As of June 2024, 27 percent of adults in the United States spent one to five hours per week playing video games. A further 10 percent of respondents stated that they spent 11 to 15 hours on video gaming in an average week. Overall, 19 percent of respondents were non-gamers and did not spend any time on video games.

In 2024, the gaming hardware segment of the games market achieved the highest average revenue per user worldwide, reaching approximately 1,705.65 U.S. dollars. The mobile games segment ranked second with an average revenue per user of about 59.27 U.S. dollars, while in-game advertising followed with around 58.16 U.S. dollars.

Data Description - gender - birth year - gamer : if the respondent has played video games in the past few week - League of Legends : yes or no - Rocket League : yes or no - Valorant : yes or no - Fortnite : yes or no - Minecraft : yes or no - Genshin Impact : yes or no - The Sims : yes or no - game hours : less than 1 hour, 1-5 hours, 5-20 hours, 20-50 hours, more than 50 hours - romantic : single or in a relationship - go out : on a scale from 1 to 4 (1 = never going out, 4 = always going out)

Dataset collected through a questionnaire distributed on Discord groups.

A September to November 2024 survey of UK adults aged 16 years and older found that 85 percent of respondents aged between 16 and 24 years played video games, down from a 92 percent gaming penetration among the same age group in 2020. Overall, gaming uptake increased in 2020 due to the global COVID-19 pandemic. Video games industry in the UK In 2023, consumers in the United Kingdom spent over 3.16 billion British pounds on digital and online games. In regard to the annual contribution of the video games industry to the UK Gross Domestic Product, it amounted to 3.68 billion British pounds in 2023. Mobile games The popularity of mobile gaming increased over the last decade and overtook games consoles and TV for the first time in 2014. As of 2024, approximately 34 percent of UK adults reported playing games on their mobile phone, the highest rate ever reported. The most downloaded gaming apps in the UK include puzzle and casual titles such as Supermarket Simulator and Block Blast.

Discover key mobile games statistics, including user growth, revenue trends, top genres, engagement metrics, and global market share!

Overview:

This dataset captures comprehensive metrics and demographics related to player behavior in online gaming environments. It includes variables such as player demographics, game-specific details, engagement metrics, and a target variable reflecting player retention.

Features:

• PlayerID: Unique identifier for each player.
• Age: Age of the player.
• Gender: Gender of the player.
• Location: Geographic location of the player.
• GameGenre: Genre of the game the player is engaged in.
• PlayTimeHours: Average hours spent playing per session.
• InGamePurchases: Indicates whether the player makes in-game purchases (0 = No, 1 = Yes).
• GameDifficulty: Difficulty level of the game.
• SessionsPerWeek: Number of gaming sessions per week.
• AvgSessionDurationMinutes: Average duration of each gaming session in minutes.
• PlayerLevel: Current level of the player in the game.
• AchievementsUnlocked: Number of achievements unlocked by the player.
• EngagementLevel: Categorized engagement level reflecting player retention ('High', 'Medium', 'Low').

Target Variable:

• EngagementLevel: Indicates the level of player engagement categorized as 'High', 'Medium', or 'Low'.

Potential Applications:

• Predictive modeling of player retention and engagement patterns.
• Analysis of factors influencing player behavior and game performance.
• Optimization of game design, marketing strategies, and player experience enhancements.

Usage:

This dataset is suitable for exploring patterns in online gaming behavior, developing machine learning models for player engagement prediction, and conducting research in gaming analytics.

Dataset Usage and Attribution Notice:

This dataset, shared by Rabie El Kharoua, is original and has never been shared before. It is made available under the CC BY 4.0 license, allowing anyone to use the dataset in any form as long as proper citation is given to the author. A DOI is provided for proper referencing. Please note that duplication of this work within Kaggle is not permitted.

Exclusive Synthetic Dataset

This dataset is synthetic and was generated for educational purposes, making it ideal for data science and machine learning projects. It is an original dataset, owned by Mr. Rabie El Kharoua, and has not been previously shared. You are free to use it under the license outlined on the data card. The dataset is offered without any guarantees. Details about the data provider will be shared soon.

Games are the most popular app category on both app stores, accounting for about 59 percent of the consumer spending on app and 37 percent of all app downloads. Inside of the gaming category, there...

The Rea Games dataset provides valuable insights into the gaming industry, offering detailed data on game sales, player demographics, and user engagement. This dataset is designed for exploratory data analysis (EDA) to uncover patterns and trends within the gaming sector, helping analysts, game developers, and industry researchers better understand player behavior, revenue drivers, and popular game genres.

