The lucrative market for limited-edition Air Jordan re-releases demands more than just luck and guesswork. Successful resellers rely on precise data analytics to navigate the complexities of demand, hype, and profitability. The CNfans spreadsheet has emerged as an indispensable tool for this very purpose, providing a sophisticated predictive analysis model tailored for the Chinese sneaker proxy buying (代购) scene.

Deconstructing the CNfans Presale Data Analysis Model

At its core, the CNfans model is a powerful algorithm built within a collaborative spreadsheet environment. It aggregates historical release data to identify patterns and trends that are invisible to the naked eye. By inputting key variables, resellers can transform raw data into a strategic procurement plan.

The model's accuracy stems from its multi-faceted approach to data collection. It doesn't just look at past prices; it synthesizes three critical dimensions of the sneaker market:

1. Social Media Volume and Sentiment Tracking

Hype is the engine of the sneaker market. The CNfans spreadsheet tools automate the process of scraping and analyzing data from major platforms like Weibo, Xiaohongshu, Douyin, and overseas forums. By measuring the volume of posts, mentions, and engagement rates for a specific Air Jordan colorway weeks before its drop, the model quantifies the "buzz factor." A sudden spike in positive sentiment is a strong leading indicator of high demand.

2. Historical Secondary Market Premium Rates

This is the financial backbone of the model. The algorithm ingests historical resale data from platforms like Poizon, StockX, and GOAT. It doesn't just calculate an average premium; it tracks how the value of similar models and collaborations (e.g., Off-White, Travis Scott) appreciated over time—48 hours, one week, one month post-drop. This allows for a dynamic profit margin forecast that accounts for both quick "flips" and longer-term holds.

3. Colorway Popularity Index (CPI)

Not all colorways are created equal. The CNfans model assigns a weighted Colorway Popularity Index to upcoming releases based on historical performance of analogous styles, brand collaborations, and classic OG status. A "Bred" or "Chicago" colorway will inherently have a higher CPI than a less iconic one, significantly influencing the demand forecast.

From Data to Decision: Outputting Size-Specific Recommended Procurement

The true genius of the system is its granular output. The consolidated data is processed through a demand prediction algorithm that generates specific recommended purchase quantities per shoe size. This is crucial because demand and profit margins can vary dramatically between sizes.

For instance, the model might analyze a release like the Travis Scott x Air Jordan 1 Low. By examining the 48-hour resale data from the previous collaboration, it can reveal that larger sizes (US 11-13) in North America yield a 15% higher profit margin than smaller sizes, while in Asia, sizes US 8-9.5 have a faster sell-through rate. This allows a reseller to strategically allocate their budget and efforts, maximizing both profit and efficiency.

"The CNfans model transformed our approach from speculative buying to calculated investment. We're no longer just buying hype; we're buying data." - A top-tier sneaker proxy buyer.

A Practical Workflow: Implementing the CNfans Spreadsheet

1. Data Collection:CNfans.run.
2. Variable Input:
3. Automated Analysis:
4. Strategy Formulation: