All Xbox One Games Dataset

The Xbox One is a line of home video game consoles developed by Microsoft. Announced in May 2013, it is the successor to Xbox 360 and the third base console in the Xbox series of video game consoles. It was first released in North America, parts of Europe, Australia, and South America in November 2013 and in Japan, China, and other European countries in September 2014. It is the first Xbox game console to be released in China, specifically in the Shanghai Free-Trade Zone. Microsoft marketed the device as an "all-in-one entertainment system", hence the name "Xbox One". An eighth-generation console, it mainly competed against Sony's PlayStation 4 and Nintendo's Wii U a and later the Switch. This dataset contains details of all Xbox One Games published so far.

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Xbox Games

Games released for Xbox

About this dataset

This dataset contains a list of Xbox games, including their genres, developers, publishers, and release dates. With this information, you can get an idea of what kind of games are available for the Xbox platform, as well as when they were released. You can also see who developed and published each game

How to use the dataset

This dataset contains a list of Xbox games and their respective ratings. The ratings are provided by gamers who have played the game

Research Ideas

This dataset can be used to:

• Track the release dates of Xbox games over time;
• Study the trends in game genres popular among Xbox gamers; and
• Analyze the business relationship between Microsoft, as the owner of the Xbox platform, and video game developers and publishers who make titles for the console

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 Xbox game. (String) | | Genre | The genre of the Xbox game. (String) | | Developer(s) | The developer(s) of the Xbox game. (String) | | Publisher(s) | The publisher(s) of the Xbox game. (String) | | Release date | The release date of the Xbox game. (String) | | Release date.1 | The release date of the Xbox game in North America. (String) | | Release date.2 | The release date of the Xbox game in Europe. (String) |

File: df_4.csv | Column name | Description | |:--------------|:--------------| | 0 | | | 1 | |

File: df_9.csv | Column name | Description | |:--------------|:--------------| | 0 | | | 1 | |

File: df_10.csv | Column name | Description | |:--------------|:--------------| | 0 | | | 1 | |

File: df_3.csv

File: df_2.csv | Column name | Description | |:-------------------------------------|:------------------------------------------------------------------------------------------------| | Articles related to Xbox games | A list of Xbox games, including their genres, developers, publishers, and release dates. (List) | | Articles related to Xbox games.1 | A list of Xbox games, including their genres, developers, publishers, and release dates. (List) |

File: df_6.csv

File: df_7.csv | Column name | Description | |:-------------------|:-------------------------------| | vteMicrosoft | The name of the game. (String) | | vteMicrosoft.1 | The type of game. (String) |

File: df_8.csv | Column name | Description | |:--------------|:--------------| | 0 | | | 1 | |

File: df_5.csv | Column name | Description | |:--------------|:--------------| | 0 | | | 1 | |

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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...

Discovering Hidden Trends in Global Video Games Sales

Platforms, Genres, and Profitable Regions

By Andy Bramwell [source]

About this dataset

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

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How to use the dataset

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

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

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

Research Ideas

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

Acknowledgements

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

License

See the dataset description for more information.

Columns

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

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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

Column Name	Description
Name	The name of the video game.
Platform	The platform on which the game was released, such as PlayStation, Xbox, Nintendo, etc.
Year of Release	The year in which the game was released.
Genre	The genre of the video game, such as action, adventure, sports, etc.
Publisher	The company responsible for publishing the game.
NA Sales	The sales of the game in North America.
EU Sales	The sales of the game in Europe.
JP Sales	The sales of the game in Japan.
Other Sales	The sales of the game in other regions.
Global Sales	The total sales of the game across the world.
Critic Score	The average score given to the game by professional critics.
Critic Count	The number of critics who reviewed the game.
User Score	The average score given to the game by users.
User Count	The number of users who reviewed the game.
Developer	The company responsible for developing the game.
Rating	The rating assigned to the game by organizations such as the ESRB or PEGI.

