Coach AI

Inspiration VALORANT coaches spend countless hours manually reviewing VODs—pausing, rewinding, taking notes. Meanwhile, platforms like GRID Esports generate incredibly rich match data that mostly goes unused. We saw a gap: what if AI could do the tedious analysis instantly, freeing coaches to focus on strategy and player development? Coach AI was born from a simple idea—give every esports team access to a data scientist that never sleeps.

What it does Coach AI transforms raw VALORANT match data into actionable coaching insights in seconds. It provides:

Round-by-round tactical analysis with execution scores for utility usage, trades, and entries Automatic issue detection identifying 80+ potential problems per match (untraded deaths, economy mismanagement, rotation timing) Priority-ranked recommendations with expected impact (e.g., "Fixing A-site retake coordination could improve defense win rate by 15–20%") Player-level insights including strengths, weaknesses, and personalized drill suggestions Predictive analytics powered by ML models that forecast round outcomes based on game state One API call delivers a complete breakdown: 45 rounds analyzed, 100+ key moments flagged, and 5 strategic action items ready for the next practice session.

How we built it Backend: FastAPI (Python) for a fast, async API layer Frontend: React with Tailwind CSS for a clean, coach-friendly interface ML Pipeline: CatBoost and scikit-learn for round outcome prediction and impact modeling Data Ingestion: Custom parsers for GRID Esports API match files AI Integration: Gemini-powered chatbot for natural-language queries about match data The insights engine scores each round using a weighted formula across tactical dimensions, then aggregates patterns across games to surface what matters most.

Challenges we ran into Messy esports data – GRID events have inconsistent timestamps, nested states, and edge cases like pauses and remakes. Data cleaning took longer than expected. Balancing detail vs. usability – With 225+ player snapshots per match, we had to ruthlessly prioritize what coaches actually need. Making ML explainable – Coaches don't trust black boxes. We added confidence scores and plain-English explanations to every prediction. Performance at scale – Full match analysis needed to stay under 2 seconds. We optimized with pre-computed aggregates and lazy-loading. Accomplishments that we're proud of End-to-end pipeline: From raw GRID data to coach-ready insights in a single API call 80+ automated issue detections that would take a human analyst hours to identify ML models with real predictive power: Our round outcome classifier achieves meaningful accuracy using features coaches actually care about A UI coaches want to use: Simple, color-coded, with one-click drill recommendations—not a cluttered dashboard What we learned Domain expertise beats raw data: Our models improved dramatically when we added coaching-relevant features (utility economy, site control, trade timing) instead of just kills and deaths. Coaches think in narratives: "62% first-blood rate" means nothing without context. Framing insights as stories—"You're losing first contact on A-main 70% of the time on pistol rounds"—makes them actionable. Less is more: The best feature we built was filtering. Showing 5 prioritized recommendations beats showing 50 stats. What's next for Coach AI Live match analysis – Real-time insights streamed during scrims and official matches Video timestamp linking – Click an insight and jump directly to that moment in the VOD Multi-game support – Expand beyond VALORANT to CS2, League of Legends, and other titles Team comparison benchmarks – See how your stats stack up against pro teams and regional averages Voice assistant integration – Ask questions mid-review: "Show me every round we lost after winning pistol"