Adidas Resale Trend Prediction: Data Modeling Methodology with acbuy Spreadsheet
2025-05-29
In the booming sneaker resale market, predicting Adidas hype releases has become crucial for resellers. This article explores how acbuy spreadsheet data modeling
Data Foundation: Key Metrics in acbuy Spreadsheets
- Historical Transaction Records: Last sale prices for similar models
- Real-time Search Volume: Trend data across platforms
- Inventory Turnover Rates: Days-on-market metrics
- Size-specific Premiums: Breakdown by US/EU sizing
Pro Tip: Always normalize data against general market trends to isolate Adidas-specific demand signals.
The Predictive Modeling Framework
| Factor | Weight | Data Source |
|---|---|---|
| Collaboration Tier | 35% | Past collab performance |
| Design Uniqueness Score | 25% | Social media sentiment |
| Production Volume | 20% | Retailer pre-order quotas |
| Athlete Association | 15% | Player performance metrics |
| Cultural Relevance | 5% | Spotify/Google Trends |
The weighted formula generates a HPI™
Validation Case: Forum 84 Bad Bunny Collab
Our model predicted 82 HPI
Practical Implementation Steps
- Scrape latest stockists list from Adidas Confirmed app
- Import acbuy's regional resale data into Google Sheets
- Apply our HPI formula template (available for premium users)
- Cross-reference with factory production leaked data
- Adjust for local hype factors (event tie-ins, celebrity co-signs)
"Data modeling reduced our deadstock from 27% to 9% within two quarters" - Taobao Top 10 Reseller