Building a soccer coaching app on Databricks

Summary

Coach’s Corner, also called La Pizarra, turns high-frequency soccer tracking data into a bench-side application for replay, tactical analysis, scouting, standings, and agent-generated dossiers. Built as a Databricks App, it ingests NDJSON feeds at 25 frames per second through Auto Loader and Spark Declarative Pipelines, enforcing 46 data-quality expectations across bronze, silver, and gold layers. Liquid clustering supports 1–3-second DBSQL queries, while Lakebase synchronizes gold data to Postgres for millisecond replay reads and separates sequential playback from exploratory analytics. The scouting layer grounds Genie, Vector Search, a Unity Catalog-registered xG model, and an Agent Bricks supervisor in governed data, with Claude calls routed through the Unity AI Gateway, MLflow tracing, and a deterministic fallback. Together, these components are presented as a way to deliver traceable insights within seconds without forcing coaches to interpret raw tables or analysts to relay every result.

Takeaways

1. 1

   Spark Declarative Pipelines process bronze, silver, and gold data while enforcing 46 named data-quality expectations for replay, analytics, and AI consumers.

2. 2

   Lakebase serves sequential replay reads from synchronized Postgres tables at millisecond-level latency, while the Statement Execution API routes heavier exploratory analytics to SQL warehouses.

3. 3

   The scouting supervisor combines Genie, Vector Search, a registered xG model, and Claude; MLflow traces execution and a deterministic scripted fallback prevents the agent from dead-ending.