AI-Powered Sports Analytics Platform for Fanalyze
The Problem
Fantasy sports and sports analytics platforms were drowning in data but starving for insight. Fans, analysts, and fantasy league players had access to raw statistics — but turning those numbers into actionable predictions required hours of manual research, spreadsheet wrangling, and cross-referencing multiple sources.
Fanalyze came to us with a vision: an AI-powered chatbot that could answer natural language questions about NFL players and games using live data — and respond with not just text, but charts, tables, and predictions.
The system needed to pull from external APIs updated every few hours and respond to queries using real-time data — not stale training data from a base model.
Context
Fanalyze operated in the sports technology space where speed and accuracy are everything. Their target users — fantasy league players and sports analysts — needed trustworthy, up-to-date information they could act on immediately.
The challenge wasn't just building an AI chatbot. It was building one that could reason over live data, generate visual outputs, and handle the nuance of sports-specific queries ("Compare Mahomes vs. Allen this season in away games").
Approach
Data Infrastructure
We built a robust data pipeline that connected to external sports APIs, pulling player stats, game schedules, team trends, and historical performance data. The system was designed to refresh every few hours, ensuring users could trust the data without hesitation.
We chose Supabase as the database layer for its real-time capabilities and built the ingestion pipeline as serverless functions that ran on a schedule.
RAG & AI Engine
The core of the platform was a retrieval-augmented generation system built on OpenAI's API. Rather than relying on the LLM's training data, every response was grounded in Fanalyze's proprietary database of live statistics.
We implemented:
- Structured retrieval over player stats, game logs, and season trends
- Context-aware query parsing that understood sports-specific language
- A prediction engine fine-tuned on large historical datasets
The prediction engine required careful fine-tuning — sports data is highly contextual and a naive model would produce confidently wrong answers. We built evaluation pipelines to catch and correct hallucinations before they reached users.
Data Visualization
One of the most technically challenging features: the AI could generate charts and data visualizations dynamically based on its response. We achieved this through extensive LLM fine-tuning on open-source visualization libraries, giving the model the ability to produce charts for any kind of data — team trends, season stats, player comparisons.
Frontend & Launch
Built the user-facing application with React and Chakra UI, backed by a Node.js API layer. The interface featured player search, position filtering, real-time stat tables, Twitter feed integration, and the AI chat interface front and center.
Architecture
The tech stack was chosen for speed and real-time capability:
- Frontend: React with Chakra UI for rapid, accessible UI development
- Backend: Node.js serverless functions handling API orchestration
- Database: Supabase for real-time data sync and structured storage
- AI Layer: OpenAI API with RAG retrieval over Supabase, custom fine-tuned models for predictions and visualization
- Data Pipeline: Scheduled serverless jobs pulling from external sports APIs
Results
Fanalyze launched in beta and saw immediate traction. According to initial user feedback, they observed almost 70% improvement in their decision-making process. Users were analyzing player performance in minutes instead of hours, and the data visualization capabilities helped them spot trends that were previously invisible in raw stat tables.
Next Steps
We continue to work with Fanalyze, iterating based on user feedback and expanding the platform's capabilities. The AI models are continuously improving as they ingest more data, and the team is exploring expansion beyond NFL into additional sports leagues.