"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.
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Updated
Feb 22, 2026 - Python
"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.
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
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.
an end‑to‑end machine‑learning application that recommends the most appropriate reliever to bring into a game given the current game state
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.
Calibrated probability models for MLB game and prop markets
Analyzing MLB umpire strike-zone consistency with Statcast pitch-level data (2015–2025): called-strike models, zone maps by count, and umpire/catcher leaderboards.
Using the Pybaseball-database to create a Points based ranking dashboard
MLB HR & game winner prediction models built on Statcast data. XGBoost/LightGBM with barrel rate, exit velocity, wind, platoon splits, and pitcher matchup features. Daily pipeline with betting edge calculation. AUC: 0.620.
The complementary code to an analysis of hitting and pitching in the Seattle Mariner's T-Mobile Park.
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