---
title: Score Analytics
description: Visualize score trends and quality metrics across your project
---

# Score Analytics

Latitude provides time-series dashboards that help you understand quality trends across your project. Score analytics answer questions like: Is quality improving? Which evaluations catch the most failures? Are there patterns in when issues occur?

## Project-Level Dashboard

The project overview shows aggregate score metrics:

- **Pass/fail distribution**: How many scores passed vs. failed over time
- **Failure rate trend**: The percentage of failing scores, trended over days or weeks
- **Score volume**: Total number of scores produced, broken down by source (evaluation, annotation, custom)

These give you a high-level picture of your agent's quality trajectory.

## Evaluation-Level Analytics

Each evaluation has its own analytics page showing:

- **Pass/fail trend**: How the evaluation's results have changed over time
- **Value distribution**: Histogram of score values
- **Volume**: How many traces the evaluation has scored
- **Alignment**: If human annotations exist for the same traces, alignment metrics (MCC, agreement rate) are shown

Use evaluation-level analytics to detect:

- Regressions: sudden increase in failure rate
- Improvements: failure rate dropping after a fix
- Drift: alignment with human judgment changing over time

## Issue-Level Analytics

Each issue tracks its own trends:

- **Occurrence count**: How many times the issue has been detected
- **Lifecycle state**: Whether the issue is New, Escalating, Resolved, or Regressed
- **Resolution history**: When it was resolved and whether it has regressed

## Score-Aware Trace Filtering

Traces and sessions can be filtered by score-derived properties, letting you find interactions based on their quality signals:

- **Score state**: Find traces with failing scores, passing scores, or draft annotations
- **Value thresholds**: Find traces where scores fall below a quality threshold
- **Issue linkage**: Find traces associated with a specific issue
- **Score source**: Find traces scored by a specific evaluation, annotation source, or custom source

This bridges observability and reliability. You can go from "show me traces where the jailbreak evaluation failed" directly to investigating the conversations.

## Filtering Analytics

Analytics dashboards use the same [filter system](../observability/filters) as the trace view. You can narrow analytics to:

- Specific time ranges
- Specific models or providers
- Specific score sources (only evaluations, only annotations, etc.)
- Custom metadata values

This lets you answer targeted questions like "What's the failure rate for GPT-4 traces in production this week?"

## Next Steps

- [Scores Overview](./overview): How the score model works
- [Evaluations](../evaluations/overview): How automated evaluations produce scores
- [Issues](../issues/overview): How failure patterns are discovered from scores
