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🎾 Cracking the Code of Champions: What the Data Reveals About Top 10 Tennis Players

This case study investigates what separates the ATP Top 10 players from lower-ranked players by analyzing Grand Slam match data from 2000–2022. Using SQL (BigQuery) and Excel, I explored serve performance metrics across different ranking tiers and surfaces.

🔍 Key Insights

Top 50 players hit the most aces on average — more than the Top 10 — suggesting they rely on serve dominance to compete.
The Top 10 are more efficient and precise, with the best ace-to-double-fault ratio.
First and second serve win percentages show a clear ranking gradient: the higher the rank, the more consistently points are won.
Grass courts yield the highest serve stats across all ranks, while clay reduces serve impact.

📊 Tools Used

BigQuery (SQL): Data cleaning, transformation, aggregation.
Excel: Visualizations, pivot tables, formatting.
Kaggle ATP Dataset: 180K+ matches from 1968–2022, filtered to Grand Slams post-2000.

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📊 Key Visualizations

1st Serve Win Percentage 1st Serve Win %

2nd Serve Win Percentage 2nd Serve Win %

Ace to Double Fault Ratio Aces to df ratio

Average Aces Avg aces