This dataset contains a list of video games with sales greater than 100,000 copies. It was generated by a scrape of vgchartz.com.

Fields include

• Rank - Ranking of overall sales

• Name - The games name

• Platform - Platform of the games release (i.e. PC,PS4, etc.)

• Year - Year of the game's release

• Genre - Genre of the game

• Publisher - Publisher of the game

• NA_Sales - Sales in North America (in millions)

• EU_Sales - Sales in Europe (in millions)

• JP_Sales - Sales in Japan (in millions)

• Other_Sales - Sales in the rest of the world (in millions)

• Global_Sales - Total worldwide sales.

The script to scrape the data is available at https://github.com/GregorUT/vgchartzScrape. It is based on BeautifulSoup using Python. There are 16,598 records. 2 records were dropped due to incomplete information.

In July 2025, total video games sales in the United States amounted to **** billion U.S. dollars, representing a five percent year-over-year increase. Generally speaking, the video game industry has its most important months in November and December, as video game software and hardware make very popular Christmas gifts. In December 2024, total U.S. video game sales surpassed **** billion U.S. dollars. Birth of the video game industry Although the largest regional market in terms of sales, as well as number of gamers, is Asia Pacific, the United States is also an important player within the global video games industry. In fact, many consider the United States as the birthplace of gaming as we know it today, fueled by the arcade game fever in the ’60s and the introduction of the first personal computers and home gaming consoles in the ‘70s. Furthermore, the children of those eras are the game developers and game players of today, the ones who have driven the movement for better software solutions, better graphics, better sound and more advanced interaction not only for video games, but also for computers and communication technologies of today. An ever-changing market However, the video game industry in the United States is not only growing, it is also changing in many ways. Due to increased internet accessibility and development of technologies, more and more players are switching from single-player console or PC video games towards multiplayer games, as well as social networking games and last, but not least, mobile games, which are gaining tremendous popularity around the world. This can be evidenced in the fact that mobile games accounted for ** percent of the revenue of the games market worldwide, ahead of both console games and downloaded or boxed PC games.

Global Video Game Sales and Reviews

Global Video Game Performance: Sales, Reviews, and Rankings

By Andy Bramwell [source]

About this dataset

The elements covered in this well-curated dataset include: The ranking of the game based on global sales under the column 'Rank'. This metric provides perspective on how popular or successful a particular game has been across countries in comparison to others during its time. Noting that video games' popularity could vary greatly from one geography to another due to factors like cultural nuances, gamer preferences, etc., regional sales have been marked separately for North America (North America), Europe (Europe), Japan (Japan) as well as for other parts of the World excluding these three regions under the column 'Rest of World'.

For easy identification among massive chunks of data, we've included each game's title (Game Title) along with additional categorization based on their genre (Genre). From action-packed adventures to strategic board-like scenarios or enchanted magic realms - classifications cover it all! In addition, detailed information about publishers can be found under 'Publisher', which grants insights about leading companies dominating market shares.

Further details expand into mentioning platforms such as PS4, Xbox, PC where these games can be played under 'Platform'. A unique attribute covered in this database is ‘Review’. Given that critique ratings play an influential role in engaging new players into trying out a particular video game or boosting existing user morale regarding their choice; this numeric representation ranging typically from 1-10 vividly captures public opinion about them.

Lastly, just for keeping tabs on ever-evolving gaming technology standards where newer versions often outshine predecessors irrespective of actual gameplay quality itself; having release years mentioned ('Year') proves beneficial for categorizing them chronologically. This helps correlate whether higher sales figures can sometimes merely be indicative of more people having access to necessary high-end gaming hardware during later periods.

In essence, this dataset titled ‘Video Games Sales.csv’ holds immense potential for informative deep-dives into the Video Game industry's trends and paradigms, forming a solid foundation for market research, academic purposes or personal projects

How to use the dataset

This dataset provides extensive information about various video game titles, their sales performance across multiple regions, publisher details and game reviews. Follow the steps outlined below to make the most out of this remarkable dataset!

1. Game Research & Evaluation:

With columns such as 'Game Title', 'Genre' and 'Review', you can research on particular games or genres that interest you. You can evaluate a game based on its review scores, delving into what makes a top-rated game.

2. Publisher Analysis:

The 'Publisher' column lets you track which publishers are behind the most successful games in terms of sales and reviews. This analysis could be useful for people interested in business trends in gaming industry or trying to identify potential innovative publishers.

