DiamondEye

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  Streamlit App

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  Extracted Bat Swing Metrics

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  Player Details

Check our demo out at:

https://youtu.be/g23myiIVnBU

Inspiration

Advanced MLB analytics platform to provide deeper insights into home run plays using modern technology and data science.

What it does

• Retrieves home run play details
• Extracts player profiles
• Performs video analysis of home runs
• Calculates advanced bat swing metrics
• Generates interactive performance visualizations

How we built it

Tech stack:

• Streamlit web framework
• Python backend
• MLB Stats API
• Google Cloud Video Intelligence
• spaCy NLP
• Pandas data processing
• Machine learning for video analysis

Challenges

• Integrating multiple APIs
• Tracking bat movement
• Calculating precise swing metrics
• Handling video complexity
• Implementing robust error handling

Accomplishments

• Comprehensive home run analysis tool
• Advanced video processing
• Machine learning-powered object tracking
• User-friendly sports analytics interface

What we learned

• Video processing techniques
• API integration
• Machine learning in sports analytics
• Error handling strategies

What's next

• Expand player and season datasets
• Improve swing metric accuracy
• Add predictive performance analytics
• Develop coaching tools
• Enhance computer vision techniques

Built With

• google-video-intelligence
• mathematics
• mlb-stats-api
• numpy
• pandas
• physics
• python
• spacy
• streamlit