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...

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

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.

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.

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 ↑ ⠀⠀⠀

Global Video Game Sales and Ratings

Global Overview of Video Game Sales, User and Critic Ratings

By LearnToViz [source]

About this dataset

The Video Game Sales and Ratings Dataset is a comprehensive collection of data points concerning the video game industry. Predominantly prepared with the intent of facilitating data-driven decision making, this dataset serves as an essential tool for game developers, publishers, critics and researchers interested in examining trends and patterns within this burgeoning domain.

Consisting of a wide gamut of variables such as platform availability, genre classification, publishing entities involved, developers responsible for creating the games to understanding their global reach through sales figures across various regions including North America (NA_Sales), Europe (EU_Sales), Japan (JP_Sales) and even other diverse regions consolidated under 'Other_Sales'- this dataset offers an in-depth exploration into the intricacies inherent in video gaming business dynamics.

Furthermore, since it includes both critic reviews and user ratings along with associated count metrics that denote how many individuals voiced their opinion via these ratings; it not only lends performance transparency but also ensures broad inclusivity to make assessment more holistic. Therein lies its uniqueness: aptly capturing both market reception (sales diligence) along with perceptual quality review criteria led by expert critics and public users alike.

To facilitate a thorough understanding our array fields also cover critical detail including 'Year_of_Release', offering temporal insight into when these games arrived on market shelves around the world; how they have been categorized ('Genre'), which platforms host these games ('Platform'); who took responsibility for developing them ('Developer') & then getting them promoted or distributed to target consumer base ('Publisher').

Our 'User_Count' column offers further informed perspective about community engagement levels while inclusion of 'Rating' variable provides standards-based categorical info about age-specific content appropriateness as per internationally recognized rating agency- Entertainment Software Rating Board. With all these multifaceted components combined together in one robust dataset- rich analysis like sales forecasting, trend identification or patterns discernment among others can be most effectively accomplished.

By providing such a wealth of diverse and detailed information, this dataset opens up a world of possibilities for analysis and investigation that stretches the boundaries of what we can learn about video games- both as entertainment artifacts and as commercial entities. Overall this dataset stands to be an informative resource to better comprehend the complex dynamics shaping the globally-flourishing video game industry

How to use the dataset

This guide will provide tips on how to effectively navigate this dataset for both beginners in Data Science and researchers with more advanced skills.

• Getting Started: Familiarize yourself with the general contents of the data file by checking all column descriptions. This will give you an overarching understanding of what kind of data you can find in this dataset.

• Conceptual Understanding: Understand the context each column provides for every unique video game title:

• Platform: Can help highlight which gaming platforms have optimal sales.

• Year_of_Release: Can provide insights into gaming market trends or allow creation of timeline analysis.

• Genre: Useful for analyzing popular genres or identifying niche markets.

• Publisher / Developer: Monitoring these can reveal industry leaders or developers that consistently produce well-received games.

• NA_Sales / EU_Sales / JP_Sales / Other_Sales / Global_Sales : These columns are instrumental in performing regional market analyses, studying performance indicators globally, or comparing regional popularity differences versus global acceptance levels.

• User Scores Vs Critic Scores: These two columns might present divergent perspectives about a video-game's quality; critics might highly rate games users didn't favor & vice versa. Exploring these discrepancies could prove intriguing!

• Choosing Tasks:

  • Exploratory Data Analysis (EDA): This could be your first step if you're starting out learning how to handle datasets; identify missing values/nulls & their impact on data analysis; extract meaningful summary statistics.

  • Data Visualization: Generate charts, scatter plots, heat maps etc. to encapsulate sale...

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

A survey conducted in the second quarter of 2025 found that around 91.5 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 82 percent of internet users were gamers.

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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.