3. Regional Market Trend Identification:

You can use data from columns like ‘North America’, ‘Europe’, ‘Japan’ and ‘Rest of World’ to study regional market trends for certain genres or platforms; it might enable one to recognize patterns over time or cultural preferences with regard to video games.

4. Global Sales Analysis:

Using the 'Global' column, you could observe which games have been globally successful, going beyond regional preferences by genre or platform.

5. Platform Insight:

The platform on which a particular game is available is another significant factor (e.g., PC, PS4, Xbox). By utilizing the data contained in this dataset regarding platforms, one may learn how platform choice impacts global sales as well as discern any correlation between preferred platform types among specific regions.

Remember that every statistical analysis begins with knowing your data - dive deep into each variable; explore patterns within variables before looking at correlations between different fields.

Don't forget - when engaged with comprehensive datasets like these - creativity is your only limit! Happy analyzing!

Research Ideas

• Trend Analysis: This dataset can be used to analyze the trends in video game preferences over the years based on genre, publisher, platform and region. It can provide interesting insights into how consumer tastes have evolved with time and which game genres are becoming more popular.
• Sales Forecasting: U...

Includes 24 hour recall data that children were instructed to fill-out describing the previous day’s activities at baseline, weeks 2 and 4 of the intervention, after the intervention (6 weeks), and after washout (10 weeks). Includes accelerometer data using an ActiGraph to assess usual physical and sedentary activity at baseline, 6 weeks, and 10 weeks. Includes demographic data such as weight, height, gender, race, ethnicity, and birth year. Includes relative reinforcing value data showing how children rated how much they would want to perform both physical and sedentary activities on a scale of 1-10 at baseline, week 6, and week 10. Includes questionnaire data regarding exercise self-efficacy using the Children’s Self-Perceptions of Adequacy in and Predilection of Physical Activity Scale (CSAPPA), motivation for physical activity using the Behavioral Regulations in Exercise Questionnaire, 2nd edition (BREQ-2), motivation for active video games using modified questions from the BREQ-2 so that the question refers to motivation towards active video games rather than physical activity, motivation for sedentary video games using modified questions from the BREQ-2 so that the question refers to motivation towards sedentary video games behavior rather than physical activity, and physical activity-related parenting behaviors using The Activity Support Scale for Multiple Groups (ACTS-MG). Resources in this dataset:Resource Title: 24 Hour Recall Data. File Name: 24 hour recalldata.xlsxResource Description: Children were instructed to fill out questions describing the previous day's activities at baseline, week 2, and week 4 of the intervention, after the intervention (6 weeks), and after washout (10 weeks).Resource Title: Actigraph activity data. File Name: actigraph activity data.xlsxResource Description: Accelerometer data using an ActiGraph to assess usual physical and sedentary activity at baseline, 6 weeks, and 10 weeks.Resource Title: Liking Data. File Name: liking data.xlsxResource Description: Relative reinforcing value data showing how children rated how much they would want to perform both physical and sedentary activities on a scale of 1-10 at baseline, week 6, and week 10.Resource Title: Demographics. File Name: Demographics (Birthdate-Year).xlsxResource Description: Includes demographic data such as weight, height, gender, race, ethnicity, and year of birth.Resource Title: Questionnaires. File Name: questionnaires.xlsxResource Description: Questionnaire data regarding exercise self-efficacy using the Children's Self-Perceptions of Adequacy in and Predilection of Physical Activity Scale (CSAPPA), motivation for physical activity using the Behavioral Regulations in Exercise Questionnaire, 2nd edition (BREQ-2), motivation for active video games using modified questions from the BREQ-2 so that the question refers to motivation towards active video games rather than physical activity, motivation for sedentary video games using modified questions from the BREQ-2 so that the question refers to motivation towards sedentary video games behavior rather than physical activity, and physical activity-related parenting behaviors using The Activity Support Scale for Multiple Groups (ACTS-MG).

Violent Video Games and their relationship with Aggressive Behaviour 🎮

This data was collected from students to find out, how much time they spend playing video games, especially violent video games, and whether this affects their behavior.

The dataset is already depersonalized, which means sensitive information, (PII) is taken out to protect privacy.

The columns which are our concern are "How much time do you play "violent" video games specifically?" which we will set as the independent variable and the other 29 Questions (columns 11 till 40, 0 being the first column 'Timestamp').

These are 29 questions from the Buss-Perry Aggression Scale, set on the Likert Scale. They can be coded as:

'Strongly disagree': 1, 'Disagree': 2, 'Neither agree nor disagree': 3, 'Agree': 4, 'Strongly agree': 5

By default the dataset does not have these codes, rather we have to replace them. Then we will add all these columns to check the overall aggression level.

