The global match data collection market is projected to grow from USD 940 million in 2023 to USD 3,530 million by 2033, at a CAGR of 16.7%. Growing adoption of data-driven decision-making in the sports industry, the increasing popularity of esports, and advancements in sensor technology are the primary factors driving the market growth. The use of match data allows teams, players, and coaches to gain insights into their performance, identify strengths and weaknesses, and make informed decisions. The market is segmented by type (sensor data, video data, and others), application (sports industry and esports), and region (North America, South America, Europe, Middle East & Africa, and Asia Pacific). North America is the largest market, followed by Europe. The Asia Pacific region is expected to witness the highest growth rate due to the increasing popularity of esports and the growing number of professional sports leagues in the region. Key players in the market include Opta, Sportradar, N3XT Sports, Sportsdata, OUTFORZ, KINEXON Sports, Stats Perform, Baidu Cloud, Bestdata, Gracenote, Genius Sports, Statscore, and Broadage.

Data does not appear to be open but is substantial.

Don't forget to upvote if you enjoy my work :)

Serie A Match Results Dataset (2020–2025) was created in response to community requests following the release of my LaLiga Match Results Dataset.

This dataset contains match-level results and performance stats from the Italian Serie A football league, covering seasons 2020 to 2025.

Source: Data was collected using a custom Python web scraper from FBref.com (https://fbref.com/en/comps/11/Serie-A-Stats).

Uses: - Match prediction models - Sports analytics - Feature engineering experiments - Educational ML datasets

Licensing Intended for educational and research use only. All rights remain with original data providers.

This dataset comprises comprehensive information from ranked matches played in the game League of Legends, spanning the time frame between January 12, 2023, and May 18, 2023. The matches cover a wide range of skill levels, specifically from the Iron tier to the Diamond tier.

The dataset is structured based on time intervals, presenting game data at various percentages of elapsed game time, including 20%, 40%, 60%, 80%, and 100%. For each interval, detailed match statistics, player performance metrics, objective control, gold distribution, and other vital in-game information are provided.

This collection of data not only offers insights into how matches evolve and strategies change over different phases of the game but also enables the exploration of player behavior and decision-making as matches progress. Researchers and analysts in the field of esports and game analytics will find this dataset valuable for studying trends, developing predictive models, and gaining a deeper understanding of the dynamics within ranked League of Legends matches across different skill tiers.

Context

Most publicly available football (soccer) statistics are limited to aggregated data such as Goals, Shots, Fouls, Cards. When assessing performance or building predictive models, this simple aggregation, without any context, can be misleading. For example, a team that produced 10 shots on target from long range has a lower chance of scoring than a club that produced the same amount of shots from inside the box. However, metrics derived from this simple count of shots will similarly asses the two teams.

A football game generates much more events and it is very important and interesting to take into account the context in which those events were generated. This dataset should keep sports analytics enthusiasts awake for long hours as the number of questions that can be asked is huge.

Content

This dataset is a result of a very tiresome effort of webscraping and integrating different data sources. The central element is the text commentary. All the events were derived by reverse engineering the text commentary, using regex. Using this, I was able to derive 11 types of events, as well as the main player and secondary player involved in those events and many other statistics. In case I've missed extracting some useful information, you are gladly invited to do so and share your findings. The dataset provides a granular view of 9,074 games, totaling 941,009 events from the biggest 5 European football (soccer) leagues: England, Spain, Germany, Italy, France from 2011/2012 season to 2016/2017 season as of 25.01.2017. There are games that have been played during these seasons for which I could not collect detailed data. Overall, over 90% of the played games during these seasons have event data.

The dataset is organized in 3 files:

• events.csv contains event data about each game. Text commentary was scraped from: bbc.com, espn.com and onefootball.com
• ginf.csv - contains metadata and market odds about each game. odds were collected from oddsportal.com
• dictionary.txt contains a dictionary with the textual description of each categorical variable coded with integers

Past Research

I have used this data to:

• create predictive models for football games in order to bet on football outcomes.
• make visualizations about upcoming games
• build expected goals models and compare players

Inspiration

There are tons of interesting questions a sports enthusiast can answer with this dataset. For example:

• What is the value of a shot? Or what is the probability of a shot being a goal given it's location, shooter, league, assist method, gamestate, number of players on the pitch, time - known as expected goals (xG) models
• When are teams more likely to score?
• Which teams are the best or sloppiest at holding the lead?
• Which teams or players make the best use of set pieces?
• In which leagues is the referee more likely to give a card?
• How do players compare when they shoot with their week foot versus strong foot? Or which players are ambidextrous?
• Identify different styles of plays (shooting from long range vs shooting from the box, crossing the ball vs passing the ball, use of headers)
• Which teams have a bias for attacking on a particular flank?

