Desert Eagle | Hand Cannon
This skin has a restricted minimum float of 0.01, meaning Factory New versions are rarer. Maximum float is 0.70.
Market Prices
Market prices are volatile and subject to rapid change. Verify current listings before buying inputs or executing a trade-up.
| Wear | Float Range | Normal Price | Volume |
|---|---|---|---|
| FN Factory New | 0.00 – 0.07 | €647.31 | — |
| MW Minimal Wear | 0.07 – 0.15 | €529.83 | — |
| FT Field-Tested | 0.15 – 0.38 | €517.50 | — |
| WW Well-Worn | 0.38 – 0.45 | €488.78 | — |
| BS Battle-Scarred | 0.45 – 1.00 | €405.21 | — |
Buy Desert Eagle | Hand Cannon
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Trade-Up Relevance
This skin can be used as <strong>input</strong> in Restricted → Classified trade-up contracts from the The Cobblestone Collection. Why it matters: In CS2 trade-ups, this skin's float range dictates the average float target required. If used as an input, its collection determines output odds. If targeted as an output, its profitability depends on the input cost and current market price.
Possible Outputs (Classified)
Input Skins (Mil-Spec Grade)
Why Float, Rarity and Collection Matter for Trade-Ups
In CS2 trade-up contracts, the output skin's wear is determined by a mathematical formula using each input skin's float value and float range. Lower input floats produce better output conditions, which often means significantly higher output prices.
The collection determines which skins can appear as output, and the rarity determines the tier. Understanding these mechanics is the key to finding profitable contracts.
Float Value Details
Trade-Up Float Formula
adjusted_float = (input_float - 0.01) / (0.70 - 0.01)
When Desert Eagle | Hand Cannon is used as a trade-up input, its float value is first normalized to the skin's specific float range (0.01 – 0.70). The adjusted float contributes to the average that determines output float.
Output Float Prediction
With 10× Desert Eagle | Hand Cannon inputs at the same float, the output float for Classified skins in the The Cobblestone Collection would be:
output_float = out_min + avg_adjusted_float × (out_max - out_min)