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.

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

More Datasets

For more datasets, click here.

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 ↑ ⠀⠀⠀

Dataset

This dataset was created by Ezgi Turalı

Contents

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?

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.

JerichoWorld is a dataset that enables the creation of learning agents that can build knowledge graph-based world models of interactive narratives. Interactive narratives -- or text-adventure games -- are partially observable environments structured as long puzzles or quests in which an agent perceives and interacts with the world purely through textual natural language. Each individual game typically contains hundreds of locations, characters, and objects -- each with their own unique descriptions -- providing an opportunity to study the problem of giving language-based agents the structured memory necessary to operate in such worlds.

JerichoWorld provides 24,198 mappings between rich natural language observations and: (1) knowledge graphs that reflect the world state in the form of a map; (2) natural language actions that are guaranteed to cause a change in that particular world state. The training data is collected across 27 games in multiple genres and contains a further 7,836 heldout instances over 9 additional games in the test set.

This dataset is about books. It has 1 row and is filtered where the book is How to play the 200 best-ever card games : a fantastic compendium of the greatest card games from around the world, including the history, rules, and winning strategies for each game, with more than 400 colour images : everything from fun games and simple ways to get started for beginners and family players, to professional tips and expert guidance for advanced play in serious games of chance. It features 7 columns including author, publication date, language, and book publisher.

Esports Performance Rankings and Results

Performance Rankings and Results from Multiple Esports Platforms

By [source]

About this dataset

This dataset provides a detailed look into the world of competitive video gaming in universities. It covers a wide range of topics, from performance rankings and results across multiple esports platforms to the individual team and university rankings within each tournament. With an incredible wealth of data, fans can discover statistics on their favorite teams or explore the challenges placed upon university gamers as they battle it out to be the best. Dive into the information provided and get an inside view into the world of collegiate esports tournaments as you assess all things from Match ID, Team 1, University affiliations, Points earned or lost in each match and special Seeds or UniSeeds for exceptional teams. Of course don't forget about exploring all the great Team Names along with their corresponding websites for further details on stats across tournaments!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Download Files First, make sure you have downloaded the CS_week1, CS_week2, CS_week3 and seeds datasets on Kaggle. You will also need to download the currentRankings file for each week of competition. All files should be saved using their originally assigned name in order for your analysis tools to read them properly (ie: CS_week1.csv).

Understand File Structure Once all data has been collected and organized into separate files on your desktop/laptop computer/mobile device/etc., it's time to become familiar with what type of information is included in each file. The main folder contains three main data files: week1-3 and seedings. The week1-3 contain teams matched against one another according to university, point score from match results as well as team name and website URL associated with university entry; whereas the seedings include a ranking system amongst university entries which are accompanied by information regarding team names, website URLs etc.. Furthermore, there is additional file featured which contains currentRankings scores for each individual player/teams for an first given period of competition (ie: first week).

Analyzing Data Now that everything is set up on your end it’s time explore! You can dive deep into trends amongst universities or individual players in regards to specific match performances or standings overall throughout weeks of competition etc… Furthermore you may also jumpstart insights via further creation of graphs based off compiled date from sources taken from BUECTracker dataset! For example let us say we wanted compare two universities- let's say Harvard University v Cornell University - against one another since beginning of event i we shall extract respective points(column),dates(column)(found under result tab) ,regions(csilluminating North America vs Europe etc)general stats such as maps played etc.. As well any other custom ideas which would come along in regards when dealing with similar datasets!

Research Ideas

• Analyze the performance of teams and identify areas for improvement for better performance in future competitions.
• Assess which esports platforms are the most popular among gamers.
• Gain a better understanding of player rankings across different regions, based on rankings system, to create targeted strategies that could boost individual players' scoring potential or team overall success in competitive gaming events

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: CS_week1.csv | Column name | Description | |:---------------|:----------------------------------------------| | Match ID | Unique identifier for each match. (Integer) | | Team 1 | Name of the first team in the match. (String) | | University | University associated with the team. (String) |

File: CS_week1_currentRankings.csv | Column name | Description | |:--------------|:-----------------------------------------------------------|...

