Computer Vision for Sports Analytics

6,000+ hours of sports footage annotated at 98%+ accuracy for AI-driven performance analytics. A sports analytics company developing AI models for player detection, movement tracking and performance analysis required large-scale, frame-accurate annotation support across multiple sports datasets. Existing in-house and freelancer-led operations struggled to maintain consistency, scalability and cost efficiency across growing volumes of match footage.

Scalable sports video annotation workflows improved AI analytics accuracy, accelerated training cycles and reduced operational costs across multi-sport datasets significantly.

Industry: Sports & Media
Service: Generative AI, Workflow Automation

6,000+: Video hours annotated
98%+: Annotation accuracy maintained
−70%: Cost vs in-house operations

Challenge

The client required high-precision annotation across soccer, basketball and American football datasets involving more than 3,900 matches. Frame-by-frame player detection, trajectory tracking and action segmentation demanded continuous iteration and rapid delivery cycles to support ongoing AI model improvement. Existing annotation workflows were costly, inconsistent and difficult to scale under increasing dataset volumes.

Approach

SBL Infotech deployed MMS — its Managed Media Services platform — to govern the complete sports annotation workflow from secure footage intake through quality control and model-ready delivery. A dedicated team of 125 annotation specialists executed bounding-box labeling, player trajectory tracking and action segmentation across high-volume match datasets. Structured workflows, flexible annotation rules and multi-level quality validation ensured accuracy remained above 98% across all deliverables. Continuous coordination with the client’s AI teams enabled faster feedback loops and rapid deployment of training-ready datasets through cloud-based delivery pipelines.

Outcome

The client transformed fragmented sports annotation operations into a scalable AI data production pipeline capable of supporting continuous model improvement across multiple sports environments. Faster delivery cycles accelerated training workflows while improved annotation consistency enhanced player detection and analytics performance. The engagement reduced operational costs by 70%, improved AI model accuracy by 22% and established a repeatable annotation framework capable of scaling across future sports datasets, leagues and performance analysis initiatives.

IX Case studies

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