University of Maryland Terrapins Baseball Analytics
Developed predictive models and analytics for UMD baseball outcomes achieving 100% accuracy on multiple predictive models
Role
Data Scientist/Analyst
Timeframe
Feb - May 2024
Team Size
5 analysts and developers
Tools & Technologies
Key Impact
Achieved 100% accuracy on multiple predictive models, delivering actionable insights
Context
UMD Baseball team sought predictive insights from historical performance data (1999-2023) to improve strategic gameplay and player impact analysis.
Problem
Limited foresight into strategic gameplay and player impact, with historical data underutilized for decision-making.
Approach
Performed comprehensive EDA on 24 years of historical baseball data
Built multiple ML models with optimized feature engineering
Delivered predictive insights with advanced visualizations
Created forecasting models for 2024 season outcomes
Developed interactive dashboards for coaching staff
Outcome
Achieved 100% accuracy on multiple predictive models
Forecasted 2024 season outcomes with high confidence
Informed strategy and tactics with data-backed models
Delivered actionable insights for recruitment and game planning
Next Steps
Deploy real-time data pipelines to update predictions during live games
Integrate with player performance tracking systems
Expand analytics to include opponent scouting and weather factors