Minimum total can possibly be 1 x 29 = 29 (least aggressive) Maximum total can possibly be 5 x 29 = 145 (most aggressive)

After coding, we will make a column of the sum of the total codes. We will call this column 'BPAQ (Buss-Perry Aggression Questionnaire) Score', and consider it as the dependent variable.

We are trying to establish a relationship between The Time of Violent Video Gameplay and the Level of Aggression, for which the indicators are discussed above, and see if it is significant.

but we have to see whether ANOVA suits it or Linear Regression, You can visit the code section and follow my notebook, or find your own insights.

General video gaming use among the U.S. population increased significantly during the COVID-19 pandemic. Between May and December 2020, U.S. teens aged 15 to 19 years spent an average of 112.8 daily minutes on playing games and using computers for leisure, up from 73.8 minutes per day in the corresponding period of 2019. In 2024, the daily time spent on such activities among this age group decreased to 78.6 minutes per day.

Discovering Hidden Trends in Global Video Games Sales

Platforms, Genres, and Profitable Regions

By Andy Bramwell [source]

About this dataset

This dataset contains sales data for video games from all around the world, across different platforms, genres and regions. From the thought-provoking latest release of RPGs to the thrilling adventures of racing games, this database provides an insight into what constitutes as a hit game in today’s gaming industry. Armed with this data and analysis, future developers can better understand what types of gameplay and mechanics resonate more with players to create a new gaming experience. Through its comprehensive analysis on various game titles, genres and platforms this dataset displays detailed insights into how video games can achieve global success as well as providing a wonderful window into the ever-changing trends of gaming culture

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Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to uncover hidden trends in Global Video Games Sales. To make the most of this data, it is important to understand the different columns and their respective values.

The 'Rank' column identifies each game's ranking according to its global sales (highest to lowest). This can help you identify which games are most popular globally. The 'Game Title' column contains the name of each video game, which allows you to easily discern one entry from another. The 'Platform' column lists the type of platform on which each game was released, e.g., PlayStation 4 or Xbox One, so that you can make comparisons between platforms as well as specific games for each platform. The 'Year' column provides an additional way of making year-on-year comparisons and tracking changes over time in global video game sales.
In addition, this dataset also contains metadata such as genre ('Genre'), publisher ('Publisher'), and review score ('Review') that add context when considering a particular title's performance in terms of global sales rankings. For example, it might be more compelling to compare two similar genres than two disparate ones when analyzing how successful a select set of titles have been at generating revenue in comparison with others released globally within that timeline. Lastly but no less important are the three variables dedicated exclusively for geographic breakdowns: North America ('North America'), Europe (Europe), Japan (Japan), Rest of World (Rest of World), and Global (Global). This allows us to see how certain regions contribute individually or collectively towards a given title's overall sales figures; by comparing these metrics regionally or collectively an interesting picture arises -- from which inferences about consumer preferences and supplier priorities emerge!

Overall this powerful dataset allows researchers and marketers alike a deep dive into market performance for those persistent questions about demand patterns across demographics around the world!

Research Ideas

• Analyzing the effects of genre and platform on a game's success - By comparing different genres and platforms, one can get a better understanding of what type of games have the highest sales in different regions across the globe. This could help developers decide which type of gaming content to create in order to maximize their profits.
• Tracking changes in global video games trends over time - This dataset could be used to analyze how various elements such as genre or platform affect success over various years, allowing developers an inside look into what kind of videos are being favored at any given moment across the world.
• Identifying highly successful games and their key elements- Developers could look at this data to find any common factors such as publisher or platform shared by successful titles to uncover characteristics that lead to a high rate-of-return when creating video games or other forms media entertainment

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: Video Games Sales.csv | Column name | Description | |:------------------|:------------------------------------------------------------| | Rank | The ranking of the game in terms of global sales. (Integer) | | Game Title | The title of the game. (String) | | Platform | The platform the game was released on. (String) ...

Cross-sectional survey of individuals aged 16–25 residing in Spain (n = 1,000), fielded in June 2025. The dataset captures demographics, gaming practices, exposure to gamblified mechanics (e.g., loot boxes, randomized rewards, time-limited offers), spending bands, emotional responses, and perceptions among non-players.The survey questionnaire (available as related material) consists of three sections: 1) sociodemographic data; 2) gamers (n=927); and 3) non-gamers (n=73). To distinguish between gamers and non-gamers, a filter question was placed at the end of section 1 (“Do you regularly play video games, mobile games, or digital games?”).The first section collects information on: sex/gender, municipality of residence, province/community of residence according to Nielsen area, size of municipality of residence, level of education completed, and current employment status.Block two collects information on estimated weekly hours of play, the device usually used, estimated monthly money spent on video games, and the two most played games. It also includes five-point Likert scale questions (disagree/agree) on issues related to random mechanisms, payments, or gaming habits.The third section repeats the same pattern of five-point Likert scale questions (disagree/agree) but from the perspective of non-video game players.

