> For the complete documentation index, see [llms.txt](https://help.runonrufus.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.runonrufus.com/rufus-race-manager/classification-and-results-processing/understanding-classifications.md).

# Understanding Classifications

In **RUFUS Race Manager (RRM)**, race classifications are generated **dynamically** each time they are requested. Unlike systems that “lock” results after initial processing, RRM continuously adapts to changes, ensuring results are always **accurate, up to date, and consistent** with the raw timing data.

## Dynamic Recalculation

Every time classifications are viewed or exported, RRM recalculates them in real time. This provides key benefits:

* **Modifications without reprocessing** → Edits to participants, passings, or race start times are immediately reflected in the results. No batch reprocessing is required.
* **Error correction** → Mistakes (like missing or duplicated passings) can be fixed on the fly, with classifications updating instantly.
* **Raw data preserved** → All passings are stored with their original timestamps. Adjustments never overwrite the source data.

## Role of Segments and Checkpoints

Classifications in RRM are built on the structure of **checkpoints** and **segments**:

* **Checkpoints** mark specific locations on the course (Start, intermediates, Finish). As participants cross checkpoints, passings are recorded.
* **Segments** are the timing intervals between checkpoints. At least one segment (Start → Finish or Gunshot → Finish) is required for ranking a **CLASSICAL** race. Additional segments (like Start → 10k or Swim → Bike) allow for splits and transition analysis.

By combining checkpoints and segments, RRM generates meaningful classifications that reflect both total race times and detailed performance insights.

The diagram shows how raw **Passings** flow into **Checkpoints**, combine into **Segments**, and ultimately generate **Classifications**.

<figure><img src="/files/URTgNiP9kpjn9gjPdN4v" alt=""><figcaption><p>Classification Flow Diagram</p></figcaption></figure>

## Why This Matters

This approach ensures that:

* Last-minute changes don’t break results.
* Manual passings or floating passings can be added confidently.
* Rankings are always based on the most accurate, current data.

Timers can adapt to real-world conditions (mass starts, late edits, hardware hiccups) without sacrificing the reliability of official results.

## Summary

Classifications in RRM are **live, dynamic, and resilient**. By recalculating on demand and preserving original data, the system guarantees that every result reflects the true state of the race—no matter when or how it’s reviewed.