MatchMind — amateur sports analysis on Ring

Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for MatchMind — amateur sports analysis on Ring

Inspiration

We noticed that amateur athletes often lack access to professional film study and tactical analysis that elite teams use. We wanted to bridge this gap by using the ubiquitous presence of Ring cameras to make sophisticated sports data analysis accessible to everyone.

What it does

MatchMind analyzes sports clips uploaded from Ring cameras to identify key plays and tactical errors. It uses advanced AI to track specific movements and provides players with a detailed report to help them improve their game performance over time.

How we built it

We built the backend using Python and AWS Lambda to handle video processing and metadata orchestration. We integrated LangGraph to manage the complex logic of sports analysis and used Amazon Bedrock to generate natural language insights based on the visual data.

Challenges we ran into

Processing video data in a serverless environment required significant optimization to ensure low latency. We also spent considerable time refining the LangGraph states to ensure the AI correctly identified different types of sports maneuvers without confusion.

Accomplishments that we're proud of

We successfully created a pipeline that transforms a raw Ring clip into a structured tactical breakdown automatically. Seeing the AI correctly interpret nuanced sports movements through Bedrock was a major milestone for our team.

What we learned

We learned a lot about orchestrating stateful agents with LangGraph for specialized domain analysis. We also gained deep insights into how to effectively structure AWS Lambda functions for video-heavy applications.

What's next for MatchMind

We plan to expand our analysis library to include more sports and real-time coaching feedback. We also hope to integrate a dashboard where teams can track their progress across multiple matches and sessions.

Built With

• amazon-bedrock
• amazon-ring
• aws-lambda
• langgraph
• matchmind
• sport