B

Sports Data Analysis Sandbox

3.50

Derivation Chain

Step 1 Growing interest in professional baseball sabermetrics
→
Step 2 Demand for introductory baseball data analysis Education
→
Step 3 Sports data analysis practice Platform for non-specialists

Problem

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.

Solution

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.

Target: Baseball fan college students and office workers (ages 20–40), sports journalism/media majors, data analysis beginners
Revenue Model: One-time course fee of 49,000 KRW (~$37) for the 4-week fundamentals course; Monthly Subscription of 29,000 KRW (~$22) for advanced content + new season data updates. Free Trial (Week 1 free)
Ecosystem Role: Education
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
4.0/5
V Validation
4.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (77%)

Tech Complexity
34.7/40
Data Availability
22.5/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (56/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
7.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Frontend [medium] Backend [low] AI/ML [low]
Dashboard