Assisted in roster management and player transaction decisions.
Baseball Operations Intern — On Field Strategy
Analyzed minor league stadium data on experimental rules including the ABS Challenge system and shift restrictions using R and SQL. Competed in a mock arbitration hearing on behalf of a Major League club.
Prep Baseball
A Bayesian hierarchical classifier that identifies pitch types from high school TrackMan data collected at Prep Baseball events nationwide. Brings pitch classification accuracy typically reserved for the professional level down to the amateur game.
Personal Project
A Shiny application that grades every MLB pitcher's arsenal on the 20-80 scouting scale across both stuff and location dimensions. Separate multilayer perceptron models are trained per pitch type on Statcast data, covering the 2024–2026 seasons.
January 2026
June 2025
February 2025
October 2024