B

Baseball Fan Gameday Planner

2.95

Derivation Chain

Step 1 Doosan Bears and KBO season opener buzz
→
Step 2 Growing pro baseball attendance and in-person fan culture
→
Step 3 Integrated gameday planner for optimal schedule, seating, and transportation

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

KBO (Korean Baseball Organization) fans attending games in person spend 30 minutes to 1 hour preparing for each visit — checking game schedules, comparing seats (sunlight, sightlines, pricing), arranging transportation (parking, public transit), and searching for restaurants near the stadium across multiple separate apps. This is especially problematic for away games, where unfamiliarity with each stadium's layout leads to frequent poor seat choices.

Solution

Provides a seat guide comparing sightline photos, sunlight direction, and pricing across all 10 KBO stadiums. Based on the game schedule, it recommends optimal transportation (public transit routes, real-time parking availability). Nearby restaurants and convenience stores within 500m of the stadium are displayed on a map, and a gameday recommendation score is calculated by combining weather forecasts.

Target: KBO fans aged 20–40 who attend games in person, baseball enthusiasts attending 10+ games per season
Revenue Model: Premium Subscription at 2,900 KRW/month (~$2.20/month). Free tier: seat guide + schedule. Premium: real-time parking + restaurants + weather integration + away game guide
Ecosystem Role: Consumer
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
2.0/5
U Urgency
2.0/5
M Market
3.0/5
R Realizability
4.0/5
V Validation
3.0/5
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.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (76%)

Tech Complexity
34.7/40
Data Availability
20.8/25
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 (50/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
7.5/15
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

Backend [low] Frontend [medium] Data Pipeline [low]
Dashboard