Forecasting professional tennis players winning matches has a wide range of practical applications. We introduced a new approach to measure and combine strategic and psychological momentum using the entropy weight method and the analytic hierarchy process, and test its effectiveness. Using data from Wimbledon Championship 2023, we then constructed a support vector machine (SVM) model to predict the turning point and winner of each point, and we optimized it using particle swarm optimization (PSO). Our model achieved a significant level of accuracy (96.09\% turning point and 83.52\% predicting winner) and performs well in different courts and players. Furthermore, we compare its performance with commonly utilized predictive models, including ARIMA, LSTM and BP network, and find that our model exhibits higher accuracy than other existing models on predicting the point winner. Our research can be used to calculate odds in tennis matches and provide advice to coaches.

Here are a few use cases for this project:

1. "Sports Analysis": The "tennis-tracker" model could be used for detailed game analysis in broadcasting. Being able to differentiate between player-front, player-back, and ball are crucial elements for sports analysts to study player movements, strategies, and game patterns.

2. "Player Performance Evaluation": Coaches and trainers could use this model to assess players' performance during training or matches. The model's ability to identify players and tennis balls can be used for tracking player movement, speed, consistency, and accuracy, contributing to better training strategies.

3. "Automated Replay System": This model can be utilized for managing replays in live or recorded matches. It can quickly identify key moments or points of interest (like when a player hits the ball) to create automated highlights or checks for foul play.

4. "Augmented Reality Tennis Game": Game developers could use this model in the development of AR-based tennis games. The model could identify player and ball positions to create a realistic and interactive gaming experience.

5. "Crowd Control & Safety Management": During major tournaments, security staff can use this model to monitor crowd behavior. Distinguishing between players, balls, and spectators can help identify potential disruptions or emergencies. It can also ensure player safety, tracking unauthorized individuals entering the court.

Tennis Match Analysis: Players' Time and Events' Time

Analyzing Match Time, Surfaces, and Tournaments

By FiveThirtyEight [source]

About this dataset

This dataset contains data from a collection of professional tennis matches that took place between 2008 and 2019. It provides an in-depth analysis into the time played by each player, and factors such as tournament, surface and years taken into account. By utilizing this dataset, one can gain an interesting insight into the amount of time added to each point for every player in professional tennis match-ups. This data can then be used to analyze a variety of trends such as how increases or decreases in time spent per point affected a player's performance over different tournaments and surfaces during the period analysed. As such, we invite you to explore this dataset further – whether through your own stories or interactive visualizations – to uncover new patterns concerning the timing of play in professional tennis match-ups!

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How to use the dataset

This dataset provides information on how long tennis matches last when factoring in strokes, points, and rest periods. It also provides tournament-specific data on match length as well as surface-specific information.

This guide will provide an overview of the dataset and explain how you can use it to conduct thorough tennis match analysis.

Understanding the Data Columns

The dataset consists of a total of seven column features which are explained below:

• Player: The name of the player participating in the match. (String)

• Seconds Added per Point: The amount of time (in seconds) added to a match for each point played by a player. (Integer)

• Tournament: The name of the tournament where the match was played. (String)

• Surface: The type of court surface used for play during the match. There are three types included here – Hard, Clay, and Frictionless Court – with all other types falling under “Other”.(String)

• Years : The year in which the match was played out.(Integer).

  Analyzing Tennis Match Lengths with this Dataset

  Armed with an understanding of what this dataset tracks, we can begin our analysis by looking at different scenarios related to tennis players and tournaments over different surfaces or years. For example, you might want to investigate which tournament produces matches that have longer average lengths than others or what is making them last so long? Was it because more points were being scored compared to other matches? Or were it due simply due to number additions based on restart times after each point? You could even look into whether certain players tend towards longer or shorter matches than others when playing against any given opponent or under any particular conditions related to surfaces or tournaments over specific years etc.

Research Ideas

• Identifying players with the highest amount of playing time in certain tournaments, surface types or years to draw conclusions on the best performing players.
• Investigating any correlations between time added per point and the surface type of a match to determine if certain surfaces are faster or slower than others.
• Analyzing patterns of how different tournaments affect playing times across different player types and surfaces to better understand which tournament structures yield longer games per point/set/match

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: players_time.csv | Column name | Description | |:----------------------------|:--------------------------------------------------------| | player | The name of the player. (String) | | seconds_added_per_point | The amount of time added for each point played. (Float) |

File: events_time.csv | Column name | Description | |:----------------------------|:--------------------------------------------...

Tennis Player Detection

## Overview

Tennis Player Detection is a dataset for object detection tasks - it contains Tennis Players annotations for 1,323 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset contains tennis data from the ATP World Tour website. The data is updated annually in October. The data contains ATP tournaments, match scores, match stats, rankings and players overview. The latest available data is for 2017.

Here are a few use cases for this project:

1. Sports Analytics: The model can be used to analyze player movements, racquet swings, ball speed, and trajectory in real-time while a tennis match is ongoing. This can give valuable insights to the coaches and players to enhance their strategies.

2. Automated Broadcasting: It can be used to automate camera movements during broadcasting of tennis matches. The recognition of various classes can help the camera automatically identify and follow the most relevant action on the field.

3. Game Highlights: The model can be deployed to automatically generate match highlights by identifying key moments like a player hitting the ball, referee decision making moments, and ball boy fielding the balls.

4. Game Equipment Quality Assurance: Manufacturers of tennis equipment can use the model to automatically identify defects or inconsistencies in balls or racquets as they move along the production line.

5. Sports Training and Education: Trainers can utilize the model to analyze and educate athletes about their performance, considering factors such as their stance, the angle of the racquet during a shot, and the speed and spin of the ball.

