Real-Time Fantasy Cricket Platform
High-performance fantasy gaming platform with AI-powered team recommendations and real-time scoring for millions of concurrent users
Overview
BCCI wanted to deepen fan engagement beyond passive TV viewing by creating an official fantasy gaming experience for IPL. We built a platform handling 1M+ active users with sub-second score updates, AI-powered team suggestions for casual fans, and social features that drove viral adoption.
The Problem
Cricket fans in India are deeply passionate but increasingly distracted by competing entertainment options. BCCI needed a digital experience that would keep fans engaged throughout the 2-month IPL season, not just during match hours. Existing fantasy platforms had poor UX, slow updates, and no assistance for casual fans who didn't know player statistics.
Key Challenges
Extreme Scale Requirements
IPL attracts 500M+ viewers. The platform needed to handle 100K+ concurrent users during peak moments (final overs, wickets) with sub-second response times. Traditional architectures would collapse under this load.
Real-Time Scoring Complexity
Fantasy points depend on live match events (runs, wickets, catches) that happen in rapid succession. Scores need to update within 1-2 seconds of the actual event. Any lag destroys the live engagement experience.
Casual Fan Barrier
70% of potential users don't follow player statistics closely enough to build competitive teams. Without assistance, they'd create poor teams, lose immediately, and churn. Expert-only platforms have limited market size.
Viral Growth Mechanics
Fantasy gaming is more fun with friends. The platform needed built-in social mechanics (leagues, challenges, sharing) to drive organic growth without expensive user acquisition.
How We Built It
Scalable Architecture Design
Designed event-driven architecture using WebSockets for real-time updates. Implemented aggressive caching with Redis clusters. Built auto-scaling infrastructure on AWS that could handle 10x normal traffic during match peaks.
Real-Time Scoring Engine
Integrated with official match data feeds with <500ms latency. Built complex scoring rules engine supporting multiple point systems. Implemented optimistic UI updates with server reconciliation for perceived instant response.
AI Team Recommendations
Developed ML models analyzing player form, historical performance, pitch conditions, and opponent matchups. Created tiered suggestions from "safe picks" to "differential picks" based on user risk preference. Added natural language explanations for each recommendation.
Social & Gamification Features
Built private leagues with invite codes for friend groups. Implemented leaderboards, achievements, and streaks. Added mini-games and predictions to engage users between matches. Created shareable team cards for social media.
Solution Highlights
Sub-Second Live Updates
WebSocket-based architecture delivers score updates within 1 second of match events. Optimistic UI shows changes instantly while syncing with server. Zero perceived lag even during peak traffic.
AI Team Builder
Machine learning models suggest optimal team compositions based on player form, pitch conditions, and historical matchup data. Explanations help casual fans understand why picks are recommended.
Social Leagues
Private leagues with friends, office challenges, and public competitions. Leaderboards update in real-time during matches. Shareable team cards drive organic social media exposure.
Gamification Engine
Daily challenges, prediction games, streaks, and achievements keep users engaged even on non-match days. Tiered rewards system encourages consistent participation throughout the season.
Technical Deep Dive
The platform uses a CQRS (Command Query Responsibility Segregation) pattern separating read-heavy score lookups from write operations. Score updates flow through Apache Kafka, processed by Node.js workers, and pushed to clients via Socket.io with Redis adapter for horizontal scaling. The AI recommendation engine uses a gradient boosting model trained on 5 years of IPL data (50,000+ player-match records) with features including recent form metrics, venue-specific performance, bowling matchup statistics, and team composition balance scores. Inference runs on AWS Lambda for cost-effective scaling, with recommendations cached by match and updated hourly.
AI Capabilities
Player Performance Prediction
ML models forecasting expected fantasy points per player
Team Composition Optimization
Suggesting balanced teams within salary cap constraints
Risk-Adjusted Recommendations
Tiered picks from safe to differential based on user preference
Natural Language Explanations
Human-readable reasoning for each AI recommendation
Churn Prediction
Identifying disengaging users for re-engagement campaigns
Technology Stack
Backend
Real-Time
Mobile
Machine Learning
Infrastructure
Results & Outcomes
1M+
Active users
Peak daily active users during IPL season
10x
Engagement multiplier
Session time during live matches vs non-match periods
100K+
Concurrent users
Peak simultaneous connections during match finals
72%
User retention
Week-over-week retention throughout 8-week season
<1s
Score update latency
From match event to user screen
99.9%
Uptime
Zero downtime during peak match hours
Services Used for the Client Product
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