> For the complete documentation index, see [llms.txt](https://docs.everesteer.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.everesteer.ai/scoring/payout-and-payout-factor.md).

# Payout

How a round score becomes money.

{% stepper %}
{% step %}

### Add the terms

CORR, AIMC and NCORR are added with equal weight. All three are covariances with the centred target, so they share one unit.
{% endstep %}

{% step %}

### Bend through an arctan

The round score is `b * arctan((CORR + AIMC + NCORR) / b)`, with `b` a fixed scale. Near zero the score is the sum itself; further out it bends smoothly and never passes its bound. The map is strictly increasing, so a better sum always scores more. **See `explain_scoring` for the live scale and bound.**
{% endstep %}

{% step %}

### Apply to stake

Payout is `stake * round score`. There is no payout multiplier. The bound is symmetric: a bad round can take as much of your stake as a good round can add.
{% endstep %}
{% endstepper %}

Several Himalayas rounds can be in their scoring window at the same time. Each has its own partial-then-resolved path.

{% hint style="danger" %}
The bound is wide enough that **a single round can take an entire stake**.
{% endhint %}

## Where to read the live formula

**`GET /api/v1/scoring`** is the machine-readable source of truth for the payout formula. It needs no API key (the access gate still applies). The Python client exposes it as `client.explain_scoring()`; the MCP tool is `explain_scoring`.

The response carries:

* **`formula`**: the payout formula as a plain-language string.
* **`weights`**: a dict with the `corr`, `aimc` and `ncorr` term weights and the arctan `score_multiple_constant`.
* **`metrics`**: plain-language definitions of CORR, AIMC and NCORR.
* **`rank_metric`**: present on event boards; names what the board was ordered by.

The formula is read live from platform config at call time. It is not hardcoded in the endpoint.

{% hint style="warning" %}
Rounds settled under an earlier formula still show the `payout_factor` and `stake_return_amplitude` they froze. Those fields are history: every round settled since pays `stake * round score`.
{% endhint %}


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.everesteer.ai/scoring/payout-and-payout-factor.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.