How float ranges affect CS2 trade-up outputs
Float is one of the biggest reasons two trade-ups with similar expected value can behave differently in practice. The same output skin can land in a different wear band depending on the average float of the inputs and the skin-specific float cap of the output.
The average input float is only the start
A trade-up uses the average float of its inputs, but the final output float is normalized through the output skin's minimum and maximum float range. That means a low average input float is useful, but it does not map one-to-one onto every possible output.
For example, an output with a narrow float cap can stay inside a desirable wear band more easily than an output with a wide cap. This is why contract analysis needs to evaluate every eligible output, not just the average input number.
Wear bands change pricing behavior
Factory New, Minimal Wear, Field-Tested, Well-Worn, and Battle-Scarred prices can be very different. A contract that pushes a valuable output just below a wear threshold may be materially better than one that lands the same output slightly above it.
The reverse is also true. If most output probability lands in a weak wear band, a contract can look attractive on collection names but fail after exact float pricing is considered.
Skin-specific caps matter
Not every skin can exist across the full 0.00 to 1.00 range. Some skins are capped, and those caps define how trade-up output floats are calculated. Ignoring caps can overstate or understate the value of a contract.
Phase variants and special finishes also need careful handling. A price for one Doppler or Gamma Doppler phase should not be blindly used for another phase when the market prices them differently.
Practical float review checklist
- Confirm the average input float required for the target output wear band.
- Check every eligible output, not only the high-value target skin.
- Use wear-specific pricing rather than broad item averages.
- Verify rare variants manually before buying inputs.
Float targeting can improve a scenario, but it also makes sourcing harder. A mathematically strong contract still needs input listings that can actually be bought at the required floats and prices.