InjurAI

Know the real injury risk before the panic starts

Score: 7.8/10ArgentinaHard BuildReady to Spawn
Brand Colors

The Opportunity

Problem

Argentina fans endure acute anxiety from Messi and key player injury scares threatening their World Cup hopes

Solution

InjurAI uses public training data, weather, pitch conditions, and historical patterns to predict exact probability of key Argentine players missing upcoming matches. Instead of vague 'Messi has a knock' headlines, fans get precise percentages and explanations that remove the terror of the unknown.

Target Audience

Argentine national football team supporters and Messi-centric football fans

Differentiator

First predictive injury intelligence platform built exclusively for the emotional needs of Argentine and Messi fans with transparent model explanations

Brand Voice

professional

Features

Injury Probability Engine

must-have70h

Daily updated risk percentages for next 5 matches for Messi and key players

Factor Breakdown

must-have45h

Shows exactly why the model gave a certain probability (age, pitch, recent workload, etc)

Historical Accuracy Tracker

must-have30h

Shows how accurate past predictions were to build user trust

Custom Match Simulator

must-have55h

See how different scenarios (Messi rests vs plays) affect Argentina's chances

Early Warning System

must-have35h

Alerts when a player's risk jumps more than 15% from baseline

Data Visualization Suite

nice-to-have50h

Beautiful charts showing workload, recovery curves and risk trends

Exportable Reports

nice-to-have25h

PDF reports fans can share in their groups to calm friends

API for Fantasy Players

future60h

Data feed for fantasy football users who want edge

Total Build Time: 370 hours

Database Schema

users

ColumnTypeNullable
iduuidNo
emailtextNo
tiertextNo
preferred_playerstextYes
created_attimestampNo

Relationships:

  • predictions.user_id → users.id

predictions

ColumnTypeNullable
iduuidNo
user_iduuidYes
player_nametextNo
match_datetimestampNo
risk_percentageintNo
confidenceintNo
created_attimestampNo

model_factors

ColumnTypeNullable
iduuidNo
prediction_iduuidNo
factor_nametextNo
impact_valueintNo
explanationtextNo

Relationships:

  • model_factors.prediction_id → predictions.id

API Endpoints

GET
/api/predictions

Get latest risk predictions for user's players

🔒 Auth Required
POST
/api/predict

Trigger new prediction calculation (admin/internal)

🔒 Auth Required
GET
/api/factors

Get detailed factor breakdown for a prediction

🔒 Auth Required

Tech Stack

Frontend
React Native with Expo
Backend
Python with FastAPI
Database
PostgreSQL
Auth
Auth0
Payments
Mercado Pago
Hosting
Railway
Additional Tools
Scikit-learn for baseline modelsBeautifulSoup + API scrapers for training dataPlotly for visualizations

Build Timeline

Week 1: Data pipeline

48h
  • Scrapers for player workload data
  • Database design
  • Auth setup

Week 2: Core prediction model

55h
  • Initial ML model
  • Factor explanation logic
  • Admin dashboard

Week 3: Mobile app foundation

50h
  • React Native UI
  • Prediction feed
  • Detail screens

Week 4: Visualization and alerts

45h
  • Charts
  • Push notification system
  • Risk jump detection

Week 5: Payments and polish

35h
  • Mercado Pago integration
  • Landing page
  • Testing with beta users
Total Timeline: 5 weeks • 310 hours

Pricing Tiers

Free

$0/mo

Updated once per day

  • Messi risk only
  • Basic percentages
  • One match lookahead

Analyst

$12/mo

No limits

  • All players
  • Factor breakdowns
  • 5-match lookahead

Oracle

$25/mo

No limits

  • Everything + custom simulations
  • Historical accuracy reports
  • PDF exports
  • Priority support

Revenue Projections

MonthUsersConversionMRRARR
Month 19505%$1,188$14,250
Month 67,2009%$16,200$194,400

Unit Economics

$18
CAC
$235
LTV
4%
Churn
78%
Margin
LTV:CAC Ratio: 13.1xExcellent!

Landing Page Copy

Stop guessing. Start knowing.

Precise injury probability predictions for Messi and Argentina with full model transparency

Feature Highlights

Data-driven peace of mind
Beat the rumor mills
Built for Argentine fans by Argentine fans

Social Proof (Placeholders)

""This app saved my sanity during the last international break." — Carlos, Córdoba"
""The factor breakdowns actually make sense." — Martín, data analyst"

First Three Customers

1. Seed beta access to fantasy football communities in Argentina who already obsess over player availability. 2. Partner with two popular football data Twitter accounts for affiliate revenue share. 3. Cold DM members of statistics-focused Seleccion fan Discords offering free Oracle tier for feedback.

Launch Channels

Product Huntr/FantasyPLr/ArgentinaTwitter football data accountsLinkedIn sports analytics

SEO Keywords

messi injury probabilityplayer availability predictorargentina injury riskfootball injury predictionmessi fitness tracker

Competitive Analysis

Injury Expert

injuryexpert.com
Freemium
Strength

Large historical database

Weakness

No probabilistic model or fan-focused explanations

Our Advantage

We translate numbers into emotional relief for Argentine fans

🏰 Moat Strategy

Continuously improving model trained on unique Argentine league + international match workload data combined with fan reaction patterns

⏰ Why Now?

Increased availability of public training data, major improvements in explainable AI, and heightened anxiety around Messi's final years

Risks & Mitigation

technicalhigh severity

Model accuracy could be poor initially

Mitigation

Start with conservative predictions and be transparent about confidence levels

marketmedium severity

Fans prefer hope over data

Mitigation

Heavy emphasis on 'this is what actually happened last 47 times' storytelling

Validation Roadmap

pre-build14 days

Create landing page and collect 400 email signups from targeted ads

Success: At least 400 signups with 22% stating they would pay

mvp30 days

Release closed beta to 120 users and track prediction vs actual outcomes

Success: Model accuracy > 68% and 40% conversion from beta to paid

Pivot Options

  • Sell predictions to betting syndicates
  • Expand model to all major leagues as general sports analytics tool
  • White label for football clubs' internal medical teams

Quick Stats

Build Time
310h
Target MRR (6 mo)
$16,000
Market Size
$35.0M
Features
8
Database Tables
3
API Endpoints
3