This dataset provides a detailed collection of Nintendo Switch video game information and user reviews, meticulously scraped from a dedicated gaming website [1]. It serves as a valuable resource for understanding the landscape of Nintendo Switch titles, offering insights into game attributes and public sentiment [1, 2]. The dataset is part of a free data library and is offered by a verified data provider [1].

Columns

• id: A unique identifier for each video game, generated using an MD5 function [1, 2].
• title: The official name of the game [1, 2].
• game_url: The original URL from which the game's information was extracted [1, 2].
• image_url: A URL linking to an image associated with the game [1, 2].
• system: Specifies the gaming system on which the video game can be found, primarily "Nintendo Switch" [1, 2].
• publisher: The company responsible for publishing the video game [1, 2].
• developer: The company responsible for developing the video game [1, 2].
• genre: The genre or genres of the video game. Multiple genres are separated by a space, and some genres consist of more than one word [1, 2].
• num_players: Indicates the maximum number of players supported, both offline and online, if applicable [1, 2].
• release_date: The date when the game was initially released [1, 2].
• review: The complete written review of the game [1].
• review_conclusion: The concluding summary or verdict of the game's review, if provided by the source website [1].
• num_votes: The total number of votes given to the game by users [1].
• rating: The calculated user rating for the game. This value is determined if the number of votes exceeds a specific threshold; otherwise, it defaults to 0 [1].

Distribution

This dataset is typically provided in a CSV file format [3]. It contains 637 unique records, with each record representing a distinct Nintendo Switch game [4]. The structure includes detailed attributes for each game, along with associated review data [1, 2].

Usage

This dataset is ideal for various applications, including: * Market analysis and trend identification within the video game industry [1]. * Sentiment analysis of game reviews using Natural Language Processing (NLP) techniques [1]. * Game development research, understanding player preferences and popular genres [1, 2]. * Academic studies on media consumption and digital entertainment [1]. * Building recommendation systems for video games [1].

Coverage

The dataset's coverage is global [5]. It includes information on Nintendo Switch games released on various dates, with specific examples from October 2019, and the release date is a key column for temporal analysis [1, 2, 4]. There are no specific notes on demographic scope beyond general video game enthusiasts and players.

License

CC0

Who Can Use It

This dataset is suitable for: * Data scientists and machine learning engineers for developing and training models related to text analysis (NLP) and recommendation systems [1, 5]. * Game developers and publishers seeking insights into market trends, player feedback, and genre popularity [1, 2]. * Market researchers interested in the entertainment and media consumption sectors [1]. * Students and academics conducting research on gaming culture, digital media, or data analysis [1, 2]. * Data providers and those building open data repositories [5].

Dataset Name Suggestions

• Nintendo Switch Game Reviews & Metadata
• Global Nintendo Switch Game Data with Ratings
• Nintendolife Switch Game Database
• Switch Game Titles and Reviews
• Video Game Market Insights: Nintendo Switch

Attributes

Original Data Source: Nintendo Switch Games Reviews

Minecraft Statistics: The reports say that the gaming industry is expected to reach $431.87 billion by the year 2030. Since technological developments, not only there are laptops and PCs which are gaming-oriented but mobile devices have become compatible with many advanced games today. The recent release of the Harry Potter game ‘ Hogwarts Legacy is already doing its magic on the muggle world. These Minecraft Statistics include insights from various aspects that provide light on why Minecraft is one of the best games today. Editor’s Choice In Minecraft, 24 hours of the game is 20 minutes in real life. As of January 2023, the recorded number of players is 173.5 million. On average, 110,000 concurrent viewers are found on Twitch. Revenue generated from mobile downloads excluding in-game transactions counts for up to 41% of total Minecraft revenue. The Chinese edition of Minecraft has been downloaded more than 400 million times. To heal the players’ health healing potions have been used more than 1.1 billion times. Before launching Minecraft, the game was almost named a ‘Cave Game’. The game sometimes misspells its name by changing the order of words ‘C’ and ‘E’ with ‘Minecraft’. During the initial years of the pandemic, the database of total players increased by more than 14 million. The average age of a player is 24 years.