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 ↑ ⠀⠀⠀

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

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

About Columns

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

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1. Why build a dataset?

I wanted to run data analysis and machine learning on a large dataset to build my data science skills but I felt out of touch with the various datasets available so I thought... how about I try and build my own dataset?

2. Why gaming data?

I wondered what data should be in the dataset and settled with online digital game purchases since I am an avid gamer. Imagine getting sales data from the PlayStation Store or Xbox Microsoft Store, this is what I was aiming to replicate.

3. Scope of the dataset

I envisaged the dataset to be data created through the purchase of a digital game on either the UK PlayStation Store or Xbox Microsoft Store. Considering this, the scope of dataset varies depending on which column of data you are viewing, for example: - Date and Time: purchases were defined between a start/end date (this can be altered, see point 4) and, of course, anytime across the 24hr clock - Geographically: purchases were setup to come from any postcode in the UK - in total this is over 1,000,000 active postcodes - Purchases: the list of game titles available for purchase is 24 - Registered Banks: the list of registered banks in the UK (as of 03/2022) was 159

4. Over 42,000 rows isn't enough?

To generate the dataset, I built a function in Python. This function, when called with the number of rows you want in your dataset, will generate the dataset. For example, calling function(1000) will provide you with a dataset with 1000 rows.

Considering this, if just over 42,000 rows of data (42,892 to be exact) isn't enough, feel free to check out the code on my GitHub to run the function yourself with as many rows as you want.

Note: You can also edit the start/end dates of the function depending on which timespan you want the dataset to cover.

5. Disclaimer - this is still a work in progress!

Yes, as stated above, this dataset is still a work in progress and is therefore not 100% perfect. There is a backlog of issues that need to be resolved. Feel free to check out the backlog.

One example of this is how on various columns, the distributions of data is equal, when in fact for the dataset to be entirely random, this should not be the case. An example of this issue is the Time column. These issues will be resolved in a later update.

Last updated: 24/04/2022

MultiPENG: Multimodal Dataset for Player Engagement Analysis in Video Games

Overview

This comprehensive multimodal dataset enables the study of player engagement in video games through synchronized physiological, behavioral, and visual data streams. It combines six data modalities (EEG, eye tracking, heart rate, OpenFace facial analysis, user inputs, and gameplay frames) with strategically placed self-reported engagement metrics.

Key Features

• Synchronized multimodal data from 39 participants playing FIFA'23 and Street Fighter V
• Fine-grained temporal alignment between data streams
• Natural engagement reporting leveraging game pause points to minimize disruption
• Multiple dimensions captured: engagement, interest, stress, and excitement
• 900 micro-game sessions with varied difficulty levels
• OpenFace facial analysis providing detailed facial features including head pose dynamics, facial landmarks, gaze direction, and 17 facial action unit intensities

Dataset Structure

The dataset is organized into two main directories:

Questionnaire/

• participants.csv: Contains demographic information for all participants including ID, age, gender, and self-reported expertise levels in FIFA and SF games
• submissions.csv: Contains session-specific metadata including unique submission ID, participant ID, game type, difficulty level, session number, timestamps, and the four engagement metrics (engagement, interest, stress, excitement) rated on a scale of 0-4

Samples/

Each participant has a dedicated folder (<id>/) containing their data across six modalities:

• EEG/: Raw signals from 14 channels (128 Hz), signal quality (0-1) at 0.5s intervals, and band powers (Alpha, Beta H/L, Gamma, Theta) at 8 Hz
• EYE/: Eye tracking data at 60 Hz, including fixation points, unfiltered gaze, pupil coordinates/diameters, blink presence/duration, and saccade measurements
• HR/: Heart rate data at 0.2 Hz (5-second intervals), containing BPM and confidence level (0-3)
• OpenFace/: Processed facial features extracted from webcam footage including head pose dynamics (translation, rotation, velocity, and acceleration vectors), facial landmarks, gaze direction estimates, and 17 facial action unit intensities
• XBOX/: Controller input data capturing button presses (a,b,x,y, bumpers) as binary states and stick positions as absolute values (-32767 to 32767)
• OBS/: Webcam footage at 30 fps (1280×1024 video with H.264 encoding, audio at 44.10 kHz/127 Kbps)