These datasets contain reviews from the Steam video game platform, and information about which games were bundled together.

Metadata includes

• reviews

• purchases, plays, recommends (likes)

• product bundles

• pricing information

Basic Statistics:

• Reviews: 7,793,069

• Users: 2,567,538

• Items: 15,474

• Bundles: 615

Hundreds of millions of people play intellectually-demanding video games every day. What does individual performance on these games tell us about cognition? Here, we describe two studies that examine the potential link between intelligence and performance in one of the most popular video games genres in the world (Multiplayer Online Battle Arenas: MOBAs). In the first study, we show that performance in the popular MOBA League of Legends’ correlates with fluid intelligence as measured under controlled laboratory conditions. In the second study, we also show that the age profile of performance in the two most widely-played MOBAs (League of Legends and DOTA II) matches that of raw fluid intelligence. We discuss and extend previous videogame literature on intelligence and videogames and suggest that commercial video games can be useful as 'proxy' tests of cognitive performance at a global population level.

Overview

This dataset is part of the study titled "Player Experience in Video Game Character Analysis: A Study of Female Characters", conducted at Mapúa University. The research aims to integrate player experience into an existing framework for video game character analysis.

Content

The dataset includes:

A partial transcript of 5 semi-structured interviews with the key informants. Originally, 8 interviews were conducted, but the audio/video recordings for 3 interviews were lost and thus their transcripts are not available.

Significant codes presented in tabulated form.

Data Collection Method

Data were collected through in-depth interviews conducted via Facebook Messenger and Discord from March to April 2024. Participants were various video game players from different backgrounds and age groups, ranging from 20 to 40 years old. Due to technical issues, the recordings of 3 interviews were lost, resulting in only 5 available transcripts.

Data Processing and Analysis

The 5 available interviews were transcribed verbatim. Data were analyzed using thematic analysis, involving initial coding, theme development, and refinement.

Usage data

The dataset is organized into several sections within a single Word document (.docx). This word document has headings for navigation and a definition of terms.

Limitations

The dataset only includes 5 out of 8 due to technical difficulties encountered after the recording of the interview. This may impact the comprehensiveness of the findings.

Contextual Reference

The manuscript associated with this dataset heavily references the works "A Structural Model for Player-Characters as Semiotic Constructs." (DOI: https://doi.org/10.26503/TODIGRA.V2I2.37) and "Object, me, symbiote, other: A social typology of player-avatar relationships." (DOI:https://doi.org/10.5210/FM.V20I2.5433) which explore the foundational frameworks on video game character analysis.

For any further information or clarifications, please contact wbdg2000@gmail.com

A global consumer survey conducted in March 2024 found that 18 percent of respondents were more likely to buy a video game if it was advertised as a collector or limited edition. However, 45 percent of respondents stated that they were not interested in limited edition releases.

Developmental psychology_raw data and code also see https://osf.io/2h6bu/

This dataset contains information about the average & peak monthly number of players per 6725 unique games placed on Steam. This dataset was inspired by another similar one, but it was not updated for the last 5 years, so I decided to make a bit more fresh one: https://www.kaggle.com/datasets/michau96/popularity-of-games-on-steam

The data was received by web scraping from https://steamcharts.com website by usage the basic script pandas read_html command to be launched on Kaggle side. The list of steam id to be scrapped was grabbed from Steam All Games Data dataset: https://www.kaggle.com/datasets/fmpugliese/steam-all-games-data.

The reason there are only 6725 unique name values is related to limitations of Steam Charts portal: when the game has minimal audience volume, the page for that game is not rendered in almost all cases. Also, some major games also could not be presented due to limitations of the selected scraping method.

A structure of the dataset: - Month - month-year of observation - avg_players - average players count (float) - gain - difference comparing to previous month (float) - gain_percent - difference comparing to previous month in percents (float) - peak_players - highest value of players at the same time for selected month (float) - name - name of the game (string) - steam_appid - steam ID of the game (string)

Please feel free to combine this dataset with any other video games-related sources on Kaggle.