And many many more...

Context

I thought of consolidating and sharing this public data to see how the data science world uses it discover interesting patterns. The data has been collected from 2018 FIFA World Cup Russia Official App.

Content

The data will be updated after each match daily.

Note: On the column '1st Goal', any goal that was scored in the extra time will be denoted as 45 or 90 based on 1st or 2nd half of the game (ex. if 1st goal was scored in 45+2 mins then it will be mentioned as 45 instead of 47, likewise for the 2nd half)

Acknowledgements

Thanks to the FIFA 2018 World Cup App.

Inspiration

I thought of consolidating and sharing this public data to see how the data science world uses it discover interesting patterns. Can we predict the Man of the match award using this statistics before the official announcement that will be made right after the match?

Here are a few use cases for this project:

1. Real-time match analysis: The "Football" model can be used to provide real-time insights and statistics about the ongoing match, such as ball possession percentages, player movements, goal attempts, successful corner kicks, and identification of goalkeepers making crucial saves.

2. Automated highlight generation: By identifying critical events like goals, corners, and exceptional goalkeeper saves, the model can automatically create highlight reels of important moments in a football match, saving content creators and broadcasters significant editing effort.

3. Performance analytics for teams and coaching staff: The model can be used to analyze and quantify individual player performance and team dynamics during a match, providing valuable insights for coaching staff to optimize strategies, identify strengths and weaknesses, and enhance team performance.

4. Enhanced fan engagement: With its ability to identify various elements of a football match, the model can be used to develop interactive applications and augmented reality solutions that engage fans and provide them with additional information, such as player statistics, goal breakdowns, or immersive replays of key events.

5. Referee decision support: The model can be integrated into a decision support system for referees, assisting with offside calls or other contentious decisions by providing accurate information about the positions of the ball, players, and goalkeepers during critical moments.

This dataset was obtained as part of my project to rate player performances in a game and use it to model game outcomes. I was looking for an open dataset which included important in-game stats for players but couldn't find one. Hence I ended up scraping data myself. Subsequently, it has been successfully used to predict player performances in future games and build an optimum fantasy league team. I would be updating the dataset monthly to include newer games of the current season.

The dataset includes 2 JSON files. One of the files describes in-game match stats for every match of the past 4 seasons (current season included) like player touches, passes, shots, yellow cards, saves etc. Some of the stats are available as aggregate stats for the entire team and some of them are player specific. Second, file describes general match outcomes like the full time and half-time score etc.

Data snapshot --

{
  "1190174":{
    "13":{
      "team_details":{
        "team_id":"13",
        "team_name":"Arsenal",
        "team_rating":"7.30714285714286",
        "date":"11/08/2017"
      },
      "aggregate_stats":{
        "fk_foul_lost":"9",
        "won_contest":"16",
        "possession_percentage":"70",
        "total_throws":"21",
         .............
       },
      "Player_stats":{
        "Petr Cech":{
          "player_details":{
            "player_id":"6775",
            "player_name":"Petr Cech",
            "player_position_value":"1",
            "player_position_info":"GK",
            "player_rating":"5.78"
          },
          "Match_stats":{
            "good_high_claim":"1",
            "touches":"27",
            "total_tackle":"1",
            "total_pass":"20",
            "formation_place":"1",
            "accurate_pass":"16"
          },

This dataset could be used to predict player performances and how a particular player/team plays against another. Can a game outcome be modeled on the player composition of the participating teams? Are goals the most important factor that determines season outcomes or something other than historical goals be used to predict the future team performance in the league?

This dataset contains detailed ball-by-ball information from various cricket matches. It provides an in-depth view of match events, such as player performance, wickets, and scoring patterns, enabling analysis of team strategies, individual contributions, and overall match outcomes.

