That's Big Time
A national sports discovery and media platform built around organizations, teams, schools, athletes, events, stories, media, search, and structured sports data.
The Problem
Sports discovery online is fragmented across scores, rosters, recruiting sites, and social posts. Fans, families, and organizations lack a single structured place where teams, athletes, events, and stories connect.
The Vision
A national platform where every sports entity — organization, team, school, athlete, event, story — is modeled, searchable, and interconnected through a knowledge graph, backed by editorial media and community.
My Role
Directed AI-assisted product development and translated business goals into product requirements, workflows, and implementation priorities. Owned product vision, backlog prioritization, entity modeling, UX iteration, and release planning.
Product Decisions
One consistent search component across all entity types with typed categories.
Standardized entity model connecting orgs, teams, schools, athletes, events, and media.
Structured import workflows for schools, rosters, and events to scale content.
Stories and media generate indexed pages that drive discovery and return visits.
Programmatic page structure with canonical URLs, JSON-LD, and clean sitemaps.
Editorial workflows for stories, media, and event coverage.
Product Process
Challenges
- Search inconsistency across entity types
- Unclear high school and roster imports
- Data normalization across ingested sources
- Information hierarchy on entity pages
- Balancing product depth with simplicity
Solutions
- Created one universal search component
- Introduced consistent search categories
- Added a dedicated high school import path
- Designed sports-market pages
- Standardized graph entities and relationships
- Improved SEO and sitemap structure
Outcomes
- Launched a live, working national platform
- Created a structured, scalable page architecture
- Standardized reusable UI and data components
- Built repeatable import and discovery workflows
- Generated indexed public pages across markets
Lessons Learned
- Model the entities before the screens.
- Search is the product, not a feature.
- Every import needs an operator, not just a script.
- Iterate on the smallest unit of clarity first.
What's Next — Continued Evolution
That's Big Time continues to evolve through AI-assisted product development and serves as the foundation for ongoing experimentation in search, discovery, product workflows, and scalable knowledge graph architecture.