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With the rapid growth of recreational tennis players in Korea (estimated 30% increase vs. 2025), tennis lesson coaches (Freelancer, ages 30-50) manage 15-25 students per week but spend an additional 30-40 minutes per student manually analyzing and documenting form (grip, swing, footwork) feedback from recorded videos. Lack of systematic progress tracking leads to high student churn.
Coaches upload student lesson videos filmed on smartphones, and AI pose analysis (MediaPipe/MoveNet) automatically measures joint angles and swing trajectories for forehands, backhands, and serves, generating Reports comparing improvements and weaknesses against previous lessons. Integrates per-student progress timelines with coach comments.
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |