QuickCatch

Your Personalized Player Tracker & Performance Analyzer.

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Problem

Fans of niche sports like NHL often struggle to find relevant highlights, stats, and updates, leading to information overload.

Solution

QuickCatch curates personalized content based on user preferences, sending only the most relevant videos and performance analytics to save users time and effort.

Sales Points

  • Save Time, Stay Informed: With QuickCatch, you don’t need to watch hours of highlight videos. We deliver only the content you care about—personalized for your favorite NHL players.
  • AI-Driven Performance Insights: Our app uses AI to analyze the performance of your favorite athletes and teams, providing detailed insights on engagement and gameplay, saving you time while keeping you updated.
  • Stay Ahead with Instant Notifications: Get notified instantly via email about new video content, game results, and performance statistics tailored to your interests.

How we built it

  • Frontend: We used React with TypeScript to create an interactive and responsive UI. Tailwind CSS simplified styling, while React Hooks (useState, useEffect) managed state and data fetching. We embedded YouTube videos directly using HTML5 iframes.
  • Backend: Built with Node.js and Express.js, our server handles video searches and downloads through YouTube-DL/YT-DLP, processes video files using FFmpeg, and triggers Python scripts for deeper video analysis. nodemailer delivers personalized email reports to users.
  • AI Analysis: A Python script leverages the Perplexity AI API to analyze video frames, providing detailed player-performance insights.
  • Libraries & Tools: Key tools included youtube-dl-exec for downloading videos, child_process to integrate Python scripts into our Node server, dotenv for managing environment variables, lodash for utility functions, and PIL/Pillow for image processing within Python.

Challenges we ran into

  • Deploying issues due to complex backend dependencies which made it difficult to achieve a stable production environment within the tight hackathon timeframe
  • Designing player and team infographics.

What we learned

  • How to use different automation tools.
  • Integrating AI APIs and analyzing online video content.
  • Using Figma’s UI components to quickly build a prototype.

What's next for QuickCatch

  • Enhancing data visualization for better insights.
  • Customizing notification templates for a more personalized experience.
  • Expanding from NHL to other sports.

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