Sports analytics footage was converted into frame-accurate training data so computer vision teams could improve player detection and action models without building a large in-house labeling operation.
The challenge
The client needed frame-level labels across thousands of matches while keeping accuracy high enough for model training.
The work required sport-specific interpretation, fast feedback loops and a production team large enough to absorb heavy video volumes.
The approach
SBL configured a dedicated annotation team, defined sport-specific labeling rules and ran multi-level review before delivery.
The workflow combined bounding boxes, trajectory tracking and action segmentation so the data could feed model-improvement cycles.
Define
Align sport-specific labeling rules, model needs and review criteria.
Annotate
Label players, actions and trajectories through a trained production team.
Validate
Run quality checks and correction loops before model-ready delivery.
The result
The client received a scalable annotation operation for sports AI without carrying the full cost and management burden internally.
- Large match archives were converted into structured AI training data.
- Annotation quality was governed through multi-level review.
- The operating model scaled across multiple sports and data volumes.
- The client reduced dependence on an expensive in-house annotation setup.