Bryson DeChambeau Uses Google Cloud Vision Models to Quantify Every Swing
Google Cloud’s deep learning pipeline ingests 2D and 3D video to track 30 skeletal, club, and ball keypoints through top-of-swing, impact, follow-through, and finish phases. The system outputs positional coordinates and outcome metrics that feed directly into swing-adjustment models.
Athletes stop relying on feel alone and start iterating on millimeter-level data. The workflow shifts from post-round video review to same-day algorithmic feedback loops.
Bryson DeChambeau partnered with Google Cloud to run these models on his practice footage, shortening swing diagnosis time from hours to minutes.
Step 1: Upload swing video to Google Cloud Vision API at https://cloud.google.com/vision. Step 2: Call the Pose Detection endpoint to extract the 30 keypoints and timestamps. Step 3: Export the JSON coordinates into a spreadsheet and calculate angle deltas between sessions to measure swing changes.