Playcore by BreederDAO
About
At BreederDAO, I led the strategic redesign of the Playcore platform to address a common challenge across Web3 gaming: helping players navigate overwhelming data and complex in-game economies. This initiative was a top-priority business goal, requiring direct collaboration and alignment from leadership, including the CEO and CTO, to ensure the vision met strategic objectives.
To execute this, Playcore became an integrated hub, unifying essential tools for major Web3 titles into an intuitive experience. This required cross-functional collaboration most notably with the Data Team to transform  on-chain data into accurate, user-friendly forecasts and analytics.
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The refreshed platform featured the following tools:
  • Crypto Unicorns: A breeding simulator that turned genetic probability into strategic planning
  • Axie Infinity: Advanced search and metrics tools that helped users discover the META (Most Effective Tactic Available)
  • DigiDaigaku: A trait analyzer that brought transparency to NFT valuation and collection management
Note: BreederDAO has now rebranded to Sovrun
TOOLS
Figma, Figjam
TIMELINE
2022-2024
MY ROLE
UX/UI Designer
Crypto Unicorn Breeding Tool
About the Game
Crypto Unicorns is a Web3 farming and battle game where the core assets are unique, NFT-based unicorns. In the game's economy, breeding is a fundamental mechanic. It's the primary way players create new assets, increase rarity, and drive strategy.
The Challenge
Breeding in Crypto Unicorns was a high-risk gamble. Players had to spend valuable resources without knowing the odds of getting a desirable unicorn. Our challenge was to be the first breeding simulator of its kind and give players the data they needed to make confident decisions.
The Solution
I designed and built an intuitive Breeding Simulator that players of all levels could benefit from. A user starts by selecting two parent unicorns from their collection, then the tool instantly runs hundreds of genetic simulations based on the game's official rules. It presents a comprehensive forecast, showing the probable classes, potential body parts with their inheritance odds, and the projected stat ranges of the resulting offspring.
Key Decisions
Card Style
The tool features two panels for selecting parent unicorns. I used a strict visual hierarchy (Asset Image, Name, Class, Breeding Points) to allow for instant recognition.
Sectioned Results for Different Needs
Instead of a single data dump, I implemented a sectioned results panel. As the data showed everything from the Mid to Minor genes, adding sections was a key decision to manage cognitive load.
The Genes Display offers genetic deep-dives for advanced "min-maxing" players.
The Offspring Results shows high-level probabilities for users who just want the odds.
The Stats tab provides a simple, scannable performance forecast.
Axie Infinity Equipped Win Rate
About the Game
Axie Infinity is a blockchain-based game where players collect, breed, and battle creatures called Axies. Each Axie possesses six body parts: Eyes, Ears, Horn, Mouth, Back, Tail. These body parts directly determine its combat abilities through the cards it can use in battle. 
The Challenge
Players lacked concrete data to evaluate the actual performance of specific body parts in competitive play. Team building was largely based on trial and error rather than statistical evidence, leading to:
The Solution
The Equipped Body Part Search provides players with a definitive, data-backed method for evaluating Axie builds. It features two columns, one for the user's equipped body parts and their opponents'. Users can begin by selecting one or multiple parts for each body slot (like choosing several types of Eyes). The system then calculates and displays the aggregate win rate and total number of battles for that specific selection.
Key Decisions
Multi-selection
Users can select multiple parts per body slot (e.g., Eyes: Chubby + Topaz) to analyze groups of parts simultaneously. This allows for powerful, flexible queries. Players are no longer limited to analyzing one specific part.
Visual recognition through icons
Using the part icons bridges the gap between data and gameplay, allowing users to quickly and accurately identify parts without memorizing text-based names, reducing errors and speeding up analysis.
Win rate
The tool calculates and displays the win rate percentage and number of battles fought count for the selected part or combination. This provides a direct measure of effectiveness, allowing players to make objective comparisons and invest their resources in high-performance builds.
Axie Infinity Body Part Win Rates
The Challenge
While players understood that body parts were important, they had no efficient way to discover new, high-performing strategies.
The Solution
The Body Part Win Rate  was created to solve a fundamentally different problem than the previous tool. While the earlier tool was built for validation, answering "What is the win rate for this specific build I already have in mind?" this new tool was focused on discovery.
The interface starts with a core toggle, letting users choose between analyzing Single parts to identify top-tier components in isolation or Combinations to test the synergy of specific multi-part sets. This is a key evolution from the previous tool's fixed comparison model.
Users can then conduct analysis using granular, independent filters for each slot and apply a minimum for battles fought. This allows for open-ended exploration, a capability the previous tool lacked. The results are displayed in a sortable table, presenting a clear, ranked list of optimal parts or combinations based on the user's criteria.
Key Decisions
Combination and Single Tab
This toggle serves two distinct user strategies. Single Mode lets users find the best individual parts (e.g., "What's the best Horn?". Combination Mode analyzes specific multi-part sets (e.g., "How well do this Horn and this Tail work together?")
Table Display
The results are displayed in a clear, sortable table that includes all filtered metrics, making it easy to scan and compare the performance of different part setups.
Digidaigaku Trait Distribution
About the Game
DigiDaigaku is a collection of NFT-based anime characters where each character's value is determined by its unique combination of visual traits like Background, Clothing, and Hairstyle. In this ecosystem, rarity directly dictates market price, making scarcity the most important factor for collectors and investors.
The Challenge
There was no single, reliable source where collectors could see the complete trait distribution for the entire DigiDaigaku collection.
The Solution
I designed the DigiDaigaku Trait Distribution Analyzer to bring  clarity and strategic insight to the NFT collection. Apart from being the first reliable source, the tool was built to answer the question : "How rare is my asset?" It achieves this by analyzing and visualizing the entire collection's trait distribution, allowing users to:
Retrospective
Leading the Playcore project was a significant challenge, especially as I was new to the complexities of Web3 gaming data. My path forward was driven by relentless curiosity, I immersed myself in learning about Web3 fundamentals and constantly deconstructed how game economies functioned to understand why the data was structured the way it was. This deep dive was essential to translate raw metrics into actionable user insights.
Furthermore, I consciously stepped beyond my role as a designer to adopt a business-oriented perspective. I made an effort to understand the company's strategic goals, which allowed me to advocate for user-centric design by directly linking it to business outcomes like user retention and platform credibility. This was put into practice when I had meetings with leadership, confidently standing by design decisions that balanced user needs with our business objectives.
My other works:
MAP Mima: Lingering Curse ABC: Discuss2032 Brisbane Olympics