Historical Data Analysis
Access 8 years of transaction data to identify patterns, trends, and predictability in sneaker markets.
📊
100M+
Data Points
📅
8 Years
Time Range
👟
5000+
Models Tracked
✓
99.8%
Accuracy Rate
Historical Analysis Types
Price Evolution
Track how specific models appreciate/depreciate over time
💡 Identify which models hold value long-term
Seasonal Patterns
Detect recurring trends by season, holiday, or release schedule
💡 Predict optimal times for specific model releases
Demand Lifecycle
Analyze demand curve from launch through stabilization
💡 Identify sell windows with maximum premium
Brand & Category Trends
Compare performance across brands, collaborators, and styles
💡 Select models most likely to hold value
Size Distribution
See which sizes are most profitable across different models
💡 Optimize size range for maximum ROI
Market Cycles
Identify boom/bust patterns in sneaker market
💡 Time deployments with market peaks
Predictive Insights
ML models trained on 8 years of data can predict:
- • Price trajectories - 87% accuracy in 48h forecasts
- • Sellout timing - Predict when specific sizes sell out
- • Demand peaks - Identify exact hours with highest demand
- • Resale premiums - Forecast expected profit margins
- • Competitive intensity - Estimate difficulty of winning bids
Available Queries
// Get price history for a model
GET /v3/history/prices?model=Nike%20Dunk%20Low&timeframe=8years
// Analyze seasonal patterns
GET /v3/history/seasonality?model=Jordan%204&analysis=yearly
// Predict price movement
POST /v3/prediction/price
{
"model": "Yeezy 350 V2",
"currentPrice": 450,
"days": 7
}
// Compare models historically
GET /v3/history/compare
?models=Nike%20Dunk%20Low,Jordan%204,New%20Balance%20550