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.

ATP Tennis dataset

Tis dataset provides information about all the ATP tennis match disputed since 2008. It also includes betting odds (prematch) and I think it is a good starting point for your analysts.

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.

Context

This dataset contains a large number of tennis matches from ATP and ITF leagues. Specifically, matches are available for ATP majors, masters, 500, 250, and challengers, as well as ITF procircuit and juniors. Doubles matches are also available (player_id field will have both names sorted and joined by an underscore ('_'). For example, a doubles team of Roger Federer and Novak Djokovic would appear as novak-djokovic_roger-feder in the player_id column of the all_matches.csv file. Location of players is also recorded, and standardized in a 3 character code. A country join table is also provided for convenience.

Betting data comes in 3 flavors: Money Line - predict winner of match Spread - predict whether the difference in games (or sometimes sets) between the players is greater (or less) than a certain number Totals - predict whether the total number of games (or sometimes sets) between the players is greater (or less) than a certain number

Acknowledgements

Pulled from ITF and ATP sources.

Inspiration

Can you write a consistent algorithm to that wins money in the tennis betting market?

The Sports Data API Interface market is booming, projected to reach $2.5 Billion in 2025 and grow at a 15% CAGR through 2033. This comprehensive analysis explores market drivers, trends, restraints, and key players in sports data APIs for esports, football, basketball, and more. Discover market insights and regional breakdowns for informed business decisions.

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 Sports Data API Interface market is booming, projected to reach $12B+ by 2033 (15% CAGR). This in-depth analysis covers market size, trends, key players (Sportradar, Genius Sports, etc.), and regional insights. Learn how esports, fantasy sports, and betting fuel this explosive growth.

ESports And Traditional Sports Betting Market size is growing at a faster pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2026 to 2032.

Global Esports And Traditional Sports Betting Market Drivers

Technological Progress: The market for sports betting is primarily driven by the quick development of technology. Accessing betting platforms has become simpler for customers because to the widespread use of smartphones and high-speed internet systems. Users can easily and conveniently make bets from anywhere at any time with the help of mobile apps and online betting websites. Furthermore, improvements in artificial intelligence and data analytics have improved the precision of odds and forecasts, drawing more players to the betting market.

Changes in Regulation and Legalization: The market has grown dramatically as sports betting has been made legal and regulated in several nations. A number of states have legalized marijuana in the US as a result of the Supreme Court's 2018 decision to repeal the Professional and Amateur Sports Protection Act (PASPA). This has made the betting environment more transparent and safe, which has increased engagement. In a similar vein, other nations are updating their gambling legislation to permit and control sports wagering, which is propelling the market's expansion.

Results for the men's ATP tour date back to January 2000, including Grand Slams, Masters Series, Masters Cup and International Series competitions. Historical head-to-head betting odds go back to 2001.

See here for more details about the metadata : http://www.tennis-data.co.uk/notes.txt

Source - http://www.tennis-data.co.uk/data.php

There is a lot you can do with this data set. The ultimate goal is obviously to predict the outcome of the game or to build an efficient betting strategy based on your model(s).

You can also

• compare the betting agencies (Bet365, Bet&Win, Ladbrokes, Pinnacles...) in terms of predictions quality or measure the progress of these betting agencies over the years
• discover if it's possible to predict specific events (3 sets match, retirement, walk over...) or the evolution of players.

Sports Betting Predictive Analysis Dataset

This comprehensive synthetic dataset contains 1,369 rows and 10 columns specifically designed for predictive modeling in sports betting analytics. The dataset provides a rich foundation for machine learning applications in the sports betting domain, featuring realistic match data across multiple sports with comprehensive betting odds, team information, and outcome predictions.

Dataset Overview Table

Key Applications

Machine Learning Use Cases

• Outcome Prediction Models: Train classification algorithms to predict match winners
• Odds Analysis: Analyze betting market efficiency and identify value bets
• Feature Engineering: Create derived features for advanced predictive models
• Model Validation: Compare predicted vs actual outcomes for performance metrics
• Risk Assessment: Evaluate betting strategy performance and risk management

Data Science Applications

• Exploratory Data Analysis: Understand patterns in sports betting markets
• Statistical Modeling: Build probabilistic models for outcome prediction
• Time Series Analysis: Analyze temporal trends in betting odds and outcomes
• Comparative Analysis: Study performance differences across sports and teams
• Visualization Projects: Create interactive dashboards for betting analytics

Research Applications

• Academic Research: Study sports betting market dynamics
• Algorithm Development: Test new machine learning approaches
• Benchmarking: Compare different predictive modeling techniques
• Educational Projects: Learn data science concepts with realistic data
• Portfolio Development: Demonstrate skills in sports analytics domain

Data Characteristics

Realistic Market Simulation

• Betting odds within industry-standard ranges (1.2 - 5.0)
• Sport-specific logic (draws only applicable for Football and Hockey)
• Strategic null value placement to simulate real-world data gaps
• Temporal consistency across 2-year historical period
• Unique match identifiers for easy reference and tracking

