ValoML - Automated Tactical Scouting Reports

Military-grade AI that transforms GRID esports data into actionable VALORANT scouting reports in 15 seconds. K-Means clustering + Llama 3.3 for tactical insights coaches actually need.

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Inspiration

In competitive VALORANT, coaches spend hours reviewing VODs and building spreadsheets to answer simple questions:

  • "Where does aspas hold on Haven?"
  • "What's their pistol round win condition?"
  • "How do we exploit their weaknesses?"

We thought: What if AI could do this in seconds?

Inspired by Moneyball's data-driven approach to sports, we built ValoML — a tool that transforms raw match data into tactical intelligence that coaches actually need.

What it does

ValoML is an Automated Scouting Report Generator that:

🔍 Analyzes opponents — Processes 10-20 recent matches from GRID's official esports data

📊 Generates tactical reports — Map veto predictions, player threat profiles, weakness detection

🧠 Uses real ML — K-Means clustering classifies playstyles (e.g., "Aggressive Dual-Duelist" vs "Tactical Control")

💬 AI Chat Assistant — Ask tactical questions like "How do we beat Fnatic on Lotus?" and get data-backed answers

⚔️ Versus Mode — Head-to-head comparisons with win probability predictions

How we built it

Frontend: Next.js 14 with a military-style tactical HUD interface (Tailwind CSS, Framer Motion, Recharts)

Backend: FastAPI (Python) handling data processing, ML analysis, and API orchestration

Machine Learning:

  • K-Means clustering on agent compositions to classify team playstyles
  • Weakness scoring algorithm (0-100 exploitability index)
  • Loss correlation analysis to identify defeat patterns

AI/LLM: Groq's Llama 3.3 70B for natural language report generation and tactical chat

MLOps: MLflow for experiment tracking, Prometheus + Grafana for production monitoring

Data: Official GRID Esports API with smart caching

Challenges we faced

  1. GRID API Complexity — The JSON schemas are deeply nested. We built custom parsers to extract meaningful stats.

  2. Sparse Data Handling — Not all teams have equal match history. We implemented fallback mechanisms and weighted scoring.

  3. LLM Hallucination Prevention — We constrain Llama with structured prompts and validate outputs against real data.

  4. Real-time Performance — Generating reports in ~15 seconds required aggressive caching and async processing.

What we learned

  • How to build production-grade ML pipelines with proper experiment tracking
  • The importance of data quality over model complexity
  • That esports analytics is a goldmine for applied AI

What's next for ValoML

  • 🎯 Real-time live match integration
  • 📈 Time-series analysis for form prediction
  • 🌐 Expansion to League of Legends using GRID's LoL data

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