LALIGA processes petabytes of stadium video data for real-time sports insights on Databricks
LALIGA used the Databricks Data + AI Platform on Azure, with Delta Lake and MLflow, to ingest 300,000 frames of in-game video per match from stadium cameras and build real-time machine learning models tracking player and ball movement for performance analysis and personalized fan experiences, later commercializing the platform as LALIGA Tech for other sports organizations.
Overview
LALIGA used the Databricks Data + AI Platform on Azure, with Delta Lake and MLflow, to ingest 300,000 frames of in-game video per match from stadium cameras and build real-time machine learning models tracking player and ball movement for performance analysis and personalized fan experiences, later commercializing the platform as LALIGA Tech for other sports organizations.
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Inspect the highlighted sourceThe challenge
LALIGA struggled to efficiently ingest the massive volumes of structured and unstructured live-action video data captured by stadium cameras and to extract insights that business stakeholders could understand and act on, limiting its ability to build a data-first culture.
The solution
LALIGA built its operational analytics platform on the Databricks Data + AI Platform on Azure, using Delta Lake as a common data layer to unify petabytes of video stream data and MLflow to build, train and deploy predictive models that track player and ball movement in real time, later commercializing the platform as LALIGA Tech.
Reported business value
LALIGA can now deliver reports and dashboards via Power BI to help business stakeholders make smarter decisions about team operations and revenue, analyze player performance, speed and health to inform in-game decisions, and personalize fan content experiences, opening new entertainment and e-commerce opportunities.
Sources
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