Tennis Analytics

Master Data-Driven Tennis Analysis

Tennis analytics covering serve analysis, rally patterns, and match strategy optimization

Resources

32

Tutorials

0

Examples

6

Datasets

20

Metrics

Learning Paths

Foundations
2 Topics

Introduction to tennis analytics and data sources

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Serve Analytics
4 Topics

Analysis of serve performance and strategy

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Rally Analysis
3 Topics

Rally patterns and shot selection analysis

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Surface Analysis
4 Topics

Performance analytics across different court surfaces

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Grand Slam Analytics
5 Topics

Major tournament performance and patterns

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Player Evaluation
1 Topics

Advanced player performance metrics

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Doubles Strategy
3 Topics

Partnership metrics and doubles-specific analytics

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Match Strategy
4 Topics

Tactical analysis and match preparation

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Physical Performance
3 Topics

Movement, fatigue, and conditioning analytics

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Career Analytics
3 Topics

Long-term performance trends and projections

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Quick Start Guide

1
Learn Basics
Start with fundamental statistics
2
Advanced Metrics
Explore sport-specific analytics
3
Practice with Data
Use real datasets and examples
4
Apply Knowledge
Build your own analytics projects

Key Tennis Metrics

Net Points Won Net%

Points won when approaching the net. Good volleyers are 65%+. Variables: Points won at net divided by net approaches

\text{Net\%} = \frac{\text{Points Won at Net}}{\text{Net Approaches}} \times 100
Dominance Ratio DR

Ratio of serve points won to serve points conceded. Above 1.0 is winning. Variables: SPW% = Service Points Won, RPW% = Return Points Won

\text{DR} = \frac{\text{SPW\%}}{100 - \text{RPW\%}}
Performance Index PI

Simple sum of serve and return winning percentages minus 100. Positive is winning. Variables: SPW% = Service Points Won %, RPW% = Return Points Won %

\text{PI} = \text{SPW\%} + \text{RPW\%} - 100
Total Points Won TPW%

Overall point-winning percentage. 52%+ usually wins the match. Variables: All points won divided by all points played

\text{TPW\%} = \frac{\text{Total Points Won}}{\text{Total Points Played}} \times 100
Forced Error Percentage FE%

Errors forced from opponent through pressure. Measures offensive pressure. Variables: Forced errors by opponent divided by total points

\text{FE\%} = \frac{\text{Opponent Forced Errors}}{\text{Total Points}} \times 100
Unforced Errors UE

Errors made without significant pressure. Lower is better. Variables: Errors not attributed to opponent pressure

\text{UE} = \text{Errors not forced by opponent}
Winner to Unforced Error Ratio W/UE

Ratio of winners to unforced errors. Above 1.0 is positive, 1.5+ is excellent. Variables: Winners divided by Unforced Errors

\text{W/UE} = \frac{\text{Winners}}{\text{Unforced Errors}}
Winners W

Outright winning shots the opponent cannot touch. Measures offensive firepower. Variables: Count of unreturnable shots (excluding aces)

\text{W} = \text{Shots opponent cannot reach}
Break Points Converted BP Conv%

Percentage of break points converted. 40%+ is excellent. Variables: Break points won divided by total break point opportunities

\text{BP Conv\%} = \frac{\text{Break Points Won}}{\text{Break Point Opportunities}} \times 100
First Serve Return Points Won 1st Ret%

Points won when facing first serve. Good returners are 30%+. Variables: Points won against opponent first serves divided by first serves faced

\text{1st Ret\%} = \frac{\text{Points Won vs 1st Serve}}{\text{1st Serves Faced}} \times 100

Datasets & Resources

Dataset Description Format Size Action
ATP Tennis Rankings & Stats Historical ATP player rankings, match results, and statistics from 1968 to present. CSV 100 MB
WTA Tennis Statistics Women's tennis match results, rankings, and player statistics from WTA Tour. CSV 80 MB
Grand Slam Match Data Detailed match statistics from Australian Open, French Open, Wimbledon, and US Open. CSV 150 MB
Tennis Abstract Match Charting Point-by-point match charting data including serve placement, shot types, and rally lengths. CSV 200 MB
Elo Tennis Rankings Elo-based tennis player ratings and surface-specific rankings over time. CSV 25 MB

Ready to Master Tennis Analytics?

Start with the basics and work your way up to advanced machine learning applications.

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