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...

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This dataset contains information on 10,000 of the most popular video games, curated from multiple sources. It is designed for data science, machine learning, and analytics projects in gaming, entertainment, and recommendation systems.

The dataset includes:

ID & Name – unique identifier and game title

Release & Update Dates – when the game was originally released and last updated

Rating & Suggestions Count – aggregated player ratings and number of community recommendations

Platforms – supported consoles and systems (e.g., PC, PlayStation, Xbox, Switch, Mobile)

Developers & Publishers – companies behind the games

Genres – classification (RPG, FPS, Adventure, etc.)

Image – cover art thumbnail URL for visualization

Description – text summary of the game

Potential Use Cases

Exploratory analysis: study trends in ratings, genres, or release dates

Machine Learning: build recommender systems for games

NLP: analyze game descriptions & genres

Visualization projects: timeline charts, platform distribution, developer networks

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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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) ...

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.

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Data obtained using a program from the site vgchartz.com.

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"Founded in 2005 by Brett Walton, VGChartz (Video Game Charts) is a business intelligence and research firm and publisher of the VGChartz.com websites. As an industry research firm, VGChartz publishes video game hardware estimates every week and hosts an ever-expanding game database with over 55,000 titles listed, featuring up-to-date shipment information and legacy sales data. The VGChartz.com website provides consumers with a range of content from news and sales features, to reviews and articles, to social networking and a community forum." - from the site vgchartz.com.

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"Since the end of 2018 VGChartz no longer produces estimates for software sales. This is because the high digital market share for software was making it both more difficult to produce reliable retail estimates and also making those estimates increasingly unrepresentative of the wider performance of the games in question. As a result, on the software front we now only record official shipment/sales data, where such data is made available by developers and publishers. The legacy data remains on the site for those who are interested in browsing through it." - from the site vgchartz.com.

What can you do with the data set?

If you are new to data analytics, try answering the following questions: - in what year did the active growth in the number of video games produced begin? What year was the most successful from this point of view? What can you conclude if you look at the number of video games released by country? - on what day and month were the largest number of video games released? What could be the reason for this pattern? - is there a dependence of the number of copies sold on the ratings of critics or users? - which gaming platforms, publishers and developers are the most common (the largest number of video games have been released over time)? - which gaming platforms, publishers and developers have the largest number of video game copies sold (over all time, the total number of copies sold was the largest)?

If you have enough experience, try solving a regression problem. Train a model that can predict the number of copies sold of video games: - what signs can be used to prevent leakage of the target variable? - how do outliers affect the quality of the model? - which metric should be chosen to evaluate the model? - can adding new data improve the predictive ability of the model? - does the trained model have signs of heteroscedasticity of the residuals? How does this affect the predictive ability of the model? What can you do?

Field descriptions:

The data contains the following fields: 1. name – name of the video game. 2. date - release date of the video game. 3. platform - gaming platform (All – all gaming platforms, Series – all video game series). 4. publisher – publisher. 5. developers - developer. 6. shipped - the number of copies sent (relevant for records with the values All and Series in the platform field). 7. total - total number of copies sold (millions of copies). 8. america - number of copies sold in America (millions of copies). 9. europe - number of copies sold in Europe (millions of copies). 10. japan - number of copies sold in Japan (millions of copies). 11. other - other sales in the world. 12. vgc - rating VGChartz.com. 13. critic - critics' assessment. 14. user - user rating.

Found an error or inaccuracy in the data?

This dataset is the result of painstaking work. After collection and systematization, the data is checked for integrity and correctness. If you notice an error or inaccuracy in the data, or have a suggestion on how to improve the data set, please let me know.

You can look at working with data in my github repository.

Gaming has increasingly become a part of life in Africa. Currently, no data on gaming disorders or their association with mental disorders exist for African countries. This exploratory study investigates (1) the prevalence of insomnia, excessive daytime sleepiness, anxiety and depression among African gamers based in Gabon and Tunisia and (2) the association between these conditions and gamer types (i.e., non-problematic, engaged, problematic and addicted). The questionnaire could only be completed once by participants with the same email address, and duplicates and incomplete forms were discarded. Responses were collected in multiple sites based in nine African countries between November 2015 and June 2017 (Rwanda, Gabon, Cameroon, Nigeria, Morocco, Tunisia, Senegal, Ivory Coast and South Africa). Because of local restrictions related to the expiration of some ethical certificates, this dataset currently provides aggregate data from Gabon and Tunisia.

