The Excel file contains:

Point-by-point data of singles matches at Wimbledon 1992-1995: 256 men's matches with 59,466 points, and 223 women's matches with 29,417 points; Match-level data of the same matches; Point-by-point data of three famous recent matches: Federer-Nadal, Clijsters-Williams, and Djokovic-Nadal.

Data is from 2000 Aus Open - 2019 US Open for only ATP atm. Message me if you know of good sources of women's data! Data was gathered by combining data from: - Jeff Sackmann (https://github.com/JeffSackmann) - Tennis-Data.co.uk (http://www.tennis-data.co.uk/) - Ultimate Tennis Statistics (https://www.ultimatetennisstatistics.com/)

Head to https://count.co/n/SZSMkTIP1Xf for more info about the data and a few starter queries.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1329595%2F90a882b915464b52a5c9264706e3e272%2FScreenshot%202019-09-18%20at%2011.52.30%20am.png?generation=1568803973578599&alt=media" alt="Players age when they made the top 10">

Men's Year-End ATP Rankings

A Global Perspective

By Granger Huntress [source]

About this dataset

This dataset provides a comprehensive look at the world of men's professional tennis throughout the Open Era. Every year, a new crop of tennis players has emerged to challenge long-standing traditions, while others have continued to maintain their place near the top. Through this dataset you will uncover which players succeeded in reaching or maintaining their ranking positions in the record books and how they navigated through changing eras in men’s professional tennis. Dive deep into what makes these successful athletes stand out from the rest and make impacts on their game year after year with an overview of invaluable data provided by this collection from first name, birthdate, country of origin, handedness, date range for records kept and more importantly their ATP career end rankings. Whether you are interested in a snapshot view to analyze long term trends or want to get inside insights on why top players succeed—this analysis provides invaluable resources to explore men's ATP rankings throughout its Open Era journey

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How to use the dataset

This dataset is a comprehensive source of men's year-end rankings during the Open Era. Each record includes information on the player's ranking, name, birthdate, country of origin, and handedness. This dataset can be used to study the trends in professional tennis throughout this time period and analyze how they have changed over time.

To use this dataset effectively one should first explore the data by reviewing some basic statistics. Examples include summary statistics such as total players by country or average ranking across years. Summarizing the data will help get a quick understanding of what the data is composed of and any existing patterns that may be present in it.

Another important step you can take before analyzing this data deeper would be to check for missing values or outliers within it that could affect your results if ignored or not handled appropriately. Having an understanding about any potential issues with your data like these can save you from potentially misinterpreting results due to an incomplete analysis process at some later point in time after further exploration and analysis has been done with it.

Once an overview of your dataset has been established and potential issues have been addressed it is now time to start conducting a more detailed exploration into what insights our data holds us answer questions related to Professional Tennis during this time period such as: How did various nations perform over different years? Who was consistently ranked among the top 10 players throughout this period? Any trends we see associated with handedness preference? etc… Answering questions like these properly requires finding appropriate ways analyze them given our available set up variables so keep that in mind when trying pin down connections between our variables using techniques like correlations, linear regression etc… In addition, visualizations can also help you make sense out of large amounts complex multivariable relationships which may exist between varying sets up parameters all at once so don't forget including those whenever possible! This way you'e able maximize accuracy when uncovering hidden intricacies regarding both individual components and holistic summary statistics for tennis rankings all over years covered within this open era range

Research Ideas

• Analyzing the global trends in men's tennis in the Open Era over time by examining shifts in countries represented at each year-end ranking.
• Examining the effectiveness of different opponents based on the nature of their hands (right or left) when compared to men's hand-edness throughout the Open Era.
• Tracking and predicting future player rankings based on birthdates, country, and other relevant factors that influence performance

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: ltdPlayerMaster.csv | Column name | Description | |:--------------|:---------------------------------------------------| | FIRST | First name of the player. (String) | | LAST | Last name of the player. (String) | | HAND | Handedness of the player (Right or Left). ...

In October 2019, the International Tennis Federation (ITF) published its first global report on tennis, based on data collected in 195 nations. The publication, analyzing figures from 2017, stated that there were about 87 million tennis players globally. As of the survey period, China registered the highest figure, as roughly 22.5 percent of all tennis players came from this country. The United States and India followed in the list, recording 20.7 percent and 9.2 percent respectively of all tennis players in the world.

Context

The WTA (Women's Tennis Association) is the principal organizing body of women's professional tennis, it governs its own tour worldwide. On its website, it provides a lot of data about the players as individuals as well the tour matches with results and the current rank during it.

Luckily for us, Jeff Sackmann scraped the website and collected everything from there and put in a nice way into easily consumable datasets.

On Jeff's GitHub account you can find a lot more data about tennis!

Content

The dataset present here is directly downloaded from the source, no alteration on the data was made, the files were only placed in subdirectories so one can easily locate them.

It covers statistics of players registered on the WTA, the matches that happened on each tour by year, with results, as well some qualifying matches for the tours.

As a reminder, you may not find all data of the matches prior to 2006, so be warned when working with those sets.

Acknowledgements

Thanks to Jeff Sackmann for maintaining such collection and making it public!

Also, a thank you for WTA for collecting those stats and making them accessible to anyone on their site.

Inspiration

Here are some things to start:

• Which player did the most rapidly climb the ranks through the years?
• Does the rank correlates with the money earn by the player?
• What can we find about the age?
• There is some deterministic factor to own the match?

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