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CourtCheck 
AI-powered tennis analytics by Aggie Sports Analytics
See every shot. Know every move.
Tennis has always been played on instinct. CourtCheck changes that.
Every game day, UC Davis tennis coaches face 6 singles matches worth of footage to review. A thorough analysis of each video takes hours, time that coaches and players simply don't have.
| ⏱️ Before CourtCheck | ⚡ After CourtCheck |
|---|---|
| 20+ hours of film review per week | 5-minute read per match |
| Manually scrubbing through footage | Instant heatmaps, stats & AI reports |
| Easy to miss key patterns | Every shot, bounce & position tracked |
No sensors, no special equipment, no setup. Just upload your video.
🎯 What It Does
🎾 Ball & Bounce Tracking Frame-by-frame ball trajectory with precise bounce event detection across the full court.
🗺️ Player Heatmaps Visual court coverage maps that reveal positioning patterns, movement tendencies, and court dominance.
📊 Stroke & Shot Analysis Automatic shot classification with percentages and breakdowns across a full match.
🤖 AI Scouting Reports GPT-powered match summaries that synthesize player positioning and ball data into actionable coaching insights.
📈 Match & Opponent Stats Aggregated performance trends across multiple matches with head-to-head opponent breakdowns.
🎥 Recordings Library Upload, manage, and replay processed match videos, all in one place.
⚙️ How It Works
- 📤 Upload a match recording through the dashboard
- 🧠 CourtCheck processes the video using GPU-accelerated computer vision models
- 📋 View your analytics in minutes: heatmaps, shot charts, stats, and scouting report
🛠️ Built With
- 👁️ Computer Vision: YOLOv8, OpenCV, PyTorch, CatBoost
- 🤖 AI: OpenAI GPT-4o-mini
- 🌐 Frontend: Next.js, TypeScript, Tailwind CSS
- ⚙️ Backend: Python, FastAPI, Modal (A10G GPU)
- ☁️ Infrastructure: Vercel, Supabase
Built by Aggie Sports Analytics at UC Davis. 🐮
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$ claude mcp add CourtCheck \
-- python -m otcore.mcp_server <graph>