pybaseball

"I'd walk through hell in a gasoline suit to play baseball." - Pete Rose (Charlie Hustle). Python scripts to pull MLB Gameday and stats data, build models, predict outcomes, make plays.

Exploration of possible approaches to create a DFS projection system.

Predict what an MLB pitcher will throw next

Pybaseball Database: Using the Pybaseball Python Package to create a historical baseball database for any use.

V3.0 of my MLB Game Outcome Prediction Model

ML model predicting an MLB pitcher's next pitch type from game state and sequence history, comparing logistic regression, random forest, and HistGradientBoosting.

Simple pitch visualization tool that uses StatCast to generate charts for pitcher movement, velocity distribution, and pitch usage.

Grades MLB pitch quality from physics alone on 2.14M real Statcast pitches, then turns the model into a front-office decision tool: pitch-design recommendations, undervalued-arm detection, year-over-year trends, and similarity-based scouting comps. Live dashboard + API.

Early Data & Game Analytics Report — automated Mariners & Rainiers dashboard built with pybaseball, mlb-statsapi, and nightly GitHub Actions.

Reverse-engineered MLB The Show 26 rating formulas, with a Flask app that builds cards from real MLB stats. Any player, any season, 1897–2025.

A python application to view baseball statistics and if the player is available in the provided Fantrax league. DISCLAIMER: This a personal project for education and research.

Stuff+ pitch quality model — HistGradientBoosting on 700K+ Statcast pitches. SABR L4 capstone with Python & R implementations.

STATS 141XP Final Project (Identifying MLB Draft Biases)

Calibrated probability models for MLB game and prop markets

Full-stack sabermetrics dashboard — 13 interactive Plotly charts on 2024 FanGraphs data, built with pybaseball, pandas & Streamlit. Live app linked.

an end‑to‑end machine‑learning application that recommends the most appropriate reliever to bring into a game given the current game state

Daily MLB Statcast ingestion pipeline → Snowflake. Powers HR & win prediction models in mlb_py.

A novel baseball analytics project that measures how pivotal game moments (high WPA changes) impact player career trajectories. Uses Statcast data and machine learning to calculate MSS scores (-100 to +100) for both pitchers and batters, bridging statistical analysis with the human stories that define baseball careers.

Using the Pybaseball-database to create a Points based ranking dashboard

Predicting MLB pitcher injury risk from Statcast data using machine learning