Benchmark Framework

The dataset includes a standardized benchmark framework implementing nested stratified group cross-validation with 7 outer folds, each divided into 7 inner folds. This ensures participant-level separation between training and testing data, providing a fair evaluation protocol for engagement detection models.

Human Panel Evaluation

A curated subset of 20 gameplay sessions is provided for human evaluation of player engagement, along with annotations from 14 human judges. This allows comparison between human capabilities and sensor-based approaches in assessing engagement.

File Naming Convention

Files follow this naming pattern: <id>_<sub>_<e>_<i>_<s>_<ex>.[csv|mp4]

Where: - <id>: participant_id - <sub>: submission_id - <e>: engagement score (0-4) - <i>: interest score (0-4) - <s>: stress score (0-4) - <ex>: excitement score (0-4)

Research Applications

This dataset supports various research directions including: - Development of real-time engagement estimation models - Cross-modal analysis of engagement indicators - Investigation of engagement patterns across different game genres - Evaluation of physiological vs. self-reported engagement metrics - Flow theory modeling using difficulty-skill relationships - Comparison of human vs. algorithmic engagement assessment

Baseline Models

Three baseline approaches for engagement prediction are provided: 1. Human Annotation: Demonstrates limited accuracy (50±12%) when using only visual cues 2. Flow Theory-Based: Achieves 66±6% accuracy using player skill and game difficulty 3. Multimodal Neural Architecture: Reaches 61±9% accuracy using EEG, eye tracking, and facial features

Code and Analysis

The code used for data collection, processing, and baseline analysis is available on GitHub: https://github.com/AmmarRashed/MultimodalEngagement

Ethics and Usage

This dataset was collected as part of a research study approved by the Research Ethics Board (REB) of the University of Ottawa (Protocol #H-07-23-9439). All participants provided informed consent for their data to be shared for academic and non-commercial research purposes only. In accordance with the ethics approval and ...

Video Game Sales and Ratings

Global Video Game Sales, Ratings, and User Insights

By Sumit Kumar Shukla [source]

About this dataset

The Video Games Sales and Ratings Dataset provides an in-depth view into the dynamic world of video games, offering a comprehensive analysis of sales and ratings across diverse platforms and publishers. This dataset contains valuable facets of information that bring to light various insights about the video game industry over the years.

The dataset includes critical aspects such as the Name of each individual video game which was accounted for in this data aggregation process. The name captures the branded title under which a specific game is marketed and sold within the global market.

Additionally, key details on numbers relating to sales are included as well; such as Global Sales which refers to the total number of copies each individual game has sold on all indicated platforms worldwide- recorded in millions; NA_Sales representing accumulated sales figures from North America- also captured in millions; JP_Sales showing similarly compiled data specifically for Japan and EU_Sales for Europe respectively, both equally reflected in millions.

There is more insightful granularity embedded within our Dataset including Other_Sales, that tallies copies sold outside of specifically mentioned regions (North American, European & Japanese markets), expanding our insights into an even wider spectrum.

This Dataset not only shares hard figures on sales but also valuable opinions voiced by professional critics & users alike with Critic_Count & User_Count detailing how many individuals had reviewed any specific product with Critic Score being an averaged rating given by critics while User Score echoes sentiments from regular end-users or consumers who purchased these products - showcasing public opinion on these games.

Critical parameters defining characteristics essential related to gaming experiences like Genre detail distinctive aspects or themes around gameplay found within respective titles while Platform lists down where these titles were played specifically (between options like PC based or console front like PS4, Xbox etc.). The Publisher spotlights deserved attention onto those who took upon themselves to disseminate this creative work unto masses while Developer's name elucidates those daring visionaries who birthed unique experiences with their own hands through coding & design.

