The Problem
Reliable sports prediction is a data-engineering problem long before it's a modeling one.
Raw sports-data feeds are deeply nested, split across separate endpoints and seasons, and inconsistent for postponed or cancelled games. Before any model can learn, a decade of that data has to be reconciled into a single, clean, leak-free table — one row per game.
Our Solution
We built an end-to-end pipeline that turns years of raw NBA data into a model-ready feature set.
Disciplined ingestion
We pulled roughly ten seasons of schedules, rankings, and team and player statistics from a sports-data API, snapshotting every response so the pipeline is fully reproducible and can be replayed without re-hitting a rate-limited service.
Clean, leak-free transformation
Nested feeds are flattened into a long format — each game represented from both the home and away perspective under a unified schema — with derived win/loss outcomes and home-court context. Postponed and cancelled games are handled explicitly rather than silently dropped, preserving schedule integrity.
Ready to score live
The resulting per-game feature table feeds outcome-prediction models and is structured to be re-scored as new games and rosters arrive.
Outcome
A decade of messy, multi-source NBA data becomes a single clean feature table that powers match-outcome prediction — with a reproducible, replayable pipeline built for live re-scoring as new games are played.
Tools & Technologies
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