Dataset Features:

• Match ID: A unique identifier for each match.
• Date: The date on which the match was played.
• Venue: The stadium or location where the match took place.
• Bat First: The team that batted first in the match.
• Bat Second: The team that batted second in the match.
• Innings: The innings number (1 or 2) during the match.
• Over: The over in which the ball was bowled.
• Ball: The specific ball in the over.
• Batter: The player on strike facing the delivery.
• Non-Striker: The player at the non-striker's end.
• Bowler: The bowler delivers the ball.
• Batter Runs: The runs scored by the batter from a specific ball.
• Extra Runs: Additional runs awarded due to extras (integer value.).
• Runs From Ball: Total runs scored off the delivery, including extras.
• Ball Rebowled: Indicates whether the ball was re-bowled (Yes - 1/No - 0).
• Wicket: Indicates whether a wicket was taken (Yes - 1/No - 0).
• Method: Describes how the batter got out (e.g., bowled, caught, LBW).
• Player Out: The name of the player dismissed.
• Innings Runs: Total runs scored in the respective innings.
• Innings Wickets: Total wickets lost in the innings.
• Target Score: The score the batting team is chasing (if applicable).
• Runs to Get: Runs needed to win at that point in the match.
• Balls Remaining: Number of balls left in the innings.
• Winner: The team that won the match.
• Chased Successfully: Indicates whether the target was successfully chased (1 for Yes, 0 for No).

Usage:

This dataset is ideal for cricket analytics and machine learning tasks, including: - Analysing player and team performance trends. - Training predictive models for match outcomes. - Developing simulation tools for cricket strategy optimisation. - Identifying key moments and contributors in matches.

Coverage:

The dataset encompasses critical match and ball-level details, capturing the intricacies of cricket gameplay. It is suitable for exploring various analytical dimensions, such as player efficiency, bowling performance, and team tactics.

License:

CC0 (Public Domain)

Who can use it:

This dataset is designed for data scientists, sports analysts, machine learning practitioners, and cricket enthusiasts interested in leveraging data for sports analytics.

How to use it:

• Build predictive models for match outcomes and player performances.
• Analyse player contributions in different match contexts.
• Conduct exploratory data analysis on cricket match events.
• Simulate match scenarios to evaluate team strategies.

In the first quarter of 2025, the Match Group's total revenue was around 831 million U.S. dollars. Tinder, owned by Match, is the most downloaded dating app in the world.

Sports Game Data Software Market Outlook

The global sports game data software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 3.8 billion by 2032, exhibiting a CAGR of 13.2% during the forecast period. This robust growth can be attributed to the increasing digitization of sports, the expanding adoption of data analytics in sports management, and the rising demand for enhanced fan engagement solutions.

One of the primary growth factors driving the sports game data software market is the increasing reliance on data analytics to optimize team performance and strategy. Sports organizations and teams are increasingly using complex algorithms and data analytics tools to assess player performance, develop game strategies, and enhance overall team efficiency. Integrating data analytics enables teams to make informed decisions, reducing the margin for error and contributing to better performance outcomes. This transition to data-driven sports management is significantly boosting the demand for sports game data software.

Furthermore, the rising popularity of sports globally and the consequent increase in sports viewership are contributing to the expanding market for sports game data software. With more people engaging with sports events, there is a growing need for advanced software solutions that can enhance fan engagement by providing real-time data and interactive experiences. This trend is particularly prominent in regions such as North America and Europe, where sports events attract massive viewership, necessitating sophisticated fan engagement platforms to maintain and expand audience interest.

The developments in artificial intelligence (AI) and machine learning (ML) technologies are also pivotal in propelling the sports game data software market forward. These technologies enable the creation of advanced data analytics tools that can process vast amounts of data quickly and accurately. The integration of AI and ML in sports analytics not only helps in predicting player performance and game outcomes but also in developing personalized fan experiences. With continuous advancements in these technologies, the sports game data software market is poised for significant growth.

Cricket Analysis Software has emerged as a vital tool in the realm of sports analytics, particularly for cricket teams seeking to enhance their performance. This software leverages advanced data analytics to provide insights into player performance, game strategies, and opposition analysis. By analyzing historical data and real-time match statistics, cricket teams can develop more effective game plans and make informed decisions on the field. The integration of Cricket Analysis Software into team management processes not only aids in optimizing performance but also in identifying areas for improvement, thus contributing to the overall growth of the sports game data software market.