Last but not least, the Rating as per ESRB (Entertainment Software Rating Board) is included to give a sense of what demographic brackets/age each title was intended and marketed for - an essential aspect for parents and individuals mindful about content consumed within video games.

In short, this Video Games Sales and Ratings Dataset offers insights into the vast world of video gaming from various impactful perspectives serving as a valuable learning resource to anyone interested in gaining understanding or deploying data-driven strategies within any facet of this industry

How to use the dataset

Exploring the Dataset

The first step in analyzing this dataset is getting familiarized with its structure:

• Name: This attributes refers to the name of each video game included in the dataset.
• Platform: This denotes the platform(s) on which a particular game operates.
• Year_of_Release: The year when a particular game was launched is depicted by this attribute.
• Genre: It indicates what type of genre does a certain video game correspond to.
• Publisher & Developer: These fields detail out which company has published and developed every game respectively.
• NA_Sales, EU_Sales, JP_Sales & Other_Sales: These signify sales numbers from North America (NA), Europe(EU), Japan(JP) regions as well as other parts of world respectively (measured in millions).
• Global_Sales: This category refers to overall international sales for each described gaming product.
• Critic_Score & User_score: It represents average scores attributed by critics or users; where higher indicates better reception mostly measured on a scale often spanning 0–10 or 0–100.
• Critic_Count & User_Count: They denote how many critics/users have rated particular games respectively. -**Rating**: ESRB's categorization for games (e.g., E for Everyone,T for Teen,M for Mature etc.) is portrayed through it.

Data Analysis Recommendations

Here are some suggestions based on common practices:

• Sales Performance: You can examine sales figures categorized regionally or globally, leading to understanding which games have ...

Overwatch is a team-based multiplayer first-person shooter video game developed and published by Blizzard Entertainment, which released on May 24, 2016 for PlayStation 4, Xbox One, and Windows. Described as a "hero shooter", Overwatch assigns players into two teams of six, with each player selecting from a roster of nearly 30 characters, known as "heroes", each with a unique style of play whose roles are divided into three general categories that fit their role: Offense, Defense, Tank, and Support. Players on a team work together to secure and defend control points on a map or escort a payload across the map in a limited amount of time.

I discovered this dataset on the Overwatch Subreddit here: https://www.reddit.com/r/Overwatch/comments/7o8hmg/my_friend_has_recorded_every_game_hes_played/

and there is sort of same dataset here: https://www.kaggle.com/mylesoneill/overwatch-game-records/home

Data was messy, so I try to clean it and make better and easier for visualising and analysing.

Columns as: time, result, map, team_role, character_1, character_2, character_3 and psychological_condition were transformed (clustered) into easy to use format.

Thanks to JustWingIt for collecting this amazing data!

And Myles O'Neill for bringing this data to Kaggle!

(1) Introduction

In this work, we want to collect network telemetry data related to Cloud Gaming applications. The objective is that the data collected can be analyzed, compared and useful to create solutions to improve the QoE of this type of service.

(2) Content

This dataset currently contain network telemetry data related to 3 different games played with the Xbox Cloud Gaming service. The games are: - Fortnite (Zero Build game mode) - Forza Horizon 5 - Mortal Kombat 11 (Offline mode)

For each game, many experiments of 15 minutes of gameplay were done, with 1 or 2 players connected. It was collected Packet Captures (PCAP).

Dataset is divided by type of connection (5G and wired), by games, and by number of players (1 or 2).