(Toll Free) Number +1-341-900-3252

If you’re a fan of casual (Toll Free) Number +1-341-900-3252 and immersive gaming experiences, then having a Big Fish Games login account (Toll Free) Number +1-341-900-3252 is essential. Big Fish Games is a popular platform that offers a vast collection of puzzle, adventure, hidden object, and match-3 games, providing (Toll Free) Number +1-341-900-3252 entertainment for players (Toll Free) Number +1-341-900-3252 of all ages.

(Toll Free) Number +1-341-900-3252

Creating and accessing your (Toll Free) Number +1-341-900-3252 Big Fish Games login account (Toll Free) Number +1-341-900-3252 is simple and opens up many benefits. (Toll Free) Number +1-341-900-3252 With your account, you can save your game progress, access your purchased games from any device, and participate in exclusive offers and promotions. Whether you play on a PC, Mac, or mobile device, your account ensures that your gaming experience is seamless and personalized.

To get started, just sign up with your email and create a password to establish your Big Fish Games login account. Once logged in, you can browse the extensive game library, purchase new titles, and keep track of your (Toll Free) Number +1-341-900-3252 achievements and favorites. The account (Toll Free) Number +1-341-900-3252 also allows you to receive updates about new releases and special discounts.

If you ever forget your login details, Big Fish Games (Toll Free) Number +1-341-900-3252 provides straightforward (Toll Free) Number +1-341-900-3252 recovery options to help you regain access quickly. Keeping your account secure with a strong password is also recommended (Toll Free) Number +1-341-900-3252 to protect your game purchases and personal information.

(Toll Free) Number +1-341-900-3252

In summary, the Big Fish Games login account is your key to enjoying thousands of engaging games anytime, anywhere. It helps you keep everything organized and gives you the freedom to play your favorite games without (Toll Free) Number +1-341-900-3252 interruption. So, if you haven’t already, create your account today and dive into a world of fun!

(Toll Free) Number +1-341-900-3252

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

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 ‘College Football Bowl Games’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/college-football-bowl-gamese on 13 February 2022.

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

About this dataset

Background

Home field advantage is always the most desirable, but does data back it up? I’ve pulled stats on college football bowl games to see if having the home field advantage is all it is cracked up to be.

Methodology

The data collected was scraped from www.foxsports.com.

Source

The research and blog post can be found at The Concept Center

This dataset was created by Chase Willden and contains around 20000 samples along with Receiving Receiving Yards, Kicking Pat Made, technical information and other features such as: - Kick Return Kick Return Touchdowns - Passing Completions - and more.

How to use this dataset

• Analyze Kick Return Kick Return Avg in relation to Punt Return Punt Return Long
• Study the influence of Kicking Kicking Points on Kick Return Kick Return Long
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

Start A New Notebook!

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

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.

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.

Complete dataset used in the research study on Exploring How Mobile Games Simulate Real-World Business and Economics by Dr. Susan Thomas

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)<

This dataset includes important economic, demographic and sport data related to Summer Olympic games in Rio de Janeiro 2016. Dataset includes variables such as: GDP, GDP per capita, Inflation, Population total, Population 15-64, Economic Active Population, Corruption Perception Index, Medal rankings, and World Bank's country classification by income. Dataset can be used for any Rio 2016 Olympic games related analysis and any classical economic models.

The Chess Recognition Dataset (ChessReD) comprises a diverse collection of images of chess formations captured using smartphone cameras; a sensor choice made to ensure real-world applicability. The dataset is accompanied by detailed annotations providing information about the chess pieces formation in the images. Therefore, the number of annotations for each image depends on the number of chess pieces depicted in it. There are 12 category ids in total (i.e., 6 piece types per colour) and the chessboard coordinates are in the form of algebraic notation strings (e.g., "a8").

Dataset specifications

The dataset consists of 100 chess games, each with an arbitrary number of moves and therefore images, amounting to a total of 10,800 images being collected. It was split into training, validation, and test sets following a 60/20/20 split, which led to a total of 6,479 training images, 2,192 validation images, and 2,129 test images. Since two consecutive images of a chess game differ only by one move, the split was performed on game-level to ensure that quite similar images would not end up in different sets. The split was also stratified over the three distinct smartphone cameras (Apple iPhone 12, Huawei P40 pro, Samsung Galaxy S8) that were used to capture the images. The three smartphone cameras introduced variations to the dataset based on the distinct characteristics of their sensors.