From Idea to 1M+ Users Designing a Subscription-Based Fantasy Prediction Platform
Summary
11Wizards is a fantasy cricket prediction platform that simplifies decision-making for Indian fantasy players. As Founding Product Designer, I led product definition, designed the freemium-to-subscription model, and shipped 0 → 1. The product scaled to 1M+ users with subscription plans optimized through high-intensity IPL & Worldcup seasons.
Detailed Case Study
This page presents a high-level overview of the project. For a deeper breakdown of the research, product strategy, and design process, you can explore the full case study below.
Overview
Market: Fantasy Cricket Prediction | India
Users: 1M+
Platform: iOS & Android
Monetisation: Freemium → Subscription
Role: Founding Product Designer
As the founding product designer, I owned the end-to-end product experience and partnered closely with product and engineering to ship and scale the platform.
The Opportunity
A Rapidly Growing but Fragmented Ecosystem
The Gap:
- Fantasy platforms optimized for contests not decision intelligence
- Users relied on Telegram groups and YouTube tipsters for guidance/teams
- No unified layer for predictions, contest comparison, and live tracking
The Insight
Build a prediction intelligence layer sitting above the ecosystem empowering users with clarity, confidence, and competitive edge without competing directly with Dream11 or other fantasy sport apps.
Strategic Positioning & Product Vision
Reframing the Category
We positioned 11Wizards as a prediction and intelligence layer - not a contest platform. This strategic shift avoided direct competition while empowering users across all platforms.
Core Product Vision
Reduce decision anxiety and increase winning confidence through structured, data-backed predictions.
Aggregation Layer
A centralised place to view matches and contests across platforms.
Prediction Engine
Automated team generation powered by performance insights and probability logic.
Live Validation Layer
Real-time match commentary and stats to validate decisions during live games.
Core Experience Decisions
Match & Contest Intelligence
Problem
Fantasy players often participate across multiple platforms, making it difficult to compare contest structures and prize pools.
Design Approach
We introduced a match intelligence layer that aggregates contest information and helps users make strategic participation decisions.
Key Capabilities:
• Compare prize pools and entry fees across contests
• Identify high-value contests quickly
• Understand match context before building teams
Outcome:
Users can select contests more strategically before creating teams.
Player Research & Prediction
Problem
Fantasy players must analyse multiple statistics such as recent form, match conditions, and player roles before selecting their teams.
Design Approach
We designed a player research layer that surfaces key performance insights and predictive signals. This helps users evaluate players without manually interpreting complex statistics.
Key Capabilities:
• Recent player performance trends
• Match context insights (pitch, opponent, venue)
• Prediction indicators based on statistical patterns
Outcome:
Users can evaluate players faster and with greater clarity before building their teams.
Lineup Wizard (Core Differentiator)
Problem
Building a fantasy team requires analysing multiple player statistics under time pressure.
Design Approach
We introduced a Lineup Wizard, a guided flow that helps users build optimised teams step-by-step.
The wizard simplifies decision-making by:
• Recommending players based on performance insights
• Assisting with team balance while keeping user control
• Reducing the need for manual statistical analysis
Outcome:
Users can create competitive teams faster and with greater confidence.
Designing for Perceived Performance
Generating 20 teams required 10-15 seconds. In a time-sensitive environment, waiting without feedback creates anxiety and abandonment risk.
Instead of a static loader, we designed an animated sequence showing team combinations being built, intelligence processing, and progress. This reframed waiting time from "delay" to "value creation."
Cross-Platform Team Export
Problem
Fantasy players must manually recreate teams across platforms after analysis, creating friction when participating in contests.
Design Approach
11Wizards integrates with fantasy platforms via APIs, allowing users to link their accounts using OTP verification and export teams directly to partner apps.
Key Capabilities:
• One-time platform linking using OTP verification
• Secure API integration with fantasy platforms
• One-click export of generated teams
• Automatic redirection to the contest page on the partner platform
Outcome:
Users can export teams instantly and join contests in one click, reducing friction between lineup creation and contest participation.
Live & Trust Layer
Problem
Fantasy players switch between multiple apps to track live match updates and player performance, making it difficult to understand how their selected team is performing in real time.
Design Approach
We introduced a live match layer that combines match commentary, player performance tracking, and prediction validation in one place.
This helps users monitor their decisions and build trust in the platform’s insights.
Key Capabilities:
• Real-time match commentary
• Live player performance tracking
Outcome:
Users can track their team's performance and understand how predictions are performing during live matches.
Pro Layer & Monetisation System
Problem
Fantasy players rely on prediction insights, but most platforms lack advanced tools that provide deeper analysis & strategic decision support.
Design Approach
We introduced a Pro Layer that unlocks advanced insights, predictive tools, & deeper analytics through a subscription model. This allows casual users to explore the platform while power users access enhanced decision intelligence.
Key Capabilities:
• Advanced player prediction insights
• Premium lineup recommendations
• Deep match analysis and strategy tools
Outcome:
The Pro Layer enables sustainable monetisation while delivering higher value to serious fantasy players.
Trust & Education
The Problem
Fantasy users are outcome-driven. When they win, they trust the product. When they lose, they question it.
The Intervention
Introduced short-form learning content and dedicated learning centre.
Strategic Shift
Strategic shift from 'prediction tool' to 'performance guidance platform'
Product design extended beyond UI - into expectation management, education, and trust-building systems.
Impact
1M+
Users in India
4.2
Play Store rating
Seasonal adoption
During IPL & World Cup
Subscription
Validated during peak cycles
Built a scalable prediction system operating in a volatile, outcome-driven market.
Reflection
Designing for Behaviour
In volatile environments, product design must extend beyond UI into expectation management and trust-building. The learning center was a strategic shift from prediction tool to performance guidance platform.
Monetisation Alignment
- Monetisation must align with user intensity cycles (IPL & Worldcup spikes)
- Premium features must feel like power tools, not artificial restrictions
- Conversion design is about perceived value, not aggressive gating
Systems & Collaboration
Transforming a 10-15 second delay into "intelligence in progress" improved perceived performance without compromising backend logic. Good product design doesn't fight constraints - it works intelligently within them.
“11Wizards reinforced my belief that strong product design lives at the intersection of business, behaviour, and systems-not just screens.”
Detailed Case Study
This page presents a high-level overview of the project. For a deeper breakdown of the research, product strategy, and design process, you can explore the full case study below.
Let's build thoughtful, scalable products.
Open to senior IC and design leadership roles where I can simplify complexity and ship impactful experiences.
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