Tennis Match Predictor

Overview

A data-and-model project for tennis match outcomes, following the same pattern as the NBA work: ingestion scripts pull match data, feature engineering builds per-player rolling features, and a trained model predicts the winner and margin. No UI yet; everything runs from scripts. Structured the same way as nba-stats so the tooling is reusable across sports.

Highlights

• Per-player rolling feature engineering (recent form, surface splits, head-to-head).
• Shared ingestion pattern with nba-stats so adding a new sport is structural, not bespoke.
• Script-driven workflow: ingest, train, evaluate, predict, all CLI-invoked.
• Kept local while the feature set stabilizes.

Challenges and approach

• Tennis match data is sparser per player than team sports. Bootstrapping per-surface features from rolling windows, rather than season averages, cut variance without losing signal.
• Reusing the nba-stats ingestion skeleton forced a cleaner generic layer, since tennis entities (players, matches, surfaces) do not map one-to-one onto team-sport entities.