CoachLink

Inspiration

Access to high-quality sports coaching remains uneven, especially for early-stage athletes. While elite coaching exists, it is often: Geographically limited Expensive Not personalized At the same time, a large pool of retired and semi-professional athletes remains underutilized. CoachLink was built on a simple hypothesis: If knowledge in sports can be digitized and distributed, coaching can become scalable. We aim to bridge this gap by combining human expertise with AI-driven feedback systems.

-What It Does CoachLink is a hybrid coaching ecosystem that integrates:

1. Human Coaching Layer Athletes can: Discover verified coaches Book 1-on-1 sessions Follow structured training programs
2. AI Performance Analysis Users upload training videos System analyzes: Technique Consistency Accuracy Core evaluation model:

Score=w1⋅Technique+w2⋅Consistency+w3⋅Accuracy

Where: w1,w2,w3 are adjustable weights based on sport-specific priorities

1. Hybrid Model (Online + Offline) Online: feedback, coaching, analytics Offline: real-world practice sessions This creates a continuous improvement loop, not just one-time coaching

-How We Built It System Architecture (Conceptual) Frontend: Web/mobile interface for: Booking Video uploads Dashboard tracking

Backend: User management Coach marketplace system Session scheduling

AI Module (Concept Prototype): Video input → frame extraction Feature detection (pose, motion, angles) Performance scoring

Simplified pipeline:

Video Input → Frame Processing → Feature Extraction → Scoring Engine → Feedback Output

Since this is an early-stage build: Designed UI/UX flows for user journey Created mock analysis outputs Defined scoring logic + system architecture Key Features Video-based feedback system Personalized training insights Athlete-to-coach marketplace Progress tracking dashboard Continuous feedback loop Challenges We Faced

1. Data & AI Complexity Real performance analysis requires: Large datasets High accuracy

• Approach:

Start with simplified scoring models Scale to advanced computer vision later

1. Two-Sided Marketplace Problem Need both: Coaches Athletes

-Strategy:

Start with niche sports communities Build supply first, then demand

1. Trust & Verification Users need credible coaches Solution: Verification system Ratings & reviews Performance-based credibility

-What We Learned Simplicity beats overengineering A strong user flow matters more than features Real-world problems require hybrid solutions Building for both users and providers is complex but powerful Future Scope Advanced AI using pose estimation models Full mobile application Multi-sport expansion Partnerships with academies Data-driven athlete progression insights

Why CoachLink Matters CoachLink transforms coaching from: Static, location-based, and limited into dynamic, accessible, and data-driven

We’re not just building a platform — we’re building an ecosystem for the future of sports training.

Built With

• ai