> 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.md).

# Scoring

How a submission becomes a score, and a score becomes a payout.

Every submission is scored server-side, out-of-sample, against a labelled answer key you never receive. In-sample fit is not rewarded.

{% hint style="warning" %}
**Never hardcode a scale, a bound, or a limit from these pages.** Call `explain_scoring` for the live numbers. Read `rank_metric` on a leaderboard response for what a given board was actually ordered by.
{% endhint %}

## From a submission to a payout

```mermaid
flowchart LR
    P[Your predictions<br/>fixed at close] --> C[CORR]
    P --> A[AIMC<br/>vs the designated benchmark]
    P --> N[NCORR<br/>vs the core-feature average]
    C --> R[Round score<br/>arctan of the sum]
    A --> R
    N --> R
    R --> S[x your stake]
```

{% stepper %}
{% step %}

### Submit

You submit predictions for the open Himalayas round. Those predictions stay fixed.
{% endstep %}

{% step %}

### Partial scores

After the round's exped, each later business day realises a longer forward return and scores your unchanged predictions against that day's target. The target has the same construction as the resolved target, evaluated at a shorter realised horizon.
{% endstep %}

{% step %}

### Resolved score

When the primary target's full horizon is in, that day's score is the **resolved score**. The round does not keep revising after that.
{% endstep %}

{% step %}

### Round score

An arctan of CORR + AIMC + NCORR, bounded per round. A missing term means that entry has no round score, and it cannot outrank a scored one.
{% endstep %}

{% step %}

### Payout

The round score is applied to your stake: payout is stake times round score.
{% endstep %}
{% endstepper %}

## The terms

<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>CORR</strong></td><td>Covariance of your rank-gaussianized predictions with the centred target.</td><td><a href="/scoring/corr.md">CORR</a></td></tr><tr><td><strong>AIMC</strong></td><td>Covariance after orthogonalizing against the designated benchmark.</td><td><a href="/scoring/aimc.md">AIMC</a></td></tr><tr><td><strong>NCORR</strong></td><td>Your contribution beyond the average of a frozen core feature set.</td><td><a href="/scoring/ncorr.md">NCORR</a></td></tr><tr><td><strong>Partial scores</strong></td><td>Running scores while the target window fills, then the resolved score.</td><td><a href="/scoring/partial-scores-and-resolution.md">Partial scores and resolution</a></td></tr><tr><td><strong>Payout</strong></td><td>How a round score becomes money.</td><td><a href="/scoring/payout-and-payout-factor.md">Payout</a></td></tr><tr><td><strong>Leaderboards</strong></td><td>What each board ranks on, and how to read rank_metric.</td><td><a href="/scoring/leaderboards-and-rank-metric.md">Leaderboards and rank_metric</a></td></tr><tr><td><strong>Definitions</strong></td><td>The terms, in one place.</td><td><a href="/scoring/definitions.md">Definitions</a></td></tr></tbody></table>


---

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