A collection of data analysis and prediction models across multiple sports. These projects explore statistical modeling, machine learning, and sports analytics to predict outcomes, analyze team performance, and uncover insights from real-world sports data.
This repository contains personal sports analytics projects that combine my passion for sports with data science and machine learning. The work focuses on building predictive models, analytical tools, and exploring advanced statistics across multiple professional sports leagues.
sports-projects/
├── Baseball Projects/
├── Basketball Projects/
├── Football Projects/
└── Hockey Projects/
A sophisticated playoff prediction system using machine learning to forecast World Series odds and playoff series outcomes.
Key Features:
- Ordered logistic regression model trained on historical playoff data (2016-2024)
- Advanced metrics: wRC+, xwOBA, xFIP, SIERA, DRS, OAA
- Real-time World Series probability calculations
- Pythagorean expectation analysis
Tech Stack: Python, Pandas, NumPy, StatsModels, MLB Stats API
Projects coming soon - Planned work includes NBA playoff predictions, player performance analytics, and team salary cap analysis.
A comprehensive analysis and modeling project for the NFL's Big Data Bowl competition, focusing on strategic decision-making and situational analysis in professional football.
Key Features:
- Extensive exploratory data analysis (EDA) and feature engineering
- Situational pattern analysis (two-minute drill, red zone, fourth-down decision-making)
- Coach decision-making analysis and decision framework development
- Multiple specialized models for route completion and receiver separation
- Sideline decision suggestions and coaching translation guides
Key Analysis Areas:
- Two-minute drill strategy optimization
- Red zone efficiency and scoring probability
- Fourth-down decision frameworks
- QB-WR compatibility analysis
- Player characteristics and performance patterns
Tech Stack: Python, Jupyter Notebooks, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
Projects coming soon - Planned work includes NHL playoff predictions, team strength analysis, and player performance tracking.
- Build accurate predictive models across multiple sports
- Demonstrate proficiency in data science and machine learning
- Apply statistical concepts to real-world sports data
- Explore feature engineering and model validation techniques
- Create reproducible, production-quality code
- Showcase analytical and programming capabilities
- MLB Stats API: https://statsapi.mlb.com
- PyBaseball: https://github.com/jldbc/pybaseball
- Additional sports APIs as projects expand
Feel free to reach out with feedback or collaboration opportunities!