This dataset contains all match results and bets for all tennis tournaments. ATP Men´s Tour from 2000 till 2023. WTA Women´s Tour from 2007 till 2023.

Original Data

Data Dictionary

Key to results data:

ATP = Tournament number (men) WTA = Tournament number (women) Location = Venue of tournament Tournament = Name of tounament (including sponsor if relevant) Data = Date of match (note: prior to 2003 the date shown for all matches played in a single tournament is the start date) Series = Name of ATP tennis series (Grand Slam, Masters, International or International Gold) Tier = Tier (tournament ranking) of WTA tennis series. Court = Type of court (outdoors or indoors) Surface = Type of surface (clay, hard, carpet or grass) Round = Round of match Best of = Maximum number of sets playable in match Winner = Match winner Loser = Match loser WRank = ATP Entry ranking of the match winner as of the start of the tournament LRank = ATP Entry ranking of the match loser as of the start of the tournament WPts = ATP Entry points of the match winner as of the start of the tournament LPts = ATP Entry points of the match loser as of the start of the tournament W1 = Number of games won in 1st set by match winner L1 = Number of games won in 1st set by match loser W2 = Number of games won in 2nd set by match winner L2 = Number of games won in 2nd set by match loser W3 = Number of games won in 3rd set by match winner L3 = Number of games won in 3rd set by match loser W4 = Number of games won in 4th set by match winner L4 = Number of games won in 4th set by match loser W5 = Number of games won in 5th set by match winner L5 = Number of games won in 5th set by match loser Wsets = Number of sets won by match winner Lsets = Number of sets won by match loser Comment = Comment on the match (Completed, won through retirement of loser, or via Walkover)

Key to match betting odds data:

B365W = Bet365 odds of match winner B365L = Bet365 odds of match loser B&WW = Bet&Win odds of match winner B&WL = Bet&Win odds of match loser CBW = Centrebet odds of match winner CBL = Centrebet odds of match loser EXW = Expekt odds of match winner EXL = Expekt odds of match loser LBW = Ladbrokes odds of match winner LBL = Ladbrokes odds of match loser GBW = Gamebookers odds of match winner GBL = Gamebookers odds of match loser IWW = Interwetten odds of match winner IWL = Interwetten odds of match loser PSW = Pinnacles Sports odds of match winner PSL = Pinnacles Sports odds of match loser SBW = Sportingbet odds of match winner SBL = Sportingbet odds of match loser SJW = Stan James odds of match winner SJL = Stan James odds of match loser UBW = Unibet odds of match winner UBL = Unibet odds of match loser

MaxW= Maximum odds of match winner (as shown by Oddsportal.com) MaxL= Maximum odds of match loser (as shown by Oddsportal.com) AvgW= Average odds of match winner (as shown by Oddsportal.com) AvgL= Average odds of match loser (as shown by Oddsportal.com)

Acknowlegement

Foto von Matthias David auf Unsplash

Tennis-Data would like to acknowledge the following sources which are currently utilised in the compilation of Tennis-Data's results and odds files.

Results: Xscores - http://www.xscores.com/ ATPtennis.com - http://www.atptennis.com/ ATP Tour Rankings and Results Page - http://www.stevegtennis.com/ Livescore - http://www.livescore.net/

Rankings: ATPtennis.com - http://www.atptennis.com/ ATP Tour Rankings and Results Page - http://www.stevegtennis.com/ WTA TOur Rankings - http://www.sonyericssonwtatour.com

Betting odds for matches generally represent the most recent before play starts, as reported by oddsportal.com and the individual bookmakers.

The dataset contains year-, gender- and gram-slam-wise historically compiled data on the tennis grand slam singles winners and runners, both men and women, since the year 1877. The specifics of data contained in the dataset include the names and nationality of winners and runners, grand slam types such US Open, Australian Open, Wimbledon, French Open, etc., the score in each set, and others.

1) Data Introduction • The A large Tennis dataset for ATP and ITF betting contains the results of various singles and doubles tennis matches between the ATP and ITF leagues, as well as court_surface information.

2) Data Utilization (1) A large Tennis dataset for ATP and ITF betting has characteristics that: • This dataset provides a variety of details related to tennis matches, including players, match dates, competitions, match results, and type of court surface. (2) A large Tennis dataset for ATP and ITF betting can be used to: • Analysis of win rates by court type: You can analyze the differences in the results of matches depending on the surface of the court, such as hard, clay, or grass, by player or competition. • Establishing a betting strategy: Based on court surface, player characteristics, and match history data, it can be used to predict results of tennis matches and develop betting strategies.

This dataset represents the first large-scale, fully annotated dataset of padel, a rapidly expanding sport, featuring comprehensive tracking of ball trajectories, pose estimation, and recognition of shot events. The dataset comprises approximately 100,000 frames extracted from two matches at the 2022 World Padel Tour (WPT) Finals. To enhance data quality, these frames were captured using a single camera angle that minimizes occlusions, accompanied by detailed annotations at the frame level, including ball positions, player poses, and shot event information.

The included Jupyter notebook demonstrates the practical application of the dataset.

The data set contains a single Excel file with LINCE PLUS records of Twenty-one women’s doubles matches during three Tennis Grand Slams

Context

I collect recent tweets about Emma Raducanu, winner of women US Open 2021. The teen Brit with Romanian and Chinese parents, born in Canada and arrived in Great Britain at 2 years old, stormed the US event from qualifiers, playing 10 games without losing one single set (20 sets won in a row). She is the first British woman to win a Grand Slam since 1977 (Virginia Wade), the first women in US Open history to win the event from the qualifiers. She jumped more than 120 points in the ranking to land on 23rd position. She is also a very good student, landing A grades in mathematics and economy (her preferred domains) in her selective grammar school from south London.

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Data collection

The data is collected using tweepy Python package to access Twitter API. I use a relevant search term for the topic (#EmmaRaducanu).

Data collection frequency

The data is collected continuously using a script that collects a small number of recent tweets (using Twitter API and tweepy). The dataset obtained at each sampling time step is merged with current (or previously collected) dataset and stored dataset in csv format is saved on disk. Once or several times per day the currently accumulated dataset is uploaded on Kaggle as a new version of the tweets dataset.

Inspiration

You can perform multiple operations on the tweets about this British teen with meteoric ascension at US Open 2021. Here are few possible suggestions:

• Study the subjects of recent tweets about the new US Open champion or about tennis;
• Perform various NLP tasks on this data source (topic modelling, sentiment analysis);
• Can you identify tweets about tennis women, British athletes or US Open?
• Follow the trends in the news about tennis, US Open or Emma;
• Perform sentiment analysis on the tweets corpus but also split on topics, countries etc.
• Study the hashtags (associated to the tweets) distribution.

Tennis Courts FCC. Published by Fingal County Council. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).This is a listing and mapping of public tennis courts within the Public Park/Local Parks within Fingal County Council Council.“Disclaimer”This maybe the Local Park and not the exact location of the piece of equipment as they can be in various locations within the Public Spaces/Public Parks. ...