How does the optimizer decide where to put points?

It uses greedy marginal-gain allocation. Each point goes to whichever stat would gain the most effectiveness right now. Because each stat follows a diminishing-returns curve, this greedy method is provably optimal for concave functions.

What is the diminishing-returns curve?

Each stat contributes weight times the square root of its points. The square root means the first points in a stat are worth more than the hundredth, so dumping everything into one stat is rarely best.

Why do my weights matter so much?

Weights represent how valuable each stat is for your class and playstyle. A glass-cannon caster might weight Intelligence high and Vitality low, which shifts the optimal allocation toward Intelligence.

Does this match my exact game's stat math?

It models the common pattern of weighted stats with diminishing returns, which fits most RPG-style games. If your game uses a different curve, treat the result as a strong starting point rather than an exact in-game value.

Can two stats end up with the same number of points?

Yes. When stats have equal weights or the marginal gains tie, the optimizer spreads points evenly because that maximises the combined score under diminishing returns.

CS2 Stat Optimizer — Gera Tools

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Spend stat points where they actually pay off

Most build calculators just total your stats. This CS2 Stat Optimizer instead answers the harder question: given a fixed pool of points, where should each one go? It models each stat with a diminishing-returns curve and allocates points one at a time to whichever stat gives the biggest gain at that moment.

How it works

Each stat’s effectiveness is modeled as weight × sqrt(points). The square root captures diminishing returns: the value of adding a point shrinks as a stat grows, so over-investing in a single stat wastes potential. The optimizer computes the marginal gain of the next point for every stat:

marginal(weight, current) = weight × (sqrt(current + 1) − sqrt(current))

It then assigns each point to the stat with the highest marginal gain and repeats until the pool is empty. Because the per-stat curve is concave, this greedy rule produces the globally optimal distribution — there is no allocation that scores higher.

Tips and example

If you set all four weights equal and have 100 points, the optimizer spreads them roughly evenly, since equal weights with the same curve are balanced by equal allocation. Raise one weight and watch points migrate toward it — but never all of them, because diminishing returns still reward some spread. Use the weights to encode your role: tanks lean on Vitality and Strength, casters on Intelligence, skirmishers on Agility.