Regionally, North America currently holds the largest share of the market, driven by the presence of major sports leagues and the high adoption rate of advanced technologies in sports management. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the rising popularity of various sports, increasing investments in sports infrastructure, and the growing adoption of digital solutions. This regional growth is further supported by government initiatives promoting sports as a means to enhance health and fitness among the population.

Component Analysis

The sports game data software market can be segmented by component into software and services. The software segment dominates the market due to the rising demand for advanced analytics tools that can process and interpret large volumes of data. These software solutions provide critical insights that help sports teams and organizations make data-driven decisions. The software segment includes a variety of applications such as performance analysis tools, strategy development platforms, and fan engagement solutions, which are all essential for modern sports management.

In contrast, the services segment is also experiencing substantial growth, driven by the increasing need for professional services that support the implementation and maintenance of sports game data software. These services include consulting, training, and support services, which are crucial for ensuring the optimal use of data analy

This dataset provides detailed match events and summaries for over 300 English Premier League matches from the 2023/24 season. It captures the drama, tactics, and player performances, enabling users to gain new insights into sports analytics. The file includes transcripts of both the match summary and events, along with key details for each fixture.

Columns

• id: Unique identifier for each match.
• Home: The name of the home team.
• Away: The name of the away team.
• Date: The date the match was played.
• Stadium: The stadium where the match took place.
• Attendance: The recorded attendance for the match.
• Referee: The name of the match referee.
• events: Detailed minute-by-minute match events.
• summary: A summary of the match, often including key moments and player actions.

Distribution

The data file is typically provided in CSV format. It covers over 300 matches from the 2023/24 Premier League season. Specific numbers for rows or records are not available in the provided details, but the dataset offers event details for every minute played.

Usage

This dataset is ideal for various applications, including: * Summarisation tasks for football matches. * Text generation of match reports or commentary. * Sports analytics to uncover patterns in team or player performances. * Natural Language Processing (NLP) research related to sports commentary. * Studying tactical analyses and game flow.

Coverage

The dataset primarily focuses on the English Premier League. The time range for the matches is the 2023/24 season, specifically from 12th August 2023 to 15th April 2024. The data covers matches played across various stadiums in England, featuring all teams participating in the Premier League during this period.

License

CC-BY-NC

Who Can Use It

This dataset is intended for: * Sports analysts seeking to analyse team strategies and player statistics. * Researchers in NLP and text mining interested in sports commentary. * Developers building applications that require rich football match data. * Football enthusiasts looking for detailed insights into Premier League games. * Anyone interested in text-based sports data for academic or personal projects.

Dataset Name Suggestions

• English Premier League - Match Commentary
• Premier League Match Summaries & Events 2023/24
• EPL Football Commentary Dataset
• 2023/24 Premier League Match Transcripts
• English Football Match Data

Attributes

Original Data Source: English Premier League - Match Commentary

Data used for the paper entitled "Home advantage for tournament victory: Empirical evidence from FIFA Confederations and World Cups" Date Submitted: 2022-11-04

Dataset

This dataset was created by Akash

Released under Data files © Original Authors

Contents

Our NFL Data product offers extensive access to historic and current National Football League statistics and results, available in multiple formats. Whether you're a sports analyst, data scientist, fantasy football enthusiast, or a developer building sports-related apps, this dataset provides everything you need to dive deep into NFL performance insights.

Key Benefits:

Comprehensive Coverage: Includes historic and real-time data on NFL stats, game results, team performance, player metrics, and more.

Multiple Formats: Datasets are available in various formats (CSV, JSON, XML) for easy integration into your tools and applications.

User-Friendly Access: Whether you are an advanced analyst or a beginner, you can easily access and manipulate data to suit your needs.

Free Trial: Explore the full range of data with our free trial before committing, ensuring the product meets your expectations.

Customizable: Filter and download only the data you need, tailored to specific seasons, teams, or players.

API Access: Developers can integrate real-time NFL data into their apps with API support, allowing seamless updates and user engagement.

Use Cases:

Fantasy Football Players: Use the data to analyze player performance, helping to draft winning teams and make better game-day decisions.

Sports Analysts: Dive deep into historical and current NFL stats for research, articles, and game predictions.

Developers: Build custom sports apps and dashboards by integrating NFL data directly through API access.

Betting & Prediction Models: Use data to create accurate predictions for NFL games, helping sportsbooks and bettors alike.

