CS2 Trade-Up Guide

CS2 Trade-Up Output Float Formula: A Step-by-Step Calculator Guide

What is the CS2 trade-up output float formula?

Normalize each exact input float before averaging. Every input uses its own skin-specific minimum and maximum:

Normalized input_i = (raw float_i − input min_i) / (input max_i − input min_i)
Average normalized = (normalized_1 + ... + normalized_N) / N
Output float = output min + average normalized × (output max − output min)

Do not use an input wear label or the raw arithmetic mean as a shortcut. A raw 0.04 input from a 0.00–0.08 skin is 0.50 normalized; a raw 0.04 input from a 0.00–1.00 skin is only 0.04 normalized. When a basket mixes input skins, normalize every item separately before taking the mean.

How do you calculate output float step by step?

Assume all 10 inputs use the same restricted range of 0.00–0.08 and their exact raw floats average 0.0096. Because the range is identical for every input, normalize that raw average:

Average normalized = (0.0096 − 0.00) / (0.08 − 0.00)
                   = 0.12

For a possible output with a permitted range of 0.00–0.50:

Output float = 0.00 + 0.12 × (0.50 − 0.00)
             = 0.12 × 0.50
             = 0.06

A 0.06 result is Factory New because it is below the 0.07 boundary. But another output in the same contract might have a 0.10 to 0.70 range:

Output float = 0.10 + 0.12 × (0.70 − 0.10)
             = 0.172

That outcome is Field-Tested. One input basket can therefore create different wear tiers across the output pool.

What are the CS2 wear boundaries?

A skin's own minimum and maximum can remove some of these wears. If its minimum is 0.10, it cannot exist in Factory New even though the global FN interval starts at zero.

How do you find the maximum normalized input average?

First solve for the maximum average normalized input position when you know the output wear you need:

Maximum average normalized = (target boundary − output min) / (output max − output min)

For an output ranging from 0.00 to 0.50, the normalized average required to stay below the 0.15 Minimal Wear/Field-Tested boundary is:

(0.15 − 0.00) / (0.50 − 0.00) = 0.30

Your true normalized average must be slightly below 0.30, not exactly on it. Translate that limit through each input skin's range:

Raw input target = input min + normalized target × (input max − input min)

For inputs restricted to 0.00–0.08, a 0.30 normalized cap means a raw average below 0.024. For inputs restricted to 0.10–0.70, it means a raw average below 0.28. A mixed-skin basket has no single universal raw cap; validate the average of all per-item normalized values.

How much safety margin should you use near a boundary?

Use exact inspected floats and target an average normalized position comfortably under the calculated cap. A fixed raw-float margin is not equally useful across different input or output ranges, so judge the safety margin in output-float terms. If one wear-tier step destroys the contract's EV, a tiny theoretical saving on inputs is not worth boundary risk.

The best check is to normalize every selected input, calculate every outcome at the exact basket average normalized position, and flag the smallest distance to 0.07, 0.15, 0.38, or 0.45. Read our float compression and edge-case guide for why those small distances can create large price changes.

Can one expensive low-float input rescue the average?

Yes, mathematically. Each standard-contract input contributes one tenth of the normalized average. If nine selected items total 1.80 normalized units and your target average normalized position is 0.19, the tenth input can contribute at most:

Maximum normalized total = 0.19 × 10 = 1.90
Maximum tenth normalized value = 1.90 − 1.80 = 0.10

Convert 0.10 back through the tenth skin's own range. For a 0.00–0.08 input, its raw float must be at most 0.008; for a 0.10–0.70 input, it can be at most 0.16. This is useful when optimizing cost, but always validate the complete basket's normalized arithmetic.

What float-calculation mistakes are most common?

• Averaging raw input floats instead of normalizing every item within its own allowed range.
• Averaging rounded listing labels instead of the inspected float values.
• Applying one output range to every possible skin.
• Confusing an input's wear label with its numerical float.
• Rounding normalized values or the basket average before completing the formula.
• Pricing the output wear correctly but ignoring overpay for low-float inputs.

Once output float is known, combine its wear-specific sale price with the probability of that outcome. That produces the input for an accurate EV calculation.

Frequently Asked Questions