AI-Powered Fantasy Football Prediction Platform Development
Onoja Okpe
Fantaform — AI-Powered Fantasy Football Prediction Platform
Role: Founder & Full-Stack Engineer
Timeline: 2-3 weeks (MVP → Public Launch)
Platform: Web (Next.js + Node.js)
Live URL: https://fpl-prediction.xyz
Overview
Fantaform is an AI-powered fantasy football platform that helps Fantasy Premier League (FPL) players make smarter decisions using real-time data, predictive analysis, and automated recommendations.
The goal was simple:
Remove guesswork from fantasy football and give users clear, data-backed recommendations they can trust.
Dashboard Screen
The Problem
Fantasy football players struggle with:
Information overload across multiple platforms
Conflicting advice from content creators
Difficulty interpreting raw statistics
Poor timing of transfers and captaincy decisions
Most existing tools are either:
Too complex for casual users
Too manual and spreadsheet-heavy
Or based on static, outdated data
There was a clear gap for a simple, AI-driven, real-time decision assistant.
Match Prediction
The Solution
Fantaform centralizes fantasy football decision-making into one intelligent platform that:
Accepts a user’s current FPL team
Analyzes live data and fixtures
Generates AI-powered recommendations
Explains why a decision is suggested
Instead of browsing multiple sites, users get clear recommendations in seconds.
Team Prediction
Key Features
1. Squad Analysis
Upload or manually input current FPL squad
Analyze player form, fixtures, and minutes played
Highlight weak spots in the team
2. AI Transfer Recommendations
Suggest optimal transfers based on:
Fixture difficulty
Player form & expected points
Budget constraints
Rank transfers by impact
3. Match & Player Predictions
Predict match outcomes
Forecast player performance
Include AFCON-related player availability insights
4. Simple, Fast UX
Minimal inputs
Clear call-to-actions
Mobile-friendly design
Afcon Group Stage
Match insight
Architecture Highlights
Modular services (Fixtures, Players, Predictions)
Stateless API design for scalability
Aggressive caching to reduce latency
Fault-tolerant scraping logic
Clear separation of data ingestion and AI reasoning
Challenges & How I Solved Them
1. Data Reliability
Problem: Live football data can be inconsistent or delayed.
Solution:
Implemented Redis caching with TTLs
Added fallback data sources
Graceful degradation in the UI
2. Performance at Scale
Problem: AI predictions are expensive and slow if done naively.
Solution:
Precompute popular queries
Cache prediction results
Run heavy analysis asynchronously
3. User Trust
Problem: Users won’t trust recommendations without context.
Solution:
Added clear explanations for every recommendation
Focused on transparency over “magic” AI
Results & Impact
🚀 Successfully launched MVP to the public
👥 Active users submitting teams daily
📈 Strong early engagement and repeat usage
💬 Positive feedback on clarity and speed
🧠 Users reported improved transfer decisions
Fantaform validated that AI-driven insights + clean UX significantly improves the fantasy football experience.
What This Project Demonstrates
End-to-end product ownership
Strong backend architecture skills
Real-world AI integration
Performance optimization at scale
Ability to ship, launch, and iterate fast
Final Notes
Fantaform is a real, production-grade product built from scratch not a demo.
It reflects how I approach engineering problems: practical, scalable, and user-focused.
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Posted Dec 24, 2025
AI-powered fantasy football platform that helps FPL players make smarter decisions with real-time data, predictive analysis, and automated recommendations.
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