Media Outlets: Enhance game previews, post-game analysis, and highlight reels with accurate, detailed NFL stats.

Our NFL Data product ensures you have the most reliable, up-to-date information to drive your projects, whether it's enhancing user experiences, creating predictive models, or simply enjoying in-depth football analysis.

Explore match day statistics of every game and every team during the 2021-2022 season of the English Premier League!

Data includes data, teams, referee, and stats by home and away side such as fouls, shots, cards, and more! Also included is a dataset of the weekly rankings for the season.

The 2021–22 Premier League was the 30th season of the Premier League, the top English professional league for association football clubs since its establishment in 1992, and the 123rd season of top-flight English football overall. The start and end dates for the season were released on 25 March 2021, and the fixtures were released on 16 June 2021.

Manchester City successfully defended their title, securing a sixth Premier League title and eighth English league title overall on the final day of the season; it was also the club's fourth title in the last five seasons.

The data was collected from the official website of the Premier League. I then cleaned the data using google sheets

This dataset describes all the matches made available. Each match is a document consisting of the following fields:- competitionId: the identifier of the competition to which the match belongs to. It is a integer and refers to the field "wyId" of the competition document;- date and dateutc: the former specifies date and time when the match starts in explicit format (e.g., May 20, 2018 at 8:45:00 PM GMT+2), the latter contains the same information but in the compact format YYYY-MM-DD hh:mm:ss; - duration: the duration of the match. It can be "Regular" (matches of regular duration of 90 minutes + stoppage time), "ExtraTime" (matches with supplementary times, as it may happen for matches in continental or international competitions), or "Penalities" (matches which end at penalty kicks, as it may happen for continental or international competitions);- gameweek: the week of the league, starting from the beginning of the league;- label: contains the name of the two clubs and the result of the match (e.g., "Lazio - Internazionale, 2 - 3");- roundID: indicates the match-day of the competition to which the match belongs to. During a competition for soccer clubs, each of the participating clubs plays against each of the other clubs twice, once at home and once away. The matches are organized in match-days: all the matches in match-day i are played before the matches in match-day i + 1, even tough some matches can be anticipated or postponed to facilitate players and clubs participating in Continental or Intercontinental competitions. During a competition for national teams, the "roundID" indicates the stage of the competition (eliminatory round, round of 16, quarter finals, semifinals, final);- seasonId: indicates the season of the match;- status: it can be "Played" (the match has officially finished), "Cancelled" (the match has been canceled for some reason), "Postponed" (the match has been postponed and no new date and time is available yet) or "Suspended" (the match has been suspended and no new date and time is available yet);- venue: the stadium where the match was held (e.g., "Stadio Olimpico");- winner: the identifier of the team which won the game, or 0 if the match ended with a draw;- wyId: the identifier of the match, assigned by Wyscout;- teamsData: it contains several subfields describing information about each team that is playing that match: such as lineup, bench composition, list of substitutions, coach and scores: - hasFormation: it has value 0 if no formation (lineups and benches) is present, and 1 otherwise; - score: the number of goals scored by the team during the match (not counting penalties); - scoreET: the number of goals scored by the team during the match, including the extra time (not counting penalties); - scoreHT: the number of goals scored by the team during the first half of the match; - scoreP: the total number of goals scored by the team after the penalties; - side: the team side in the match (it can be "home" or "away"); - teamId: the identifier of the team; - coachId: the identifier of the team's coach; - bench: the list of the team's players that started the match in the bench and some basic statistics about their performance during the match (goals, own goals, cards); - lineup: the list of the team's players in the starting lineup and some basic statistics about their performance during the match (goals, own goals, cards); - substitutions: the list of team's substitutions during the match, describing the players involved and the minute of the substitution.

Ball by ball data for cricket test matches between 1998 and 2006 inclusive in which a target was set for the team batting last. These are secondary data.

La Liga 2023/24: Match, Player, and Team Performance Insights

Dataset Description

This dataset provides an in-depth look at the 2023/24 La Liga season, covering various aspects of team and player performances across all matchdays. With over 50 individual CSV files, the dataset includes statistics on passing accuracy, goal-scoring, defensive actions, possession metrics, and player ratings, among others. Whether you're interested in analyzing top scorers, understanding team strengths, or delving into player-specific contributions, this dataset offers a rich foundation for football analytics enthusiasts and professionals.

