Sports Analytics: Tennis and Data Analysis

Introduction

In recent years, the world of tennis has undergone a real transformation thanks to data analysis (Sports Analytics). This discipline changed how the game is played, trained and managed, delivering valuable information that used to be invisible to players, coaches and spectators alike.

What started as a tool for reviewing controversial calls is today a technological ecosystem that measures every movement on the court. Thanks to a range of innovative solutions, detailed data now gets collected that lets us understand the game with a level of precision never seen before.

Key Technologies That Revolutionized Tennis

Here are some of the most influential technologies that marked a turning point in the sport:

Hawk-Eye
The Hawk-Eye system is a revolutionary ball-tracking technology used in tennis. First used at Wimbledon in 2006, it became an essential part of every major tournament in the world, including all four Grand Slams. Its accuracy relies on multiple high-speed cameras and algorithms that reconstruct the ball's trajectory in 3D in real time to enable fairer calls.

Motion Sensors and Wearable Devices
Players use wearables and sensors to measure serve speed, distance covered, heart rate, ball spin, and other indicators. This lets them analyze individual performance and fine-tune training with greater precision.

Tracking Cameras and Video Analysis
Cameras record the position and movement of players and the ball. Beyond supporting officiating decisions, they're used in training to assess mobility, court coverage and technique.

Data Analysis Software
Specialized programs process statistics to detect trends, compare players and support tactical decisions. Coaches and analysts plan matches based on evidence, not just intuition.

Real-Time Tracking Systems
Some tournaments display live metrics like serve speed, first-serve percentage or winners, improving the experience for players, coaching staff and fans alike.

Strategy

Data analysis has let us understand tennis from a whole new angle. Today, teams plan with precision and adapt their game based on data.

• Identifying patterns and trends: detects an opponent's and one's own tactics to prepare effective strategies.
• Real-time tactical personalization: lets you adjust decisions mid-match based on the opponent's behavior.
• Physical performance optimization: data helps manage energy, prevent fatigue and maximize recovery.

The result: smarter, more strategic and more competitive tennis.

Nutrition

Analysis has also evolved off the court, shaping diet, hydration and physical routines.

Novak Djokovic is an emblematic case. He analyzed his performance and his body's response to different foods, adjusted his diet (gluten-free and low in processed foods), and improved his energy, focus and recovery, marking a turning point in his career.

Psychology

Data also opened up a new frontier: the mental one. Behavior under pressure, decision-making and emotional management are now all analyzed.

• Mental Toughness: analyzes how a player responds at key points to train their mental resilience.
• Decision-Making Under Pressure: evaluates tie-breaks, break points and critical moments to train better decision-making.
• Emotional Management: detects emotional patterns and designs self-control techniques.
• Motivation and Confidence: uses data to reinforce strengths and set achievable goals.

Rafael Nadal is a world reference for psychological work applied to tennis. Part of his success comes from training his mind with the same discipline as his body.

Dataset

Now that we've seen how data transformed tennis, we can build a report that turns these metrics into useful insights for understanding players, surfaces and tournaments.

The dataset contains ATP men's matches from 1967 to 2023, with tournament, player and key statistics information, such as:

• tourney_name: Tournament
• surface: Surface
• winner_name / loser_name: Players
• score: Result
• minutes: Duration
• w_ace, w_df, w_1stWon, w_2ndWon... Winner's metrics
• l_ace, l_df, l_1stWon, l_2ndWon... Loser's metrics

KPIs

With this data, we can build indicators to understand behavior, comparisons and trends in professional tennis:

• Percentage of Valid First Serves
• Points Won on First and Second Serve
• Break Point Conversion Rate
• Average Aces per Match
• Average Match Duration
• Service Games Won
• Point Distribution by Surface
• Ranking Evolution
• Player Comparisons

And this is just the start: we could also analyze dominant hand, best-of-3 vs. best-of-5 sets, geographic context, match times, tournament pace, and other factors that affect performance.

Data Visualization