Basket Buddy

Basket Buddy: Experience immersive solo game simulation with real-time shot prompts, score tracking, and AI-assisted detection for the ultimate training challenge.

Comment

Inspiration

Our inspiration sprang from our collective passion for basketball and a relentless drive to elevate our skills through innovative technology. We wanted to create a system that captures the immersive experience of playing basketball — simulating not just the physical activity, but also the mental and strategic aspects of the game. Inspired by Athletiq’s vision to bridge athletic performance and technology, we sought to build an engaging, high-tech platform that empowers players to train smarter and compete against a dynamic virtual opponent.

What it does

Basket Buddy is a cutting-edge web application that simulates a complete, real-time basketball game experience. It features seamless integration of text-to-speech and speech-to-text for dynamic voice prompts and commands, enabling users to interact naturally while playing. Utilizing OpenCV-based computer vision, the app tracks player movements and basketball shot attempts, laying a foundation for sophisticated motion analysis. The system includes user authentication and profile management via a backend database, ensuring personalized gameplay and persistent stat tracking. The app provides real-time scorekeeping, shot clock management, and simulates an AI opponent with difficulty-adjusted behaviors—delivering an immersive gamified basketball training environment accessible through any modern web browser.

How we built it

We developed Basket Buddy using React and TypeScript, crafting a responsive SPA with precise type safety and modular component architecture. The integration of computer vision was implemented through WebSocket communication with a FastAPI backend using OpenCV, enabling real-time basketball and hoop detection. We leveraged modern web APIs for audio synthesis and speech recognition to create intelligent voice interaction features. The backend features robust user management and statistics tracking with seamless connection to our frontend via RESTful APIs. Throughout the process, we iteratively refined our architecture with insights and assistance from AI tools like Perplexity to optimize implementation strategies and tackle complex integration challenges.

Challenges we ran into

Our project posed significant challenges due to the steep learning curve in several new domains simultaneously. Without prior experience in mobile app development, we navigated three different production approaches before successfully pivoting to a performant web application. Computer vision integration with live video streams was particularly complex, requiring extensive study and experimentation to achieve reliable basketball detection and tracking. Additionally, coordinating audio interaction across browser limitations demanded careful handling of speech synthesis and recognition APIs. Outside development obstacles such as a taxing 6-hour drive prior to our intensive build sessions added to the challenge but strengthened our resilience.

Accomplishments that we're proud of

We are proud to have delivered a fully functional MVP that embodies a sophisticated blend of real-time computer vision, AI-driven game simulation, and advanced user interaction. The seamless synchronization of basketball shot detection and dynamic voice prompts powered by Gemini integration is a major technical achievement. Our deep dive into integrating OpenCV with a FastAPI backend to process live video feeds represents a pioneering effort for our team. The personalized user profiles and persistent stats tracking elevate Basket Buddy beyond a simple game into a serious training and analytics platform.

What we learned

This project expanded our technical horizons far beyond what we imagined possible in a short timeframe. We gained profound hands-on experience in computer vision applications, modern React best practices, WebSocket communications, and sophisticated backend API development. Our understanding of real-time audio processing and speech APIs in browsers was enhanced dramatically. Moreover, we discovered our ability to overcome steep learning curves and effectively collaborate using AI-powered development tools as valuable resources. Basket Buddy proved our capacity to execute complex, multi-disciplinary projects successfully.

What's next for Basket Buddy

Next, we plan to implement advanced AI models to accurately determine if a basketball goes through the hoop — refining automatic shot make/miss detection to near professional levels. Our teammate is actively developing these machine learning enhancements. Simultaneously, we will expand the skeletal movement analysis on the camera feed to evaluate player form and verify if the user is performing the prompted moves correctly. This opens avenues for personalized coaching feedback and more interactive training sessions. Long term, we aim to enrich multiplayer capabilities, introduce a progression system for player levels, and embed video demonstrations to further enhance the learning experience—transforming Basket Buddy into the definitive smart basketball training companion.

Built With

Share this project:

Updates