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When baseball fans interested in sabermetrics — non-specialists like college students and office workers — try to learn data analysis, existing Education Platforms focus on marketing and finance datasets, with few sports datasets or analysis examples available. There's no curriculum that teaches programming fundamentals and baseball data analysis simultaneously, wasting 2–3 months on self-study trial and error.
An interactive coding lab built on historical KBO (Korean Baseball Organization) record datasets, guiding learners step-by-step from Python data analysis basics to calculating sabermetrics indicators (WAR, OPS+, FIP, etc.). With in-browser code execution, instant visualization, and an AI hint system, even non-specialists can produce their own analysis Report within 4 weeks.
| 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. |