This table shows the handedness distribution for male professional tennis players in different datasets and categories. The column “N/A” indicates the number of players whose handedness for playing tennis was not available.

The ATP Tour (known as the ATP World Tour from January 2009 until December 2018) is a worldwide top-tier tennis tour for men organized by the Association of Tennis Professionals. The second-tier tour is the ATP Challenger Tour and the third-tier is ITF Men's World Tennis Tour. The equivalent women's organisation is the WTA Tour.

The ATP Tour comprises ATP Masters 1000, ATP 500, and ATP 250.[1] The ATP also oversees the ATP Challenger Tour,[2] a level below the ATP Tour, and the ATP Champions Tour for seniors. Grand Slam tournaments, a small portion of the Olympic tennis tournament, the Davis Cup, and the entry-level ITF World Tennis Tour do not fall under the purview of the ATP, but are overseen by the ITF instead and the International Olympic Committee (IOC) for the Olympics. In these events, however, ATP ranking points are awarded, with the exception of the Olympics. The four-week ITF Satellite tournaments were discontinued in 2007. Players and doubles teams with the most ranking points (collected during the calendar year) play in the season-ending ATP Finals, which, from 2000–2008, was run jointly with the International Tennis Federation (ITF). The details of the professional tennis tour are:

Event Number Total prize money (USD) Winner's ranking points Governing body Grand Slam 4 See individual articles 2,000 ITF ATP Finals 1 4,450,000 1,100–1,500 ATP (2009–present) ATP Masters 1000 9 2,450,000 to 3,645,000 1000 ATP ATP 500 13 755,000 to 2,100,000 500 ATP ATP 250 39 416,000 to 1,024,000 250 ATP Olympics 1 See individual articles 0 IOC ATP Challenger Tour 178 40,000 to 220,000 80 to 125 ATP ITF Men's Circuit 534 10,000 and 25,000 18 to 35 ITF

The dataset is from Jeff Sackmann(https://github.com/JeffSackmann/tennis_atp)

Tennis Players Detection

## Overview

Tennis Players Detection is a dataset for object detection tasks - it contains Tennis Players annotations for 205 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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?

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">

This table shows the handedness distribution for female professional tennis players in different datasets and categories. The column “N/A” indicates the number of players whose handedness for playing tennis was not available.

Analysis of ‘Tennis Players Ranks Prediction Using ATP Elo’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/anupangadi/tennis-players-ranks-prediction-using-atp-elo on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

Given a Dataset has Elo ratings of worldwide players, with their respective Elos on different types of courts. Your Primary Objective is to Create A Rank Prdiction Model. Which can predict the Rank of Player Based on their Elo Rank.

--- Original source retains full ownership of the source dataset ---

Here are a few use cases for this project:

1. Sporting Events: The "Tennisball-Detection" model can be used in televised tennis matches, tracking the movement of the tennis ball in real-time, helping in line calls or in creating detailed post-game analysis including speed tracking and shot mapping.

2. Player Training Software: The model can be integrated into a training software to help tennis players improve their performance by analyzing their interactions with the tennis ball. It can track the spin, speed, bounce and trajectory of the ball, providing data-driven insights to players and coaches.

3. Automated Video Editing: The model can assist in the automatic creation of highlight reels from tennis matches. It could identify the moments where the tennis ball is in action, helping to cut and focus only on the key parts of a game.

4. Umpiring Assist Tool: The model could be used as a tool to assist umpires in making accurate and consistent line calls, particularly for lower-tier matches where hawk-eye technology is not available.

5. Recreation and Home Use: For hobbyists and amateur players, the model can be used in home tennis kits to track ball movements and provide feedback to improve their game. Also, in casual games, it can be used to solve disputes over whether the ball was in or out.

Tennis Match

## Overview

Tennis Match is a dataset for object detection tasks - it contains Tennis Balls Players annotations for 6,463 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset contains ranking, birth month and handedness of the female tennis players in 2007–2016. (XLSX)

Analysis of ‘Tennis Racquets specs’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/leoyuanluo/tennis-racquets-specs on 14 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This data is acquired when I was practicing data mining. I didn't find many patterns, but maybe you can.

Content

I used request_html library. Of course there are many other ways such as beautiful soup etc. The details of how I scrape the data is in the following link: https://leoyuanluo.medium.com/tennis-racquet-data-mining-d0a53f8fd18e

Acknowledgements

All the people who contribute to open source softwares and libraries such as sublime and requests_html.

Inspiration

I am a casual tennis player, but I know very little about tennis racquets. So I am curious about what drives the price of a good tennis racquet and honestly anything that's interesting.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘World Tennis Records and Rankings 2022’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/abhijeetbhilare/world-tennis-records-and-rankings-2022 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

Tennis is one of the famous sports in the world. Here we have data which comprises Tennis Records and Rankings dataset is updated till 31st Jan 2022.

Content

Dataset contains 2 files Records and Rankings of all tennis players till date.

Acknowledgements

I would like to thank https://www.ultimatetennisstatistics.com/ for this ultimate dataset

Inspiration

Lets take out some amazing analysis on this data.

--- Original source retains full ownership of the source dataset ---

Tennis Tracker AI

## Overview

Tennis Tracker AI is a dataset for object detection tasks - it contains Players Tennisball annotations for 414 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Phoenix Tennis Center is a full-service tennis facility that offers leagues, tournaments, reservable courts, professional instruction and a pro shop.​Pickleball is a paddle sport created for all ages and skill levels that is played on a court similar to a traditional tennis court. The rules are simple and the game is easy for beginners to learn, but can develop into a quick, fast-paced, competitive game for experienced players.