A

Runner Injury Prevention Coaching Lab

3.70

Derivation Chain

Step 1 Marathon running boom
→
Step 2 Surge in running crew and race participation
→
Step 3 Demand for injury prevention among amateur runners

Problem

In 2026, marathon events are held weekly in Korea, and the running population has surged, but over 70% of amateur runners in their 30s-50s train without professional coaching, relying only on YouTube and community information, and suffer knee, ankle, and Achilles tendon injuries. A single injury costs an average of 500,000-1,500,000 KRW (approx. $375-$1,125) for orthopedic treatment and rehabilitation, with a recovery period of 4-12 weeks, making race entry fees and equipment costs sunk costs.

Solution

A mobile web service that automatically calculates overtraining risk when you input weekly training volume (distance, pace, elevation), and pushes daily personalized stretching and strengthening routines based on body type, running experience, and past injury history. It combines a race D-day reverse periodization plan with an injury symptom self-check questionnaire, and when risk signals are detected, it provides one-stop connection to nearby sports medicine clinics for appointments.

Target: Amateur runners in their 30s-50s who are employed, run at least 3 times a month, and participate in half/full marathon events at least twice a year, including members of running crews.
Revenue Model: Premium subscription at 39,000 KRW (approx. $29.25) per account per month (basic injury risk alerts are free; personalized routines, race plans, and clinic connections are paid). 20% discount for annual payment. 30% discount for running crew group subscriptions of 10 or more.
Ecosystem Role: Education
MVP Estimate: 2_weeks

NUMR-V Scores

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

Feasibility (73%)

Tech Complexity
29.3/40
Data Availability
23.3/25
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 (70/100)

Competition
10.0/20
Market Demand
20.0/20
Timing
16.0/20
Revenue Signals
9.0/15
Pick-Axe Fit
10.5/15
Solo Buildability
5.0/10
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 [medium] AI/ML [low]
Dashboard