Real-Time Match Intelligence Dashboard
Just shipped a high-performance sports analytics dashboard designed for real-time match storytelling. Using the Liverpool vs. Manchester City match as a blueprint, I built a system that doesn't just display data—it interprets it.
The Engineering Challenge:
Handling high-frequency event streams while maintaining a "pixel-perfect" UI and low-latency AI responses.
The Solution:
Real-Time Data Pipeline: Architected a FastAPI backend using Server-Sent Events (SSE) to stream live ball tracking and event updates directly to a Next.js 14 frontend.
Agentic Commentary: Integrated GPT-4o-mini to act as a live tactical analyst, generating context-aware commentary based on pitch position and match intensity.
Data Reliability: Engineered a custom match processor for accurate zone calculations, live possession tracking, and score state management.
Production Standards: Fully Dockerized with a focus on accessibility, light/dark mode performance, and strict TypeScript typing.
This project sits at the intersection of Real-Time Systems, AI Integration, and UX Engineering.
115,000+ data points. Real-time AI commentary. Zero lag. 🏟️✨
Stack: Next.js 14, FastAPI, SSE, and OpenAI.
Key Features:
✅ Live Pitch Tracking: Real-time ball and player positioning.
✅ AI Tactical Analyst: GPT-powered commentary that reacts to the match as it happens.
✅ Dynamic Stats: Live possession, shots, and zone-dominance tracking.
✅ Enterprise Ready: Dockerized, Type-safe, and built for speed.
If you're looking for a High-Agency Engineer to build your next AI-native product or real-time dashboard, let’s talk.
Full project readme:
Project Link:
NextJS #FastAPI #AI #SportsTech #SoftwareEngineering #GenerativeAI #WebDevelopment
Demo below:
#nextjs #fastapi #ai #sportstech #softwareengineering #generativeai #webdevelopment | Malcolm Wiredu
Real-Time Match Intelligence Dashboard Just shipped a high-performance sports analytics dashboard designed for real-time match storytelling. Using the Liverpool vs. Manchester City match as a blueprint, I built a system that doesn't just display data—it interprets it. The Engineering Challenge: Handling high-frequency event streams while maintaining a "pixel-perfect" UI and low-latency AI responses. The Solution: Real-Time Data Pipeline: Architected a FastAPI backend using Server-Sent Events (SSE) to stream live ball tracking and event updates directly to a Next.js 14 frontend. Agentic Commentary: Integrated GPT-4o-mini to act as a live tactical analyst, generating context-aware commentary based on pitch position and match intensity. Data Reliability: Engineered a custom match processor for accurate zone calculations, live possession tracking, and score state management. Production Standards: Fully Dockerized with a focus on accessibility, light/dark mode performance, and strict TypeScript typing. This project sits at the intersection of Real-Time Systems, AI Integration, and UX Engineering. 115,000+ data points. Real-time AI commentary. Zero lag. 🏟️✨ Stack: Next.js 14, FastAPI, SSE, and OpenAI. Key Features: ✅ Live Pitch Tracking: Real-time ball and player positioning. ✅ AI Tactical Analyst: GPT-powered commentary that reacts to the match as it happens. ✅ Dynamic Stats: Live possession, shots, and zone-dominance tracking. ✅ Enterprise Ready: Dockerized, Type-safe, and built for speed. If you're looking for a High-Agency Engineer to build your next AI-native product or real-time dashboard, let’s talk. Full project readme: https://lnkd.in/efpWvyeR Project Link: https://lnkd.in/eBbukwb5 #NextJS #FastAPI #AI #SportsTech #SoftwareEngineering #GenerativeAI #WebDevelopment Demo below:
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