Video game sales from North America, Japan, the EU, Africa, and the rest of the world for 64,016 titles released from 1971-2024, including information like critic's score, genre, console, and more.

****Recommended Analysis**** Which titles sold the most worldwide?

Which year had the highest sales? Has the industry grown over time?

Do any consoles seem to specialize in a particular genre?

What titles are popular in one region but flop in another?

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 ↑ ⠀⠀⠀

The online gaming platform, Steam, was first released by the Valve Corporation in 2003. What started off as a small platform for Valve to provide updates to its games has turned into the largest computer gaming platform in the world. The platform initially released just 65 games in 2004, but this number has progressively risen in the ensuing years, reaching a staggering 15,422 in 2024, up from 9,204 in 2020. Steam’s PC dominance When you think of PC gaming, you automatically think of Steam. With such a wide range of games on offer, from traditional online multiplayer shooters to farming simulators, there is something for every gaming taste on the platform. As a result, gamers flock to Steam in their millions, with the platform registering over 132 million monthly active users in 2021. The global nature of the platform can be seen by the wide range of languages spoken by its users. Whilst English was the most spoken language for most of the platform's history, this changed as over 33 percent of users in October 2024 claimed Chinese as their platform language. Steam’s biggest games Counter Strike 2 was the most popular game on Steam during 2024. The first-person shooter averaged almost 685,000 players per hour, a significant lead over its successor, Counter-Strike 2. The game was also third among the 2024 list for peak number of concurrent players — CS2 reached over 1.74 million players in a single hour in its peak, with Black Myth: Wukong claiming first place with over 2.4 million peak concurrent players.

In April 2025, total video games sales in the United States amounted to **** billion U.S. dollars, representing a one 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.

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.

Context The propagation of covid-19 worried a lot to all us 😷. In that sense, a zombie pandemic was always a very used topic in all times. Certainly, is a horrible way to finish our existence, so, this stories were very violent and the characters were trying to survive.. That's great, however, in this century, many projects considered adding other facets: the social and psychological consequences in the characters in that world. ellie_lou2

That's how we got here. The Last of Us is a masterpiece in the industry of the videogames where many experts, critics and web-pages are agree. Justly, its story was based in that hopeless, post-apocalyptic situation. A strong point here was the exploration in this types of events. Other point, and no less important, was the gameplay and the interactions. So, this game won many prizes and maybe was a pioneer in its category 🙌 . You can find the reasons of its success in the section reviews_g1 and then establish insights for future similar games.

In the next year a dlc was released: Left Behind. It’s a prologue to the events of the original game, being Ellie the main character. In this way, the character and her actions are better understood. The game was well received. You can analize it in the section reviews_lb and identify the reviews about Ellie and its friendship. 😄

Finally, The Last of Us Part II (and the reason that I wanted to create this dataset). It shows very opposite reviews 🤔. It's amazing to see this high divergence. Personally, I like this game too, it presents incredible graphics and is very realistic. But i understand the other point of view, surely you know some reasons as the inconsistency in character decisions or the changes in the trailers. But exist other reasons, you can analize it in depth in the section reviews_g2 and if is possible, propose any predictive model. In this case you can start here.

Now, a serie will be released. All of us hope it'll be a success 🎉🎉

Content This kaggle dataset contains information scraped from metacritics using Scrapy and BeautifulSoup. More info about the used web-scraping in this github repository. The dataset contains 3 main sections: The Last of Us part II, The Last of Us, The Last of Us Left Behind where each one contains two type of files: users and critics.

The collection methodology is explained below: -The sample: The scraped reviews are the most recommend reviews. In one case is possible download all reviews but in other cases was not possible (it's possible but it's not good abuse web scraping in a web-page). However, the retrieved information is sufficient for further analysis. With the 6 files, it has a total of 40000 observations and 8 variables. Have fun! -Set of items: The game-users and/or fans of the sequel (or critics). Maybe a bot, but is just a hypothesis. Another point, the user reviews are more greater thar critic reviews by far. -Set of variables: All user data contains the following variables.

Variable Description Id The nick of the game-user. Is a unique value Review The review of the user Type_review Some reviews are large or present spoilers. Expanded is that and normal is the rest. Views Number of views in a review Votes Number of votes that it was received Date Date when the review was published Language Used language in the review Score Proposed punctuation given for the user. The target In the case of critic data, only contain Id, Review, Date and Score.

An update: I created new files. There are the files that ends in u. Those files are a duplicated of the originaI, i only added two new variables:

Variable Description Platform Now, the set contains information about ps3 and ps4 reviews Split For the modeling and the tasks. Pd1: Please check out the tasks. If you are interested, please propose any notebook 😊. If the dataset is not enough and you consider that is necessary get more variables, please let me know in the discussions. Pd2: Now, the id is not unique in tables with the variable platform. In fact, this is a gamer-id and he can write a review in both platforms.