This dataset was inspired by another Kaggle dataset: Popular Video Games 1980 - 2023 🎮, acting as a comprehensive collection of information about some of the most popular video games released. It serves as a valuable resource for researchers, gamers, and enthusiasts interested in exploring the evolution of the gaming industry over the past decades.

This dataset provides a wealth of information about each game, including its title, release date, genre, platform(s), summary, among other data. With this dataset, users can analyze trends, identify patterns, and gain insights into the popularity and commercial success of video games across different platforms and genres.

The difference between this dataset and its inspiration is that the amount of data is greater (60k rows), in addition to having the new column "Platforms", indicating which platforms the game was released on. Also, the "Reviews" column (with the texts of user reviews) has been removed, keeping only the column with the total number of reviews that the game has.

📅 - The data was collected in mid-June 2023, so some values may be slightly different today.

**To see detailed metadata see the PDF file on the right **

This dataset contains data on 64 videogames releases between 2012 and 2022. The games have been selected for being best-selling or top-rating games of their year. There are at least 5 games per year and information on the most relevant characters in the storyline.

The relationship between tables is as follows

Games.Game_ID = Characters.Game Characters.Id = Sexualization.Id

Version Update 10/11/22: - A misspelling on the games dataset column Release has been corrected - The abbreviations used to define the relevance of a character in Characters/Relevance have changed for clarity purposes · MC is now PA for protagonist · DA and SK stay the same · CR is now MC for main character · OR is now SC for secondary character · MV is now MA for main antagonist - A PDF file with detailed metadata has been added

🎮 Global Video Game Sales and Ratings Dataset

📘 Overview

This dataset provides a detailed view of video game sales performance, critical reception, and developer/publisher data across multiple gaming platforms. It’s perfect for exploring how factors like genre, publisher reputation, and critic scores influence global sales — and for building predictive models in data science and machine learning.

📊 Dataset Summary

💡 Potential Use Cases

• Predict global sales based on critic ratings and regions
• Analyze how game genres perform across regions
• Compare publisher or developer performance
• Study the impact of platform exclusivity on total sales
• Build recommendation systems for games

🧠 Example Ideas

• Machine Learning: Train regression models to forecast sales based on critic scores, genre, and platform.
• Visualization: Create dashboards showing top publishers, regional trends, or sales by genre.
• Statistical Insights: Correlation between critic score and sales success.

📅 Source

Compiled and formatted for analysis — suitable for both beginners exploring EDA (Exploratory Data Analysis)** and advanced users** working on predictive analytics projects.

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 ↑ ⠀⠀⠀

This is a simple survey dataset conducted within my computer science department, involving participation from over 100 students. The primary focus of the survey was to identify the reasons why computer science students engage in playing video games. The survey comprises only seven questions, including age, gender, whether the participant plays video games, student status, favorite game, most played game, and the most important reason for playing games. The findings reveal that a majority of participants indicated playing games for entertainment purposes.

This comprehensive dataset provides a multidimensional view of the video game industry, featuring data on 885,166 games spanning from classic titles to recent releases.

What's included

The dataset is structured in a star schema with 11 interconnected tables:

1. games: Core information about each title (ratings, release dates, playtime)
2. game_platforms: Platform availability and technical requirements
3. game_genres: Genre classifications
4. game_developers: Development studios
5. game_publishers: Publishing companies
6. game_tags: Descriptive tags and features
7. game_ratings: Detailed rating distributions
8. game_status: Player progression statistics
9. game_stores: Distribution platforms
10. game_metacritic_platforms: Critical reviews by platform
11. game_derived_metrics: Calculated metrics for advanced analysis

Key features

• Rich data variety: 48 unique variables across categorical, numerical, and temporal data types
• Player behavior insights: Track how games are acquired, played, completed, or abandoned
• Critical reception: Compare professional critics' ratings with user scores
• Derived metrics: Includes calculated indicators like completability index, consensus score, and time-value ratio
• Temporal analysis: Examine industry evolution through release dates spanning multiple decades
• Clean, structured format: Thoroughly processed to handle encoding issues and missing values

Potential applications

• Analyze genre popularity trends over time
• Identify factors that correlate with player engagement
• Compare platform ecosystems and exclusive titles
• Study the relationship between technical requirements and game success
• Discover discrepancies between critical reception and player experience
• Evaluate developer and publisher performance across different genres

This dataset was created by extracting raw data from RAWG, then cleaning, structuring, and enhancing it with derived metrics to enable sophisticated multidimensional analysis.

Crawler code available at GitHub, forked from Trung Hoang video-game-encyclopedia project whose original dataset is available at Video Game Dataset

Background image designed using AI