Data contained aggregate information describing epidemiology of self-reported measures of insomnia (with the Insomnia Severity Index), excessive daytime sleepiness (with Epworth Sleepiness Scale), anxiety (with Hospital Anxiety and Depression Scale-A), depression (Hospital and Anxiety Depression Scale-D) and gaming disorder (with game addiction scale short form) between gamers in Tunisia and Gabon. The participants who formed this convenience sample were contacted by email. The online questionnaire included a consent form on the second page, following a description of the study in French and English. Consent was required to participate in this project. The average time to answer all questions was 20 minutes. Data available are as follow: mean hours of gaming per week, period from when the participant considered him or herself a gamer, type of device used for gaming purposes, age, sex, and category of gamers.

The present research is a pilot investigation which documents sleep disorders, anxiety and depression among an African sample with a focus on gamers. It should be replicated with the general population with a longitudinal cohort study to understand the global picture of gaming disorder. Similarly, more attention should be brought to the sleep health of African populations. More research on gaming addiction needs to be performed in low- and middle-income countries where little is known about internet gaming disorder.

Comprehensive ranking dataset of the top 100 YouTube channels in the Gaming category. This dataset features 100 channels with detailed statistics including subscriber counts, total video views, video count, and global rankings. The leading channel has 110,000,000 subscribers and 29,436,109,895 total views. Each entry includes comprehensive metrics to analyze channel performance, growth trends, and competitive positioning. This dataset is regularly updated to reflect the latest YouTube channel statistics and ranking changes, providing valuable insights for content creators, marketers, and researchers analyzing YouTube ecosystem trends and channel performance benchmarks.

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.

Three corpora in different domains extracted from Wikipedia.For all datasets, the figures and tables have been filtered out, as well as the categories and "see also" sections.The article structure, and particularly the sub-titles and paragraphs are kept in these datasets.

Wines: Wikipedia wines dataset consists of 1635 articles from the wine domain. The extracted dataset consists of a non-trivial mixture of articles, including different wine categories, brands, wineries, grape types, and more. The ground-truth recommendations were crafted by a human sommelier, which annotated 92 source articles with ~10 ground-truth recommendations for each sample. Examples for ground-truth expert-based recommendations are Dom Pérignon - Moët & Chandon, Pinot Meunier - Chardonnay.

Movies: The Wikipedia movies dataset consists of 100385 articles describing different movies. The movies' articles may consist of text passages describing the plot, cast, production, reception, soundtrack, and more. For this dataset, we have extracted a test set of ground truth annotations for 50 source articles using the "BestSimilar" database. Each source articles is associated with a list of ${\scriptsize \sim}12$ most similar movies. Examples for ground-truth expert-based recommendations are Schindler's List - The PianistLion King - The Jungle Book.

Video games: The Wikipedia video games dataset consists of 21,935 articles reviewing video games from all genres and consoles. Each article may consist of a different combination of sections, including summary, gameplay, plot, production, etc. Examples for ground-truth expert-based recommendations are: Grand Theft Auto - Mafia, Burnout Paradise - Forza Horizon 3.

This dataset is about books. It has 1 row and is filtered where the book is After dinner games : 40 of the greatest after dinner games. It features 7 columns including author, publication date, language, and book publisher.

This dataset provides a list of the most popular video games in history based on the number of units sold worldwide. It includes over 40+ video games that have each sold millions of copies. The data represents a wide range of platforms including consoles like PlayStation, Nintendo, Xbox, and PC.

Some of the most iconic franchises are featured here, such as Mario, Call of Duty, Grand Theft Auto, Pokémon, The Legend of Zelda, and Minecraft. The dataset shows how certain games and series have maintained popularity over the years and how different platforms have contributed to global game sales.

About Columns

• Game Title : The name of the video game.
• Units sold : The number of copies the game has sold worldwide (in millions).
• Platform(s) : The gaming platforms or consoles the game is available on (like PS4, Xbox, Nintendo Switch).
• Initial release date : The year or full date when the game was first released.
• Developer(s) : The game development company or studio that created the game.
• Publisher(s) : The company that published and distributed the game.
• Series : The game series or franchise the game is part of (e.g., Mario, Pokémon).

This dataset is about books. It has 1 row and is filtered where the book is More games : for National Curriculum levels 1-3. It features 7 columns including author, publication date, language, and book publisher.

This dataset is about book subjects. It has 2 rows and is filtered where the books is The 50 greatest players in Denver Broncos history. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This data set includes population and electronic gaming machine (EGM) expenditure breakdowns by local government area (LGA) and gaming venue, demographic statistics, labour statistics and Socio-Economic Indexes for Areas (SEIFA) LGA score and ranking per LGA for the 2016/17 financial year. The data has been joined with LGA 2011 boundaries. For more information visit the Victorian Commission for Gambling and Liquor Regulation's (VCGLR) website. Please note: AURIN has spatially enabled the original data. EGM Numbers: a venue may be operating less machines than its licensed or attached numbers Gaming Machine Density calculations are based on operating gaming machines with attached entitlements divided by adult population divided by 1,000 (gaming machines per 1,000 adults).

