ValorLens — Valorant Analytics Modeling Layer

Renamed collaborative edition of VLML. Original commit history and license are retained.

VLML is a structured modeling layer for Valorant esports analytics. It standardizes metrics and relationships between datasets, then exposes them through MCP so AI tools can generate accurate and explainable insights.

Why This Exists

• The data model is the product. VLML is built around an analytics data model with pre-computed metrics, so analysis is fast and consistent.

• MCP is just the bridge. The server delivers structured, data-only payloads to Claude, Gemini, and other LLMs — the AI generates the coaching insights, not the server.

• Raw data becomes the model. Source data comes from GRID JSON exports, and VLML transforms it into a structured analytics model.

Related MCP server: League of Legends MCP Server

Architecture at a Glance

1. Modeling layer (DuckDB): Atomic events plus aggregated round/game/series tables optimized for analytics.

2. MCP tools (bridge): Tools that return structured metrics only — no narratives, no opinions.

3. LLM layer (insights): Generates narrative, recommendations, and VOD priorities.

What You Get

• Match analysis: Team comparison, round timelines, impact metrics, VOD review targets.

• Coaching context (v3.0): Economy cascades, round situations, attack patterns, historical benchmarks.

• Player profiling: Career stats, agent/map splits, clutch performance, trend signals.

• Scouting reports: Map pool, roster tendencies, opening duels, trade quality.

• Deep-dive queries: Use query_sql for ad-hoc analysis directly against the analytics tables.

Quick Start

python -m venv .venv
source .venv/bin/activate
pip install -e .
cp .env.example .env

Download raw events and build the database:

python database/scripts/ingestion/download_raw_events.py --year 2025
python database/scripts/orchestration/run_pipeline.py --year 2025

Run the MCP server:

vlml

Or:

.venv/bin/python -m vlml.server

Tools

Insights tools:

• match_analysis_report

• player_profile_report

• scouting_report

• pattern_detection_report

Database tools:

• query_sql

• get_database_info

See docs/tools.md for input/output details.

Documentation

Getting Started:

• Setup: docs/setup.md

• Data flow: docs/data_flow.md

• Troubleshooting: docs/troubleshooting.md

Reference:

• Tools: docs/tools.md

• Architecture: docs/architecture.md

• Prompting Guide: docs/prompting_guide.md

• Win Shares: docs/win_shares.md

• Report standard: insights_reference.md

Database:

• Pipeline guide: database/README.md

• Data model: database/DATA_MODEL.md

• Derived tables: database/DERIVED_TABLES.md

Contributing:

• Contributor guide: docs/contributing.md

• SQL helper index: src/vlml/tools/sql/README.md

Notes

• No prebuilt database is shipped. Use the pipeline to build data/vlml_events.duckdb.

• Raw input data comes from GRID JSON exports and is transformed into VLML analytics tables.

• All reports return metrics and evidence only. LLMs should generate insights and recommendations.