Debt-To-Equity-Ratio Time Series for Light & Wonder Inc. Light & Wonder, Inc. operates as a cross-platform games company in the United States and internationally. The company operates through three segments: Gaming, SciPlay, and iGaming segments. The Gaming segment sells game content and gaming machine; video gaming terminals; video lottery terminals, including conversion kits and spare parts; and table game products, including automatic card shufflers, deck checkers, table roulette chip sorters and other land-based table gaming equipment. It also leases or provides gaming content, gaming machines, and server-based system; sells and supports casino-management system based software and hardware; and licenses proprietary table games content to commercial, tribal, and governmental gaming operators. The SciPlay segment develops, markets, and operates social games on various online platforms. It sells virtual coins, chips, or bingo cards, which players can use to play slot games, table games, and bingo games. The iGaming segment provides a suite of digital gaming content, distribution platforms, player account management systems, and other iGaming content and services. This segment also offers the Open Platform System, which offers a range of reporting and administrative functions and tools providing operators control over various areas of digital gaming operations. Light & Wonder, Inc. was incorporated in 1984 and is headquartered in Las Vegas, Nevada.

In 2024, Call of Duty: Black Ops 6, published by Activision Blizzard, was the top-selling video game in the United States based on dollar sales. EA Sports College Football 25 was in second place, followed by the shooter Helldivers II. The dominance of AAA gaming productions In the video-game industry, AAA (pronounced Triple-A) is a classification that is used when describing or talking about video games that are produced and distributed by major or mid-tier video game developers. These game productions typically have a large development and marketing budget. When looking at the top-selling video games in the United States in 2023, the majority of the best-selling titles were produced by the biggest gaming companies worldwide. In addition to several publicly listed video game companies, gaming platform owners Sony and Nintendo also produce top-selling games with Sony's Marvel's Spider-Man 2 and Nintendo’s The Legend of Zelda: Tears of the Kingdom also reaching rank four and five, respectively. Spotlight: Call of Duty series Activision Blizzard’s long-running Call of Duty franchise usually features in many end-of-the-year lists as it is one of the top-grossing and best-selling gaming franchises worldwide. As of April 2021, the Call of Duty (CoD) series has generated more than 400 million lifetime unit sales and the most recent entry in the series is Call of Duty: Modern Warfare III, which was released in November 2023. The annual releases of the game series are rotated between several game development studios which are subsidiaries of Activision Blizzard: Infinity Ward, Sledgehammer Games, Treyarch, and Raven Software.

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!

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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 | |:--------------|:-----------------------------------------------------------|...

Nintendo Switch Dedicated Video Game Sales Units: 2017-2020

Quantifying Unit Sales Performance

By Charlie Hutcheson [source]

About this dataset

This dataset contains dedicated video game sales unit data for the Nintendo Switch platform from 2017 to 2020, as reported by official Nintendo Investor Relations. It provides an snapshot of consumers’ buying trends over the past four years and helps us gain insightful understanding into the introduction, expansion and success of this platform across global markets. The data can be used to analyze multiple aspects such as performance of specific titles/genres/franchises, changes in market expectations over time and more. This chart helps to visualize the dynamic changes in these sales units over that four-year timeframe. From this chart we can gain valuable understanding about how successful various releases have been on this gaming console, what titles drove its popularity levels and more useful insights that could help other developers in creating future products for similar platforms

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How to use the dataset

This dataset contains historical sales unit data for the Nintendo Switch platform from 2017 to 2020. The original visualization provides a clear visual representation of the number of sales units over time. It is easy to discern which months have seen higher levels of sales, and which have seen lower ones.

Using this dataset, users can perform various analyses on the results to gain further insights into consumer trends and buy behavior associated with the Nintendo Switch platform. Users can also glean information on pricing strategies taken by Nintendo as well as consumer preferences over time in order to inform future business decisions.

In order to maximize use of this data set, users are encouraged to consider questions such as: What types of games do consumers prefer? How has their taste changed over time? What is the average amount spent per game by region or country? How often are certain consoles purchased or rented? And what role do discounts or promotions play in influencing purchasing decisions? By exploring these questions, users can begin understanding how different factors may be affecting overall demand for a product associated with the Nintendo Switch platform.