AboutRecent research shows that visualizing linguistic media bias mitigates its negative effects. However, reliable automatic detection methods to generate such visualizations require costly, knowledge-intensive training data. To facilitate data collection for media bias datasets, we present News Ninja, a game employing data-collecting game mechanics to generate a crowdsourced dataset. Before annotating sentences, players are educated on media bias via a tutorial. Our findings show that datasets gathered with crowdsourced workers trained on News Ninja can reach significantly higher inter-annotator agreements than expert and crowdsourced datasets. As News Ninja encourages continuous play, it allows datasets to adapt to the reception and contextualization of news over time, presenting a promising strategy to reduce data collection expenses, educate players, and promote long-term bias mitigation.

GeneralThis dataset was created through player annotations in the News Ninja Game made by ANON. Its goal is to improve the detection of linguistic media bias. Support came from ANON. None of the funders played any role in the dataset creation process or publication-related decisions.

The dataset includes sentences with binary bias labels (processed, biased or not biased) as well as the annotations of single players used for the majority vote. It includes all game-collected data. All data is completely anonymous. The dataset does not identify sub-populations or can be considered sensitive to them, nor is it possible to identify individuals.

Some sentences might be offensive or triggering as they were taken from biased or more extreme news sources. The dataset contains topics such as violence, abortion, and hate against specific races, genders, religions, or sexual orientations.

Description of the Data FilesThis repository contains the datasets for the anonymous News Ninja submission. The tables contain the following data:

ExportNewsNinja.csv: Contains 370 BABE sentences and 150 new sentences with their text (sentence), words labeled as biased (words), BABE ground truth (ground_Truth), and the sentence bias label from the player annotations (majority_vote). The first 370 sentences are re-annotated BABE sentences, and the following 150 sentences are new sentences.

AnalysisNewsNinja.xlsx: Contains 370 BABE sentences and 150 new sentences. The first 370 sentences are re-annotated BABE sentences, and the following 150 sentences are new sentences. The table includes the full sentence (Sentence), the sentence bias label from player annotations (isBiased Game), the new expert label (isBiased Expert), if the game label and expert label match (Game VS Expert), if differing labels are a false positives or false negatives (false negative, false positive), the ground truth label from BABE (isBiasedBABE), if Expert and BABE labels match (Expert VS BABE), and if the game label and BABE label match (Game VS BABE). It also includes the analysis of the agreement between the three rater categories (Game, Expert, BABE).

demographics.csv: Contains demographic information of News Ninja players, including gender, age, education, English proficiency, political orientation, news consumption, and consumed outlets.

Collection ProcessData was collected through interactions with the NewsNinja game. All participants went through a tutorial before annotating 2x10 BABE sentences and 2x10 new sentences. For this first test, players were recruited using Prolific. The game was hosted on a costume-built responsive website. The collection period was from 20.02.2023 to 28.02.2023. Before starting the game, players were informed about the goal and the data processing. After consenting, they could proceed to the tutorial.

The dataset will be open source. A link with all details and contact information will be provided upon acceptance. No third parties are involved.

The dataset will not be maintained as it captures the first test of NewsNinja at a specific point in time. However, new datasets will arise from further iterations. Those will be linked in the repository. Please cite the NewsNinja paper if you use the dataset and contact us if you're interested in more information or joining the project.

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 ↑ ⠀⠀⠀

League of Legends (LOL) is the most popular game on PC, drawing 8 million concurrent players. A common activity of gamers, besides playing games, is to watch other players presenting tips and tricks. Streaming platforms allow some players to show gameplays and live games. Twitch.tv is the world´s leading live streaming platform. Considering that hate speech is a ubiquitous problem in online gaming, we collected 985,766 comments from five videos of the top 10 LOL streamers in Twitch.tv platform. The dataset is freely available in a single file, ensembling all videos/players; and divided by players as well. These comments are a rich data source for opinion mining, sentiment analysis, topic modeling, and hate speech detection (including sexism and racism).

Complete historical game data between Iowa and Iowa State including scores, dates, locations, and game statistics.

World of Tanks player activity dataset from MMO Populations, combining monthly enhanced players and 30-day daily estimates generated from public signals.

In this dataset, I collected data on 276 of the best esports players in the world.
This is not a complete list of all active, professional esports players, but rather a consolidation of the most influential or significant. The list does not include online poker or online chess players, since they are usually separated from esports.