Baseball Prospect Predictor - Machine Learning
Inspiration
Combining traditional baseball scouting with modern machine learning to create more accurate prospect projections. FanGraphs' unique dataset of scouting grades and advanced statistics provided an opportunity to bridge old-school and new-school baseball analysis.
What it does
- Predicts MLB performance metrics (WAR, ERA, wRC+) for baseball prospects
- Interactive prospect ranking system with advanced filtering
- Visual scouting grade analysis through radar charts
- Comprehensive player pages with minor league statistics
- Position-specific performance projections
How we built it
- Data Processing
- Scraped FanGraphs prospect data
- Created training dataset by matching historical prospects with MLB outcomes
- Preprocessed scouting grades and statistics
- ML Implementation
- Trained models on Google Vertex AI using AutoML Regression
- Models predicts WAR
- Data is recombined with prospect data and organized into JSON for streamlined input to site
- Frontend Development
- React with Material UI components
- Recharts for data visualization
- Context API for state management
- Firebase for hosting
Challenges we ran into
- Matching historical prospect data with MLB outcomes
- Handling missing data and scouting grade variations
- Dealing with small training dataset, and prospects who haven't yet reached MLB
- Implementing responsive data grid with dynamic filtering
- Managing state across complex component hierarchy
Accomplishments that we're proud of
- Successfully trained ML models with strong predictive accuracy
- Created intuitive interface for complex baseball data
- Implemented interactive scouting visualizations
- Developed prospect filtering system
What we learned
- AutoML regression techniques
- React Context API implementation
- Google Cloud Infrastructure
What's next for Baseball Prospect Predictor - Machine Learning
- Real-time data updates
- Sequential Prospect Data Input
- Comparative player projections
- Historical accuracy tracking
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