Tennis Big Data Analysis (with BigQuery and Colab)

This year I’ve been watching a lot of tennis matches and decided to dive into the data of current players to see what insights I could find. I used Google Cloud’s BigQuery to analyze tennis match data. I found a dataset on Github that contained historical ATP tennis match data. I loaded the csv files of 2024 players, rankings, and matches into BigQuery and used Google Colab to run SQL queries on the data and visualize the results.

The results were interesting! I will summarize the results below (see the Colab notebook for the full analysis):

• 91.6% of players are right-handed; 8.4% are left-handed
• The top 5 countries with the most tennis players are: USA, Australia, UK, Spain, then Germany
• What are the most common first names of tennis players? David, Daniel, John, Michael, and Peter
• Twice as many tournaments have been played on hard courts compared to clay courts this year
• Jannik Sinner has the highest win rate on hard courts; Sebastian Baez has the highest win rate on clay courts
• For Grand Slam matches, the average match winner is ranked 39; the average loser is ranked 83