Epic Pass Ski Tracker

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Project Overview

Curious about my lifetime skiing stats, I reverse-engineered the Vail Resorts Epic Pass mobile app API by intercepting HTTPS traffic with Proxyman. This uncovered an undocumented stats endpoint that returns day-by-day ski history including vertical feet, lift rides, and GPS data. I extracted 256 day records spanning December 2010 through January 2025 across four Colorado resorts: Vail, Breckenridge, Keystone, and Beaver Creek. A Python script processes the raw JSON into a clean CSV, and the interactive charts below are powered by Chart.js loading the data directly from the source.

Objectives

• Extract and preserve personal ski history from the Epic Pass platform via API reverse engineering.
• Build a reusable Python data pipeline to process and enrich the raw API data.
• Create interactive visualizations revealing skiing patterns across 15 seasons.
• Demonstrate API reverse-engineering and browser-based data visualization skills.

Key Results

• Successfully reverse-engineered an undocumented API by intercepting mobile app traffic with Proxyman.
• Extracted 256 day records spanning 15 ski seasons (Dec 2010 – Jan 2025) across 4 Colorado resorts.
• Python script processes raw JSON into enriched CSV with season labels, resort names, and derived fields.
• 8 interactive Chart.js visualizations reveal patterns in resort preference, seasonal trends, and activity levels.

Lifetime Stats

Best Skiing Day

Days by Resort

Vertical Feet Over Time

Lift Rides & Activity

Monthly Patterns

Methodology

• API Discovery: Used Proxyman to intercept HTTPS traffic from the Epic Pass mobile app, identifying the Vail Resorts stats API endpoint and authentication pattern (Bearer token + client credentials).
• Data Extraction: Single authenticated GET request with date range parameters to retrieve all historical day records as JSON (256 records, ~93KB).
• Data Processing: Python script (standard library only — no pip dependencies) parses JSON, maps resort IDs to names, derives ski season labels, and exports an enriched CSV.
• Visualization: Chart.js renders 8 interactive charts directly from the JSON data in the browser with no build tools or frameworks required.

Skills Demonstrated

• API Reverse Engineering: Intercepting and analyzing mobile app network traffic to discover undocumented endpoints and authentication schemes.
• Data Pipeline: Building a Python ETL script to transform raw API responses into analysis-ready datasets with derived fields.
• Data Visualization: Creating interactive browser-based charts with Chart.js and vanilla JavaScript for pattern discovery.
• Web Development: Building a responsive, data-driven project page following modern HTML/CSS patterns.

Technologies Used

Updating the Data

The Epic Pass API uses short-lived Bearer tokens (~1.5 hours), so automated refresh isn't practical. To update the dataset:

1. Open the Epic Pass mobile app while Proxyman (or a similar HTTPS proxy) is intercepting traffic.
2. Navigate to your stats/history in the app to trigger the API call.
3. Copy the Authorization: Bearer <token> header from the intercepted request.
4. Make a GET request to the stats endpoint with the fresh token:

   curl -H "Authorization: Bearer YOUR_TOKEN" \
     "https://api.vailresorts.com/digital/stats/statsapi/v3/Stats/ResortDayStat/YOUR_USER_ID?Limit=1000&MinDate=2010-01-01T00:00:00Z" \
     -o ski_day_data.json

5. Replace data/ski_day_data.json with the new response.
6. Re-run python process_ski_data.py to regenerate the CSV.

GitHub Repository

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