Computer Vision Pipeline for Competitive Tennis
How Tech Stack Playbook engineered a multi-stage computer vision pipeline that transforms match footage into biomechanical analysis — pose estimation, distance calibration, shot classification, and annotated video.
Overview
A software-only pipeline that runs on a laptop against standard phone video — transforming footage into the kind of biomechanical analysis that previously required a sports science lab. Detect the player, estimate 33 body landmarks in real-time, calibrate to real-world distances, classify shot types and phases, and render annotated video output.
Born from a real need: analyzing serve mechanics and forehand technique during a competitive tennis comeback. Every feature exists because it solved an actual training problem.
The Gap in Tennis Analytics
Competitive players invest in coaching, but the feedback loop between playing and improving is limited by real-time observation. No accessible tool converts standard video into quantitative biomechanical analysis.
- Human coaching can't measure joint angles or track movement in centimeters
- Slow-motion replay provides no measurement, annotation, or structured data
- Commercial motion capture requires $10K+ studios and reflective markers
- Consumer apps like SwingVision offer shot counting but no true biomechanical analysis
- No tool produces skeleton tracking, distance measurements, and shot phase classification from phone video
Six-Stage Pipeline
Each stage builds on the output of the previous — modular architecture enabling independent development, testing, and improvement.