Dataset Overview:

Game Sales: Detailed information on sales figures for various games, allowing for revenue analysis and trend identification. Player Demographics: Data on player age, gender, and region, enabling segmentation and targeted analysis. User Engagement Metrics: Information on player engagement levels, such as playtime, frequency, and in-game purchases, providing insights into player retention and monetization strategies. Game Categories: Classification of games by genre, platform, and popularity to identify trends across different segments of the gaming market. Columns:

game_id: Unique identifier for each game in the dataset. game_title: Name of the game. genre: Game genre/category (e.g., action, adventure, simulation). platform: Platform on which the game is available (e.g., PC, console, mobile). release_date: Release date of the game. sales: Sales figures for each game, either in units or revenue. player_age: Age of the player engaging with the game. player_gender: Gender of the player (e.g., Male, Female). region: Geographic region where the game is popular or sold. playtime: Average playtime or engagement hours per player. in_game_purchases: Amount or count of in-game purchases made by players. Possible Use Cases:

Sales Trend Analysis: Track sales trends over time to identify popular games and profitable genres. Player Demographic Analysis: Segment players by age, gender, and location for targeted marketing. User Engagement Analysis: Explore player engagement metrics to improve retention and in-game purchases. Genre Popularity: Analyze which game genres are most popular across different platforms and demographics. This dataset is a valuable resource for data scientists, marketers, and game developers who want to make data-driven decisions in the gaming industry. By examining sales, demographics, and engagement metrics, users can gain insights to optimize game development, marketing strategies, and monetization efforts.

Unlock top Cross‑Platform Gaming Statistics to understand player habits, device shifts, and growth insights that boost your strategic edge.

Introduction

Cloud Gaming Statistics: Cloud gaming, game streaming, or cloud-based gaming. It represents a transformative shift in the gaming industry. They are utilizing cloud computing infrastructure to deliver video games directly to players via the internet.

Unlike traditional gaming, where games are locally processed, cloud gaming leverages remote data centers. Executing games on powerful servers and streaming video and audio outputs to players' devices in real-time.

With advancements in infrastructure, streaming technology, and input methods. Cloud gaming has evolved from early experiments to mainstream adoption, attracting tech giants like Google and Microsoft.

It offers increased accessibility, cross-platform play, and scalability. Fundamentally reshaping how we engage with and access video games while impacting the broader gaming landscape.

When asked about "Hours spent on playing video games per week", 30 percent of U.S. respondents answer "1 to 5 hours". This online survey was conducted in 2025, among 15,497 consumers. Looking to gain valuable insights about video gamers worldwide? Check out our reports on customers of video game stores and subscription services. These reports give readers a thorough picture of the people who use these platforms, including their identities, preferences, opinions, and methods of communication.

Video Games Sales Dataset

About Dataset

This Dataset provides up-to-date information on the sales performance and popularity of various video games worldwide. The data includes the name, platform, year of release, genre, publisher, and sales in North America, Europe, Japan, and other regions. It also features scores and ratings from both critics and users, including average critic score, number of critics reviewed, average user score, number of users reviewed, developer, and rating. This comprehensive and essential dataset offers valuable insights into the global video game market and is a must-have tool for gamers, industry professionals, and market researchers. by source

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Columns

Research Ideas / Data Use

• Market Analysis: The video game sales data can be used to analyze market trends and identify popular genres, platforms, and publishers. This can be useful for industry professionals to make informed decisions about game development and marketing strategies.
• Sales Forecasting: The sales data can be used to forecast future trends and predict the success of upcoming games.
• Consumer Insights: The data can be analyzed to gain insights into consumer preferences and buying habits, which can be used to tailor marketing strategies and improve customer satisfaction.
• Comparison of Competitors: The data can be used to compare the sales performance of competing video games and identify market leaders.
• Gaming Industry Performance: The data can be used to evaluate the overall performance of the gaming industry and track its growth over time.
• Gaming Popularity by Region: The data can be analyzed to determine which regions are the largest markets for video games and which genres are most popular in each region.
• Impact of Reviews: The data can be used to study the impact of critic and user reviews on sales and the relationship between scores and sales performance.
• Gaming Trends over Time: The data can be used to identify trends in the gaming industry over time and to track the evolution of the market.
• Gaming Demographics: The data can be used to analyze the demographic makeup of the gaming audience, including age, gender, and income.
• Impact of Gaming Industry on the Economy: The data can be used to evaluate the impact of the gaming industry on the economy and to assess its contribution to job creation and economic growth.

Acknowledgements

if this dataset was used in your work or studies, please credit the original source Please Credit ↑ ⠀⠀⠀

As of March 2025, 26 percent of video gamers in the United States were aged 30 to 39 years, making this age group the biggest gamer demographic. Additionally, 24 percent of U.S. gaming audiences were aged 20 to 29 years.

🎮🧠 Online Gaming Behavior Insights Dataset

1. Overview

The Online Gaming Behavior Insights Dataset provides a comprehensive view of player behavior patterns across different demographics, game genres, and engagement levels. This dataset is designed to support exploratory data analysis, behavioral analytics, and predictive modeling related to online gaming habits. It captures how players interact with games in terms of playtime, session frequency, difficulty preference, progression, and engagement intensity.

With over 40,000 player records, this dataset is suitable for both beginner and advanced data science tasks, including visualization, clustering, classification, and behavioral trend analysis.

