CoachCam

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  Landing page of the project.

Inspiration

Many athletes and coaches rely on intuition or expensive equipment to analyze performance. While professional teams have access to advanced analytics, individual athletes are often left without actionable feedback. CoachCam was inspired by the idea that AI-powered performance analysis should be accessible to everyone, using just a camera and smart software.

What it does

CoachCam is an AI-powered sports analysis platform that transforms training videos into clear, actionable performance insights.

Users can:

Upload or analyze sports footage

Receive performance scores such as speed, accuracy, consistency, and technique

Track improvement over time through structured metrics

Get instant feedback without needing specialized hardware

The goal is to help athletes train smarter, not harder.

How we built it

CoachCam was built as a full-stack web application with a focus on performance, scalability, and user experience.

Tech Stack:

Frontend: Next.js 16, TypeScript, Tailwind CSS

Backend & Auth: Firebase Authentication (Email & Google)

Database: Firebase Firestore

State & Logic: React hooks and client-side validation

Design: Responsive UI

The application routes users dynamically:

Returning users go straight to the dashboard

New users are guided through onboarding

Performance data is stored in Firestore using structured documents with nested metrics.

Challenges we ran into

Some key challenges included:

Handling inconsistent data structures from Firestore

Preventing runtime errors when metrics were missing

Managing Firebase authentication errors and edge cases

Ensuring the UI remained responsive while data was loading

Designing a system flexible enough to support multiple sports

Each challenge helped improve both the stability and developer experience of the platform

Accomplishments that we're proud of

Built a fully functional full-stack application within hackathon constraints

Implemented secure authentication with Email & Google sign-in

Designed a scalable Firestore data model for performance analytics

Created a clean, responsive UI optimized for usability

Successfully handled edge cases and runtime errors in production

Delivered a project that combines AI potential, real-world impact, and strong UX

CoachCam represents a solid foundation for a real product, not just a demo.

What we learned

Throughout development, we learned:

How to design scalable Firestore data models

Handling authentication edge cases (new vs existing users)

Safely managing asynchronous data fetching in React

Preventing runtime errors caused by undefined or missing fields

Balancing UX design with technical constraints

This project strengthened our understanding of real-world full-stack architecture.

What's next for CoachCam

CoachCam is just getting started. Our next steps focus on making performance analysis more intelligent, accessible, and sport-specific.

Planned improvements include:

AI-powered pose estimation for more precise technique analysis

Sport-specific scoring models (e.g., tennis, football, athletics)

Real-time feedback during training sessions

Video timeline annotations to highlight key moments

Coach–athlete collaboration tools

Mobile-first experience and native app support

Long-term, we aim to make CoachCam a complete digital assistant for athletes and coaches at every level.

Built With

• firebase
• next.js
• tailwind
• turbopack
• typescript
• vercel