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Endigest AI Core Summary
This post describes how baseball teams use Databricks to convert high-fidelity pitch data into game decisions through AI agents and a governed data lakehouse.
•Analysts use Genie on Unity Catalog to query Statcast tables with natural language, generating hitter meeting one-pagers with pitch mix and location tendencies by hand and base-runner state
•A Multi-Agent Supervisor built with Agent Bricks and deployed on Model Serving simulates bullpen matchup scenarios, calling UC SQL functions to compare each reliever's arsenal against specific hitter pockets
•Pinch-hit decisions are pre-planned using the same agent framework, ranking bench bats by expected outcome against likely late-inning relievers before the game starts
•Vector Search indexes pitcher embeddings in Unity Catalog to find comparable pitchers when direct Statcast head-to-head history is thin, enabling advance scouting reports
•Lakebase Postgres backs the baseball ops app, keeping scout reports, coach tags, and GM decisio
This summary was automatically generated by AI based on the original article and may not be fully accurate.