Usage Text classification: The main topic in this types of datasets. Vectorize the reviews and define a predictive model. Identify strong and weak points of the game. Compare each games: What is preferred? In what points? Why did this game is better than other this? Reduction of dimention: Detect similar word and then, clustering the reviews. Pd: Important. Mantain discretion. Some reviews are disrespectful, violent and difficult to read 😅. And obviously contain spoilers.

Acknowledgements Thanks to Kaggle and its community. In general, thanks to the learners and teachers in machine learning, deep learning and computer vision.

Inspiration Natural language processing is a great tool. One application that I'm interested is detect bullies messages in any social network. I know that exist many notebooks and papers, but I'd like to build a bot that detect all possible cases and surely, there exist!

Original

Gameplay Images

A dataset from kaggle. This is a dataset of 10 very famous video games in the world. These include

Among Us Apex Legends Fortnite Forza Horizon Free Fire Genshin Impact God of War Minecraft Roblox Terraria

There are 1000 images per class and all are sized 640 x 360. They are in the .png format. This Dataset was made by saving frames every few seconds from famous gameplay videos on Youtube. ※ This dataset was uploaded in January 2022. Game content updated after that… See the full description on the dataset page: https://huggingface.co/datasets/Bingsu/Gameplay_Images.

Analysis of ‘Video Game Sales’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/gregorut/videogamesales on 12 November 2021.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Video Game Sales and Ratings’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kendallgillies/video-game-sales-and-ratings on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This data set contains a list of video games with sales greater than 100,000 copies along with critic and user ratings. It is a combined web scrape from VGChartz and Metacritic along with manually entered year of release values for most games with a missing year of release. The original coding was created by Rush Kirubi and can be found here, but it limited the data to only include a subset of video game platforms. Not all of the listed video games have information on Metacritic, so there data set does have missing values.

Content

The fields include:

• Name - The game's name
• Platform - Platform of the games release
• Year_of_Release - 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 (in millions)
• Critic_score - Aggregate score compiled by Metacritic staff
• Critic_count - The number of critics used in coming up with the critic score
• User_score - Score by Metacritic's subscribers
• User_count - Number of users who gave the user score
• Rating - The ESRB ratings

Acknowledgements

Again the main credit behind this data set goes to Rush Kirubi. I just commented out two lines of his code.

Also the original inspiration for this data set came from Gregory Smith who originally scraped the data from VGChartz, it can be found here.

--- Original source retains full ownership of the source dataset ---

The Sims4Action Dataset: a videogame-based dataset for Synthetic→Real domain adaptation for human activity recognition.

Goal : Exploring the concept of constructing training examples for Activities of Daily Living (ADL) recognition by playing life simulation video games.

Sims4Action dataset is created with the commercial game THE SIMS 4 by executing actions-of-interest within the game in a "top-down" manner. It features ten hours of video material of eight diverse characters and multiple environments. Ten actions are selected to have a direct correspondence to categories covered in the real-life dataset Toyota Smarthome [2] to enable the research of Synthetic→Real transfer in action recognition. Two benchmarks : Gaming→Gaming (training and evaluation on Sims4Action) and Gaming→Real (training on Sims4Action, evaluation on the real Toyota Smarthome data [2]). Main challenge: Gaming→Real domain adaptation While ADL recognition on gaming data is interesting from a theoretical perspective, the key challenge arises from transferring knowledge learned from simulated data to real-world applications. Sims4Action specifically provides a benchmark for this scenario since it describes a Gaming→Real challenge, which evaluates models on real videos derived from the existing Toyota Smarthome dataset .

References [1] Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games. Alina Roitberg, David Schneider, Aulia Djamal, Constantin Seibold, Simon Reiß, Rainer Stiefelhagen, In International Conference on Intelligent Robots and Systems (IROS), 2021 (* denotes equal contribution.)

[2] Toyota smarthome: Real-world activities of daily living. Srijan Das, Rui Dai, Michal Koperski, Luca Minciullo, Lorenzo Garattoni, Francois Bremond, Gianpiero Francesca, In International Conference on Computer Vision (ICCV), 2019.

Fictional Worlds

Immersive insights into diverse fictional realms

By Someone13574 (From Huggingface) [source]

About this dataset

With its multitude of columns, this dataset allows users to delve into each unique fictional world with precision. The seed column serves as a distinctive identifier for every fictional world within the dataset. It offers a key to unlock the vast array of information contained within.