By analyzing this dataset, users also get an insight into how other competitors within the industry are affecting sales performance and allowing them take steps necessary for either surpassing competitors or maintaining dominance through suitable tactics like improved marketing campaigns or better-priced products that appeal more strongly customers’ needs and wants . In addition, examining this data enables companies keenly understand customer demands at detailed levels including whether customers prefer switch game bundles with extra features like custom skins etc., titles released during special times such as a holiday season that incite strong demand among buyers and also relevant discounts/promotions offered during times when people want/needing much needed break from regular routine life.. Ultimately , gaining greater insight into customer objectives allows firms efficiently manage their costs while maximizing profits through effective decisions based on reliable datasets such as that contained in this one instead rarely updated manual counts/observations which dont just lack comprehensiveness but also accuracy in nature.

Research Ideas

• Producing a mobile application to present the sales units in an intuitive and interactive way;
• Utilizing the data for machine learning algorithms to predict and analyze trends in Nintendo Switch dedicated video game sales units;
• Creating infographics and visualizations that can be used for promotional materials or to educate customers about the success of Nintendo Switch dedicated game sales

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the or...

As of 2025, puzzle games had the overall shortest time between installation and first purchase across mobile operating systems. On Android, the time to first purchase was 1.6 days after installation, compared to 1.7 days on iOS. The largest discrepancy occurred with match games, with only 1.7 days between installation and first purchase on iOS compared to three days on Android.

Context

Join your crewmates in a multiplayer game of teamwork and betrayal! Play online or over local wifi with 4-10 players as you attempt to hold your spaceship.

Content

This dataset contains a subset of cleaned up gameplay data assembled by Twitch user ScaldingHotSoup. The complete dataset is available in this Google Sheet. The dataset on Kaggle is cleaned up for easier analysis in R or Python. It's made up of four tabs from the original dataset:

• Player Analysis: Analysis of individual players
• Player Record: Counts for individual players
• Game Feed: Events that happened in a given game
• Crewmate Voting: How crew voted on each day of gameplay

Acknowledgements

Thanks to Twitch user ScaldingHotSoup for assembling the original dataset from which this is derived.

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.

ThisDataset was made by saving frames every few seconds from famous gameplay videos on Youtube

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.

Overview

The World of Warcraft Avatar History Dataset is a collection of records that detail information about player characters in the game over time. It includes information about their character level, race, class, location, and social guild. The Kaggle version of this dataset includes only the information from 2008 (and the dataset in general only includes information from the 'Horde' faction of players in the game from a single game server).

• Full Dataset Source and Information: http://mmnet.iis.sinica.edu.tw/dl/wowah/
• Code used to clean the data: https://github.com/myles-oneill/WoWAH-parser

Ideas for Using the Dataset

From the perspective of game system designers, players' behavior is one of the most important factors they must consider when designing game systems. To gain a fundamental understanding of the game play behavior of online gamers, exploring users' game play time provides a good starting point. This is because the concept of game play time is applicable to all genres of games and it enables us to model the system workload as well as the impact of system and network QoS on users' behavior. It can even help us predict players' loyalty to specific games.

Open Questions

• Understand user gameplay behavior (game sessions, movement, leveling)
• Understand user interactions (guilds)
• Predict players unsubscribing from the game based on activity
• What are the most popular zones in WoW, what level players tend to inhabit each?

Wrath of the Lich King

An expansion to World of Warcraft, "Wrath of the Lich King" (Wotlk) was released on November 13, 2008. It introduced new zones for players to go to, a new character class (the death knight), and a new level cap of 80 (up from 70 previously). This event intersects nicely with the dataset and is probably interesting to investigate.

Map

This dataset doesn't include a shapefile (if you know of one that exists, let me know!) to show where the zones the dataset talks about are. Here is a list of zones an information from this version of the game, including their recommended levels: http://wowwiki.wikia.com/wiki/Zones_by_level_(original) .

