Michael Pilger · Sports Analytics & Data Science
I build quantitative tools for the sports questions I care about.
My work sits at the intersection of sports, statistics, and software—from MLB player valuation and league analytics to athlete-performance data and applied machine learning. I am most interested in models that are testable, interpretable, and useful outside a notebook.
About
Biographical note forthcoming. Academic background, professional experience, research interests, and contact information are provided below.
Biography
- Education
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University of Notre Dame
M.S., Data Science · 2025-May 2027 University of Notre Dame
B.S., Computational Math & Statistics; Economics · 2021-2025 - Experience
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Varsity Sports Performance Analyst
University of Notre Dame · Sep 2023-Present AI Training & Evaluation Contractor
Mercor / Handshake · Apr 2025-Present Data Science Assistant & Intern
Danu Analítica · 2026 Sports Management & Data Analytics Assistant
VOLO Sports · 2023-2025 - Research interests
- Sports analytics, player and team valuation, forecasting and simulation, statistical modeling, machine learning, computer vision, and data-product development.
Research and analytical projects
Projects range from original sports research to production data systems and applied machine-learning work.
Window-Adjusted WAR (wWAR)
A prospective MLB player-value framework that holds a player's projected WAR fixed and estimates how efficiently those wins convert into championships for each franchise over a five-year competitive window.
ND Wiffleball Analytics & Operations
A production league platform combining standings, schedules, player statistics, projections, awards, interactive tools, commissioner workflows, captain scheduling, and a persistent historical database.
GeoGuessr Continent Classification
A transfer-learning image-classification project using a pretrained ResNet18 to infer continent from street-level geolocation imagery across five classes.
Applied Mathematical Algorithms
Python implementations spanning sorting and selection, maximum-probability paths in Markov chains, and constrained shortest-path graph search.
Latent-Variable & Nonlinear Modeling
Applied analyses using factor models, structural equation modeling, generalized additive models, diagnostics, and reproducible statistical reporting.