### 4.1 Analytics Readiness The tennis data is structurally strong for analytics, with an overall score of 95% and completeness of 95% across five tables. However, no validated joins were detected between the three large match-level tables (raw_kaggle, return_kaggle, serve_kaggle) and the two dimension tables (players_tournament_man_, players_man_), so serve, return, and raw performance cannot yet be analyzed together per player. The 8 explicit dimension hierarchies support aggregation by player and tournament, but no ML opportunity is rated high viability, so investment should be staged. ### 4.2 Strategic ML Opportunities | Model Type | Prediction Target | Viability | Applicable Tables | |------------|-------------------|-----------|-------------------| | Time-Series Forecasting | Future values of a measure over time (e.g. 2SP, 1SP, Aces) | Medium | raw_kaggle, return_kaggle, serve_kaggle | | Anomaly Detection | Outlier or unusual records (e.g. 2SP, 1SP, Aces) | Medium | raw_kaggle, return_kaggle, serve_kaggle | | Regression | Continuous target (e.g. 2SP, 1SP, Aces) | Medium | raw_kaggle, return_kaggle, serve_kaggle | Regression offers the best near-term return. Modeling a continuous measure such as Aces or 2SP (second-serve points won) from first-serve (1SP) and related serve statistics would show which skills most drive performance, which supports scouting, match preparation, and opponent analysis. Because the three match tables differ slightly in row count (237,205, 237,196, and 237,185), the team should first confirm that the records line up before combining them. Standardizing the performance columns would also help. ### 4.3 Investment Recommendations - **Within 30 days:** Establish a reliable player-and-match key across raw_kaggle, return_kaggle, and serve_kaggle, and reconcile the gap of up to 20 rows between them. This is the main blocker to combined serve and return modeling. - **Within 60 days:** Build a baseline regression on serve_kaggle (target: Aces or 2SP, with 1SP as a predictor) and compare it against a simple average benchmark to confirm the model adds value. - **Within 90 days:** Enrich match records with players_man_ and players_tournament_man_ attributes, then use the 5 proposed engineered features to add player and tournament context. - **Ongoing:** Run anomaly detection on Aces, 1SP, and 2SP to flag implausible values before they reach models or reports. Defer time-series forecasting until date or sequence fields are confirmed.