The geography_and_nature column entails vivid descriptions and essential characteristics of the physical landscapes and natural features found in each fictional world. From lush green forests teeming with magical creatures to towering mountain ranges shrouded in mystery, this column unveils breathtaking details about the environment in these imaginary realms.

The history column takes us on a journey through time; uncovering significant milestones, developments, and turning points that have shaped each fictional world's past. Without specific dates included in this dataset description for flexibility purposes when using it.

To fully comprehend these captivating worlds from within their inhabitants' perspective comes the culture_and_society column. This section delves into customs, traditions spatial perturbations (people gathering because they like being together), social structures (how people are organized), lifestyle choices(shapes creator presence) (could be dystopia or utopia), economic systems(shapes creator presence which can also shapes culture)<

A survey conducted in the third quarter of 2024 found that over 92 percent of female internet users aged 16 to 24 years worldwide played video games on any kind of device. During the survey period, 93 percent of male respondents in the same age group stated that they played video games. Worldwide, over 83 percent of internet users were gamers.

This dataset is about books. It has 1 row and is filtered where the book is The VOID shell : a toolkit for the development of distributed video games and virtual worlds. It features 4 columns: author, book publisher, and BNB id.

The review dataset for 3 video games - Call of Duty : Black Ops 3, Persona 5 Royal and Counter Strike: Global Offensive was taken through a web scrape of SteamDB [https://steamdb.info/] which is a large repository for game related data such as release dates, reviews, prices, and more. In the initial scrape, each individual game has two files - customer reviews (Count: 100 reviews) and price time series data.

To obtain data on the reviews of the selected video games, we performed web scraping using R software. The customer reviews dataset contains the date that the review was posted and the review text, while the price dataset contains the date that the price was changed and the price on that date. In order to clean and prepare the data we first start by sectioning the data in excel. After scraping, our csv file fits each review in one row with the date. We split the data, separating date and review, allowing them to have separate columns. Luckily scraping the price separated price and date, so after the separating we just made sure that every file had similar column names.

After, we use R to finish the cleaning. Each game has a separate file for prices and review, so each of the prices is converted into a continuous time series by extending the previously available price for each date. Then the price dataset is combined with its respective in R on the common date column using left join. The resulting dataset for each game contains four columns - game name, date, reviews and price. From there, we allow the user to select the game they would like to view.

As a massive League of Legends fan for 10+ years, I realized that there weren't any datasets that helped us stay updated with Worlds 2021, thus this dataset was born!

All data was acquired from lolesports.com which shows all in-depth statistics available for each match that others can use to find correlations between in-game statistics and wins.

I would love to see this data used to answer how vision (ward interactions) and gold distribution (how a team's gold is divided among it's positions) correlate with win percentage.

There is no description for this dataset.

Here are a few use cases for this project:

1. Sports Analytics: This model can be used for player tracking, statistical analytics, and team performance analysis in football games, specifically for FC Seoul team. It's able to measure the individual’s performance which can provide insights to coaches on strategizing game plays.

2. Broadcasting and Media: The model can be used to automatically identify and highlight key players or referees during broadcasting. It can generate real-time player statistics for commentators.

3. Video Gaming and Simulation: The model can be integrated into video games to realize AI-based characters mirroring real-world players' performance and movements.

4. Security and Surveillance: The model can be utilized to monitor crowds during games for security purposes, such as identifying individuals in the audience or tracking unexpected behaviors/actions.

5. Automated Sports Journalism: This model can be a component in a system generating automated news stories or match summaries, by providing player occurrences, movements, and interactions data.

This dataset provides detailed information for Pokémon from 1 to 1045, as listed in the National Pokédex. It includes fundamental Pokédex entries such as their names, types, and physical attributes, alongside more in-depth data like move sets, type effectiveness, abilities with full descriptions, and battle strategies sourced from Smogon. Additionally, the dataset contains brief descriptions from Bulbapedia. A distinct text corpus file is also included, offering a textual representation for each Pokémon, compiled from all the details present in the main Pokédex file.

