Athlete Insights: AI-Powered Solutions
PRODUCT DISCOVERY CASE STUDY
🔹 What it is: A conceptual AI-powered sports analytics platform using machine learning & computer vision.
🔹 What I did: Led product discovery using market research, competitive analysis, and feasibility studies.
🔹 Outcome: Identified a key market gap for automated performance insights and outlined a validated business case.
1. Identifying the Problem: A Time-Consuming, Manual Process
Approach
To validate the problem, I conducted user validation, competitive analysis, and applied the Jobs to Be Done (JTBD) framework to define key pain points and opportunities.
User Pain Points
90% of coaches use film analysis, but 70% lack time for in-depth review.
Manual breakdowns take 4+ hours per game, delaying real-time strategy.
Existing tools lack AI automation, requiring manual tagging & analysis.
Competitive Gaps
Category | Key Insights |
Functional | Automate film review, measure performance, generate reports. |
Emotional | Build confidence in preparation and player development. |
Social | Help athletes showcase skills; position coaches as strong recruiters. |
Financial | Reduce manual labor costs and improve ROI on scouting. |
Opportunity
Coaches & athletes need real-time, AI-powered insights, not just film.
An AI-driven tool could automate film review, reducing time by 80%.
2. Validating Feasibility & Aligning with Business Goals
Technology Feasibility
Researched computer vision models for tracking player movements & game events.
Benchmarked AI applications in fitness & gaming to validate real-world use cases.
Identified gaps in Hudl & Synergy, which lack automated play detection.
Market Demand
Strategic Fit
Opportunity
AI-driven film analysis can cut review time by 80%, making pro-level insights accessible at all levels.
3. Defining the MVP: AI-Powered Insights for Coaches & Athletes
Empathy-Driven Insights
To refine feature prioritization and UX, I applied the Empathy Map framework, analyzing how coaches, athletes, and recruiters engage with game film.
Category | Key Insights |
Think & Feel | Coaches worry about roster decisions & data reliability; athletes lack clear, actionable feedback. |
See | Reports exist but lack quantifiable performance insights; film uploads have no automated breakdowns. |
Hear | Coaches get team updates, but no automated performance validation; athletes hear praise but lack metrics. |
Say & Do | Coaches and athletes spend hours on manual film review, making real-time decision-making difficult. |
MVP Solution: Addressing Core Pain Points
By reducing manual review time and improving decision-making efficiency, I structured an MVP that delivers maximum impact with minimal complexity.
MVP Features
Automated Play Breakdown: AI detects & tags key game moments (shots, passes, defensive plays), reducing manual review time by 80%.
Player Performance Metrics: Tracks speed, agility, reaction time, and fatigue, offering quantifiable development insights.
Instant Game Recaps: Generates digestible summaries for faster strategy adjustments during and after games.
Cross-Functional Execution (Hypothetical Approach)
4. Roadmap & Prioritization
To drive adoption and scale efficiently, I structured the roadmap using the AARRR framework (Acquisition, Activation, Retention, Revenue, Referrals). This approach ensured clear prioritization, success metrics, and a validated go-to-market strategy.
Phase 1: Research & Validation (Acquisition & Activation)
Goal: Validate user demand, technical feasibility, and market fit.
Defined target users (coaches, athletes, recruiters).
Researched AI feasibility for real-time play breakdown and player tracking.
Benchmarked against Hudl, Synergy, and emerging AI sports analytics tools.
Conducted user interviews and applied JTBD analysis to map pain points.
AARRR Focus: Ensured early traction by confirming market demand before development.
Phase 2: Prototype & User Testing (Activation & Retention)
Phase 3: MVP Launch & Feedback Iteration (Retention & Revenue)
Phase 4: Scaling & Monetization (Revenue & Referrals)
Key Takeaways: How This Showcases My Product Thinking
User-Centered Discovery | Grounded in real user pain points, validated through research and interviews. |
Data-Driven Decision-Making | Leveraged market insights, AI feasibility, and competitive analysis. |
Strategic Execution | Balanced user needs, technical feasibility, and business viability in MVP design. |
Structured Roadmap | Prioritized development with clear success metrics and scalable growth strategy. |