The NWSL deserves cutting-edge fan engagement tech.
NWSL-LFG is a backend AI-powered personalization platform that helps National Women’s Soccer League (NWSL) fans engage more deeply with content, find matches to attend, and buy merch they love — driven by real-time behavioral analytics, recommendation systems, and generative AI.
A project focused on building real-time analytics pipelines and intelligent recommendation systems — with a Phase 2 plan for full frontend integration and deployment.
- 📊 Tracks user behavior across content, merch, and match engagement
- 🎯 Recommends games, articles, and products based on user interests
- 🧠 Uses GPT to generate personalized blurbs and ticket invites
- Python — Primary language
- SQLite or PostgreSQL — Relational database for user and match data
- NumPy or Scikit-learn — Clustering and content-based recommendation models
- OpenAI GPT API — Dynamic content generation
- ASA API (itscalledsoccer) — Live NWSL match and team data ingestion
- Flask or FastAPI — Backend API framework
| Feature | Status |
|---|---|
| Data ingestion from ASA API | ⏳ In Progress |
| User behavior tracking API | ⏳ Not started |
| Recommendation engine (MVP) | ⏳ Not started |
| GPT integration for content generation | ⏳ Not started |
| Backend MVP launch | ⏳ Not started |
| Frontend UI | 🔜 Phase 2 |
| Full launch | 🔜 Phase 2 |
- Helps fans feel seen with personalized content and smart recommendations
- Drives ticket and merch sales through intelligent user engagement
- Showcases how LLMs (like GPT) and ML techniques can elevate sports marketing in a responsible, data-driven way
- Frontend UI
- Backend + Frontend Deployment
- Real-time behavioral analytics dashboards
- A/B testing for recommendation strategies
I'm Erica Rios — an entrepreneurial product builder passionate about bridging strategy, execution, and AI innovation.
I'm currently building projects that combine real-time data, AI techniques, and user-centered design to deliver the next generation of intelligent products.
Whether you're an NWSL fan, an engineer, or a recruiter — I'd love your thoughts, feature ideas, or code reviews