Comprehensive Coverage

• Multi-Sport Analysis: Five major sports for diverse modeling scenarios
• Balanced Distribution: Even representation across all sports categories
• Team Diversity: Unique city-based team names preventing data leakage
• Outcome Variety: Includes wins, losses, and draws where applicable
• Prediction Comparison: Both model predictions and actual results included

The men's professional tennis market, encompassing major tournaments like the Grand Slams (Wimbledon, US Open, Roland Garros, Australian Open), ATP Masters 1000 events, and other significant competitions, represents a substantial and dynamic global industry. While precise market size figures aren't provided, a reasonable estimate, considering the revenue generated by major tournaments and sponsorships, could place the 2025 market value between $3-4 billion USD. This estimate accounts for ticket sales, broadcasting rights, endorsements, sponsorships, merchandise sales, and hospitality packages. A Compound Annual Growth Rate (CAGR) of 5-7% over the forecast period (2025-2033) is plausible, driven by factors such as increasing global viewership, the rising popularity of tennis in emerging markets, and the continued growth of digital platforms for content delivery. Key growth drivers include strategic partnerships with media companies expanding broadcasting reach, increased investment in player development programs fostering new talent, and the rising popularity of fantasy sports and betting related to professional tennis. However, the market faces certain restraints. These include the concentration of revenue among a relatively small number of elite players and tournaments, the cyclical nature of the sport with dependence on star players’ performance and participation, and the potential impact of economic downturns on sponsorship and broadcasting revenue. Segmentation within the market includes tournament revenue, broadcasting rights, sponsorship deals, merchandise sales, and player endorsements. Major players shaping the landscape include governing bodies such as the International Tennis Federation (ITF) and the Association of Tennis Professionals (ATP), along with individual tournaments (US Open, Wimbledon, etc.) and major sponsors. Geographic growth will likely be driven by expanding markets in Asia and other developing regions where increased participation and media coverage are fostering increased interest in the sport. Further segmentation analysis would reveal valuable insights into the relative contribution of each revenue segment and geographical market.

Global Online Sports Betting Market size was USD 54.56 billion in 2023 and is grow to around USD 142.56 billion by 2032 with a CAGR of roughly 10.5%.

La taille du marché mondial des paris sportifs en ligne était de 54.56 milliards USD en 2023 et devrait atteindre environ 142.56 milliards USD d'ici 2032 avec un TCAC d'environ 10.5 %.

Der globale Markt für Online-Sportwetten hatte im Jahr 2023 ein Volumen von 54.56 Milliarden US-Dollar und wird bis 2032 auf rund 142.56 Milliarden US-Dollar wachsen, was einer durchschnittlichen jährlichen Wachstumsrate von etwa 10.5 % entspricht.

Racing and individual sports have experienced a dynamic period marked by high-profile athlete branding, evolving media landscapes and shifting fan engagement models. In recent years, individual athletes in sports such as tennis, golf and auto racing have captured significant attention for their on-field performances and their powerful personal brands that attract lucrative endorsement deals. NASCAR, tennis and golf increasingly leverage stars as promotion engines, leading to broader commercial appeal and off-field revenue streams. The proliferation of legalized online betting and the boom in mobile wagering platforms are fueling new audience participation, inviting fresh regulatory scrutiny and calls for enhanced integrity. Attendance remains mixed, with rising ticket prices pushing live experiences toward premium offerings and media consumption shifting toward digital and streaming platforms. Revenue has been expanding at a CAGR of 4.1% to $16.1 billion over the last few years, including a gain of 0.8% in 2025. Over the past few years, the industry has navigated growth and turbulence, but ultimately saw an uptick in profit. Major tournaments and marquee events in tennis and golf have hit new viewership highs, especially on the heels of captivating narratives and record-breaking athlete performances. Not all areas have experienced uniform growth; legacy motorsports like NASCAR have encountered headwinds in attracting younger viewers and some racing series have contended with eroding audiences. At the same time, increased disposable incomes, robust endorsement portfolios and dynamic pricing for event tickets have lifted revenues, especially among premium customers. The integration of technology in fan engagement and new betting products has driven innovation and introduced challenges, as regulatory bodies have struggled to keep pace with the industry’s rapid evolution and its expanding connections to online gambling. A growing focus on electric technology will reshape motorsports, as top racing series adopt more ambitious carbon strategies and integrate electric and hybrid vehicles to align with global environmental trends. Meanwhile, athlete-driven marketing and digital engagement aren’t likely to slow, with social media continuing to amplify athletes’ reach and sponsor value. Regulatory pressures around betting will intensify, prompting leagues and governing bodies to enhance compliance and data security. Concurrently, escalating tariffs on imports and core materials could squeeze smaller teams and accelerate supply chain realignment. Anchored by long-term media rights deals and a steadily globalizing fan base, individual sports will see rising commercial opportunities, even with a more fragmented, regulated and technologically complex landscape. Revenue is expected to climb at a CAGR of 1.2% to $15.4 billion over the years to 2030.