Columns

The main Pokémon file features 56 columns, providing extensive details for each creature. Key columns include: * pokédex number: The official National Pokédex identification number. * name: The English name of the Pokémon. * japanese name: The Japanese name of the Pokémon. * generation: The generation number the Pokémon originates from. * status: Indicates if the Pokémon is Legendary. * species: The specific species of the Pokémon. * type number: How many elemental types the Pokémon possesses. * type 1: The primary elemental type. * type 2: The secondary elemental type, if applicable. * height: The Pokémon's height in metres. * weight: The Pokémon's weight in kilograms. * abilities number: The count of abilities it can have. * total points: The sum of all base stats. * stats: Individual columns for key battle statistics: HP, attack, defence, special attack, special defence, and speed. * catch rate: The Pokémon's catch rate. * base friendship: The base friendship value. * base experience: The base experience yield. * growth rate: The growth rate category. * egg type number: The number of egg groups it belongs to. * egg type 1: The primary egg group. * egg type 2: The secondary egg group, if applicable. * percentage male: The likelihood of the Pokémon being male. * egg cycles: The number of steps required to hatch an egg. * type effectiveness: Columns detailing effectiveness against various types (e.g., normal, fire, water, grass, electric, flying, ground, rock, fighting, psychic, dark, ghost, dragon, ice, fairy, poison, bug, steel). * Smogon description: Battle strategies primarily from SM Pokédex, or other generations if more relevant. * Bulba description: Initial sentences from the Pokémon's Bulbapedia page. * moves: A dictionary detailing moves the Pokémon learns by levelling up, including name, type, damage type, power, accuracy, PP, level learned, secondary effect chance, and description. * ability 1, ability 2, hidden ability: The names of the Pokémon's abilities. * ability 1 description, ability 2 description, hidden ability description: Descriptions for each of the Pokémon's abilities.

The accompanying Poké corpus file contains a text corpus for each Pokémon, generated by consolidating all the information from the Pokédex file.

Distribution

This dataset encompasses information for Pokémon numbered 1 through 1045. The primary Pokémon data file contains 56 distinct columns for each entry. While specific row counts are not provided, there are 1045 unique Pokémon entries detailed. Data files are typically provided in CSV format.

Usage

This dataset is ideally suited for a variety of applications, particularly in the fields of artificial intelligence, machine learning, and data analysis related to gaming and entertainment. * Building AI Chatbots: Useful for creating conversational agents, such as a Pokémon chatbot, through retrieval-augmented generation (RAG) pipelines. * Game Development: Provides extensive data for developers creating Pokémon-inspired games or applications. * Data Analysis: Researchers and enthusiasts can analyse Pokémon stats, moves, and abilities for competitive strategy or general insights. * Natural Language Processing (NLP): The text corpus can be used for text generation, entity recognition, and other NLP tasks related to Pokémon lore.

Coverage

The dataset covers Pokémon from number 1 to 1045 in the National Pokédex. Its scope is global, providing information relevant to all regions where Pokémon are known. There are no specific notes on data availability for certain groups or years beyond the stated Pokédex range.

License

CC BY-SA

Who Can Use It

• Data Scientists and AI/ML Developers: For training models, building recommendation systems, or developing chatbots and other AI applications using the detailed Pokémon attributes and text corpus.
• Game Developers: To integrate accurate and detailed Pokémon information into their projects.
• Researchers: For academic studies on game design, character attributes, or data structures in entertainment.
• Pokémon Enthusiasts and Community Developers: For fan-made applications, wikis, or statistical analyses

Here are a few use cases for this project:

1. Sports Coaching App: This model can be implemented in a sports coaching tool to analyze the positioning of players, the net, and the ball in the game for refining strategies and improving player performance in tennis.

2. Sports Event Management: This can be used by event managers to ensure adequate setup and layout for tennis courts in different venues, making sure everything is in a correct and standard position.

3. Video Gaming Development: The model can help simulate real-world player-ball-court interactions for developing more accurate and immersive tennis video games.

4. Sports Broadcasting: During broadcasting of live games, the model can be useful for real-time graphics overlay, tracking player and ball movements, and offer statistical analyses to enrich viewer experiences.

5. Automated Sports Journalism: The tool can be used to autogenerate summative descriptions or perform detailed game analyses, based on the identification and tracking of player and ball positions in professional tennis matches.

Here are a few use cases for this project:

1. Sports Analytics: YoloV5 can be used to automate analytics in sports games, particularly in games like badminton. By identifying individual players and objects like the net and shuttlecock, the model could track player movements, interactions with the shuttlecock, and count the number of times the shuttlecock hits the net.

2. Training and Coaching: The model can assist coaches in understanding their players' performance better by monitoring their footwork, strategy implementation, speed, and other performance metrics during practice sessions or matches.

3. Gaming & Virtual Reality: The model could be applied in the development of interactive sports video games or VR simulations, where real-world actions of players are captured and transformed into in-game movements.

4. Sports Equipment Testing: Companies could use the model during the quality testing phase of sports equipment—like rackets and shuttlecocks—by tracking the movement and response of the equipment under various conditions.

5. Sports Broadcasting and Journalism: This model could be used to aid sports journalists and broadcasters by automatically generating statistics and key highlights of the game (e.g., number of net hits, shuttlecock speed and trajectory, player positioning) in real-time, making covering, analyzing, and summarizing games more efficient.