Update (Version 3): dmi3kno has generously put together some supplementary zone information files which have now been included in this dataset. Some notes about the files:

Note that some zone names contain Chinese characters. Unicode names are preserved as a key to the original dataset. What this addition will allow is to understand properties of the zones a bit better - their relative location to each other, competititive properties, type of gameplay and, hopefully, their contribution to character leveling. Location coordinates contain some redundant (and possibly duplicate) records as they are collected from different sources. Working with uncleaned location coordinate data will allow users to demonstrate their data wrangling skills (both working with strings and spatial data).

Game Boy Games

Games released for game boy

About this dataset

How do you determine whether a video game is good or bad? There are many factors to consider, from graphics to gameplay to story. But one of the most important factors is reception – how well the game is received by players and critics.

The Game Boy was one of the most popular handheld gaming consoles of all time, and it had a huge library of games. Some of these games were beloved by fans and critics alike, while others were met with indifference or even outright hostility.

So, what makes a good Game Boy game? And what makes a bad one? This dataset attempts to answer those questions by collecting information on every Game Boy game ever released, including their reception von players and critics

How to use the dataset

The dataset can be used to examine the successes and failures of individual games or groups of games on the Game Boy platform

Research Ideas

• Examining the most and least successful games on the Game Boy platform in terms of sales, reviews, or player engagement.
• Determining which developers or publishers were most successful at releasing quality games on the Game Boy platform.
• Investigating how the release date impacted the success of a Game Boy game in different regions

Acknowledgements

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: df_1.csv | Column name | Description | |:-------------------|:---------------------------------------------------------------| | Title | The title of the Game Boy game. (String) | | Developer(s) | The developer(s) of the Game Boy game. (String) | | Publisher(s) | The publisher(s) of the Game Boy game. (String) | | Release date | The release date of the Game Boy game. (Date) | | Release date.1 | The release date of the Game Boy game in North America. (Date) | | Release date.2 | The release date of the Game Boy game in Europe. (Date) |

File: df_4.csv | Column name | Description | |:-----------------|:-------------------------------------------------------------| | Title | The title of the Game Boy game. (String) | | Developer(s) | The developer(s) of the Game Boy game. (String) | | Publisher(s) | The publisher(s) of the Game Boy game. (String) | | Release date | The release date of the Game Boy game. (Date) | | Region | The region in which the Game Boy game was released. (String) |

File: df_3.csv | Column name | Description | |:-----------------|:-------------------------------------------------------------| | Title | The title of the Game Boy game. (String) | | Developer(s) | The developer(s) of the Game Boy game. (String) | | Publisher(s) | The publisher(s) of the Game Boy game. (String) | | Region | The region in which the Game Boy game was released. (String) |

File: df_2.csv | Column name | Description | |:-------------------|:---------------------------------------------------------------| | Title | The title of the Game Boy game. (String) | | Developer(s) | The developer(s) of the Game Boy game. (String) | | Publisher(s) | The publisher(s) of the Game Boy game. (String) | | Release date | The release date of the Game Boy game. (Date) | | Release date.1 | The release date of the Game Boy game in North America. (Date) | | Release date.2 | The release date of the Game Boy game in Europe. (Date) |

File: df_6.csv

File: df_5.csv

Learn With Fun

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Let us analyze the games we daily play on our mobile phone !!

In the past couple of years, Mobile Battle Royale games have become quite popular amongst the gaming community. Due to this reason, there have been many e-sports tournaments of these games. This leads to analyzing each aspect of the game for preparing for tournaments. This dataset contains stats of guns used in the following battle royale games.

Games Included : 1. PUBG Mobile 2. Call of Duty Mobile 3. Garena Free Fire

This is my first dataset, so please put up a comment, or create a discussion forum if any issues found, or any changes needed. I will try to solve it as soon as possible.

Please upvote if you find it helpful. Thank you for your time!