2. Dataset Description

The dataset contains 40,034 unique players, each represented by 13 features describing demographic attributes, gaming preferences, activity patterns, and engagement outcomes. It reflects realistic gaming scenarios where player behavior varies by age, gender, location, game genre, and difficulty level.

Each row represents a single player profile, summarizing their average gaming behavior over a defined observation period.

3. Feature Details

Below is a detailed explanation of each column:

• PlayerID: Unique identifier assigned to each player
• Age: Age of the player (numeric)
• Gender: Gender of the player (Male / Female)
• Location: Geographical region of the player (e.g., USA, Europe, Other)
• GameGenre: Preferred game genre (Action, Strategy, Sports, etc.)
• PlayTimeHours: Total hours spent playing games
• InGamePurchases: Indicates whether the player made in-game purchases (0 = No, 1 = Yes)
• GameDifficulty: Preferred difficulty level (Easy, Medium, Hard)
• SessionsPerWeek: Average number of gaming sessions per week
• AvgSessionDurationMinutes: Average duration of each gaming session in minutes
• PlayerLevel: Current progression level of the player
• AchievementsUnlocked: Total number of achievements unlocked by the player
• EngagementLevel: Overall engagement classification (Low, Medium, High)

4. Key Insights & Observations

• Players with higher session frequency and longer session durations tend to fall into the High Engagement category.
• Action and Strategy genres show higher average playtime compared to other genres.
• Players with higher player levels and more achievements unlocked demonstrate stronger engagement patterns.
• In-game purchases are more common among players with medium to high engagement, suggesting monetization correlation with activity intensity.
• Younger players tend to have more frequent sessions, while older players show longer average session durations.
• Game difficulty preference plays a role in engagement, with Medium and Hard difficulties often associated with higher progression levels.

5. Results & Use Cases

This dataset can be effectively used for: - Player engagement prediction
- Behavioral segmentation and clustering
- Game design optimization analysis
- Monetization and in-game purchase behavior modeling
- Exploratory data analysis and visualization projects
- Machine learning classification (e.g., predicting engagement level)

6. Conclusion

The Online Gaming Behavior Insights Dataset offers a rich and realistic representation of player behavior in modern online games. Its balanced combination of demographic, behavioral, and performance-related features makes it highly valuable for data science experiments, academic research, and industry-level analytics.

Whether you are exploring gaming trends, building predictive models, or creating compelling visual insights, this dataset provides a strong foundation for meaningful analysis.

Discover how Gen Z gaming platform preferences boost social connection, drive smarter spending, and shape gameplay across devices

This dataset provides an extensive collection of popular video games across various platforms and genres. It includes key details such as game titles, genres, platforms, release years, and user ratings, allowing for in-depth analysis of gaming trends and player preferences.

With a diverse selection of games, this dataset reflects current industry trends, helping researchers, developers, and gaming enthusiasts explore popularity patterns, platform preferences, and genre evolution over time.

📌 Key Features: 🕹️ Game Information: Game Title 🎮: The name of the game. Genre 🎭: The category of the game (e.g., Action, RPG, FPS, Strategy). Platform 🖥️🎮📱: The platform(s) the game is available on (e.g., PC, PlayStation, Xbox, Switch, Mobile). Release Year 📅: The year the game was released. Developer 🏢: The company or studio that developed the game. ⭐ Popularity & Reception: User Ratings 🌟: Average player ratings based on reviews. Metacritic Score 🎯: Professional critic score from Metacritic (if applicable). Total Sales (in millions) 💰: Estimated number of copies sold worldwide. 🎮 Additional Insights: Multiplayer Support 🔄: Whether the game offers multiplayer features (Yes/No). Game Mode 🎯: Single-player, Multiplayer, or Both. ESRB Rating 🔞: Age classification (E, T, M, etc.). 📊 Use Cases & Applications: 🔹 Analyzing gaming trends over time 🔹 Comparing the popularity of different game genres 🔹 Identifying top-selling games by platform 🔹 Understanding user preferences and ratings 🔹 Exploring relationships between game ratings and sales

⚠️ Important Note: This dataset is synthetically generated for educational and analytical purposes. It does not contain real proprietary data but is designed to resemble real-world gaming statistics.

The education level was matched between the addiction group (education range: 12–13 years) and control group (education range: 12–13 years). The more detailed information, which was described as Mean ± SD (range: min-max), can be found in the table.

When asked about "Most used devices for video games", most Chinese respondents pick "Smartphone" as an answer. 72 percent did so in our online survey in 2025.

Explore PC Gaming Market Share and Usage Statistics for crisp data, clear insights and actionable trends driving the industry’s growth.

Introduction

Video Games Statistics: Video games have transformed into a worldwide cultural sensation, captivating diverse audiences across generations.

The industry has seen the emergence of numerous titles boasting exceptional sales, solidifying their status as all-time best-selling video games.

Beyond revenue generation, these games have wielded enduring influence over pop culture and entertainment engagement.

The sales landscape has shifted from physical editions to digital dissemination. Platforms like consoles, PCs, and mobile devices are substantially impacting sales dynamics.