In addition to the core dataset, we have now added more files related to the league table, expanding the dataset with essential information on match outcomes, league standings, and advanced metrics.

Contents

The dataset contains the following types of data:

• Team Performance Metrics: Information on accurate passes, crosses, goals conceded, interceptions, and other team stats.
• Player Performance Metrics: Individual stats including expected goals (xG), assists, clearances, fouls committed, and tackles won.
• Match-Specific Insights: Detailed metrics on goals scored, scoring attempts, possession percentages, and cards issued per match.
• Match Details (New): Information about rounds, match IDs, teams, scores, and match statuses.
• League Tables (New):
  • Overall standings including matches played, wins, draws, losses, goals scored, goal differences, and points.
  • Separate breakdowns for home and away performances.
  • Advanced metrics including expected goals (xG), expected goals conceded, and expected points.

The file details provide an overview of each dataset, including a brief description of the data structure and potential uses for analysis. This helps users quickly navigate and understand the data available for analysis.

This dataset is ideal for statistical analysis, data visualization, and machine learning applications to uncover patterns in football performance.

Suggested Analysis

This dataset opens up multiple avenues for data analysis and visualization. Here are some ideas:

1. Team Performance Analysis

• Analyze team performance trends, such as comparing passing accuracy, possession, and expected goals (xG) across teams.
• Visualize which teams generate the most scoring opportunities and miss the most big chances.
• Identify the strongest and weakest defenses based on goals conceded, clean sheets, and clearances.

2. Player Performance Analysis

• Identify top-performing players by goals scored, assists, expected goals, and expected assists.
• Explore defensive contributions by analyzing tackles won, interceptions, and clearances per player.
• Assess attacking efficiency by comparing total attempts vs. on-target attempts for each player.

3. Goalkeeping and Defensive Analysis

• Compare goalkeepers on metrics like saves made, goals conceded, and clean sheets to highlight the top performers of the season.
• Evaluate defensive strength by analyzing interception rates and clearances by both teams and players.

4. League Table Insights (New)

• Analyze overall league standings to determine team performance trends.
• Explore home and away performance and identify strengths and weaknesses in different scenarios.
• Utilize advanced metrics to evaluate under- and overperforming teams.

5. Advanced Metrics Exploration

• Examine possession-based metrics, such as possession percentage and possessions won in the attacking third, to identify possession-dominant teams.
• Use expected goals and expected assists data to build profiles highlighting efficient playmaking and finishing among players and teams.

This dataset is a valuable resource for football enthusiasts, data scientists, and analysts interested in uncovering patterns, building predictive models, or generating insights for La Liga 2023/24.

License and Disclaimer

License

This dataset is shared for non-commercial, educational, and personal analysis purposes only. It is not intended for redistribution, commercial use, or integration into other public datasets.

Disclaimer

This dataset was sourced from FotMob, a proprietary provider of football statistics. All rights to the original data belong to FotMob. The dataset is a restructured collection of publicly viewable data and does not claim ownership over FotMob's data. Users should reference FotMob as the original source when using this dataset for research or analysis.

Terms of Use

By using this dataset, you agree to the following: - Non-commercial Use: This dataset is only for educational, analytical, and personal use. It may not be used for commercial purposes or integrated into other public datasets. - Proper Attribution: Please attribu...

Match Group, a market leader in online dating platforms, has experienced a shift in its user base over the past two years. In the first quarter of 2025, the company reported approximately 14.2 million paid users. Despite a slight decline in paid users, Match Group's revenue has continued to grow. Regional distribution of paid subscribers Match Group's paid subscriber base is not evenly distributed across global regions. As of the second quarter of 2024, the Americas led with nearly seven million paying subscribers, followed by Europe with 4.4 million, and the Asia Pacific region with 3.6 million. This regional breakdown provides insight into where Match Group's services, including popular platforms like Tinder, Match.com, and OkCupid, are most widely adopted and monetized. Tinder's dominance in the dating app market Tinder, Match Group's flagship app, continues to dominate the dating app market. In November 2024, Tinder generated over 8.2 million downloads globally across the Apple App Store and Google Play Store. The app's popularity is further evidenced by its financial performance, as it was the highest-grossing dating app worldwide in 2024, generating approximately one billion U.S. dollars in revenue. This success underscores Tinder's significant contribution to Match Group's overall performance and its ability to monetize its user base effectively.