(3) Methodology

Games were played using laptops with Chrome browser. To collect the data, it was used a Raspberry Pi with P4Pi platform installed on it. P4Pi system is a Linux like, and can run tshark, so we used it to collect PCAPs in every experiment. Experiments were made using two different connections: 5G and wired. Data was collected on Centro Universitário Facens (for 5G connection) and Federal University of São Carlos - Sorocaba campus (for wired connection). In the following figure is represented our setup.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18694461%2Fcc611fa6106a88e1640042b279ed5e04%2FUntitled%20presentation.png?generation=1711490000766201&alt=media" alt="">

(4) Data catalog

Experiement	Game	Connection	Number of Players
Ex35	Fortnite	5G	1 Player
Ex39	Fortnite	5G	2 Players
Ex37	Forza Horizon 5	5G	1 Player
Ex40	Forza Horizon 5	5G	2 Players
Ex38	Mortal Kombat 11	5G	1 Player
Ex7	Fortnite	Wired	1 Player
Ex19	Fortnite	Wired	1 Player
Ex20	Fortnite	Wired	1 Player
Ex21	Fortnite	Wired	2 Players
Ex22	Fortnite	Wired	2 Players
Ex23	Fortnite	Wired	2 Players
Ex8	Forza Horizon 5	Wired	1 Player
Ex10	Forza Horizon 5	Wired	1 Player
Ex11	Forza Horizon 5	Wired	1 Player
Ex29	Forza Horizon 5	Wired	1 Player
Ex15	Forza Horizon 5	Wired	2 Players
Ex16	Forza Horizon 5	Wired	2 Players
Ex17	Forza Horizon 5	Wired	2 Players
Ex12	Mortal Kombat 11	Wired	1 Player
Ex13	Mortal Kombat 11	Wired	1 Player
Ex14	Mortal Kombat 11	Wired	1 Player
Ex24	Mortal Kombat 11	Wired	1 Player
Ex25	Mortal Kombat 11	Wired	1 Player
Ex30	Mortal Kombat 11	Wired	1 Player
Ex32	Mortal Kombat 11	Wired	2 Players
Ex33	Mortal Kombat 11	Wired	2 Players
Ex34	Mortal Kombat 11	Wired	2 Players

Overwatch is a team-based multiplayer online first-person shooter video game developed and published by Blizzard Entertainment. It was released in May 2016 for Windows, PlayStation 4, and Xbox One. Overwatch assigns players into two teams of six, with each player selecting from a roster of over 20 characters, known in-game as "heroes", each with a unique style of play, whose roles are divided into four general categories: Offense, Defense, Tank, and Support. Players on a team work together to secure and defend control points on a map or escort a payload across the map in a limited amount of time.

I discovered this dataset on the Overwatch Subreddit here: https://www.reddit.com/r/Overwatch/comments/7o8hmg/my_friend_has_recorded_every_game_hes_played/

It represents a ridiculous amount of effort in terms of manually recording game results. This data, whilst in some places incomplete, gives an unprecedented view into the experience of a single overwatch player over the course of years of gameplay. From it you can track the ups and downs, shifts in hero preference and all sorts of other exciting in game trends.

Thanks to JustWingIt for their amazing collecting this data.

I cleaned the data a little and put it into a single CSV.

History’s greatest warriors brawl to prove who’s the best in an epic test of strength and skill. These slugfests are salted with powerful weapons and gadgets. Every weapon you pick up changes your style of play

Brawlhalla is a free 2D platform fighting game that supports up to 8 local or online players with full cross-play for PC, Xbox One, Xbox Series X, PS5, PS4, iOS, and Android.

This dataset contains a curated list of video games developed or published by Tecmo Koei, compiled via web scraping from Wikipedia. Tecmo and Koei, two prominent Japanese video game companies, merged in 2009, creating a legacy of titles across a wide range of platforms and genres.

Purpose

The main purpose of this dataset is to serve as a learning tool for practicing data cleaning, standardization, and exploratory analysis. Real-world data, even when sourced from structured platforms like Wikipedia, often comes with inconsistencies, missing values, and formatting issues. This dataset offers a realistic example of how to:

• Clean textual data (e.g., standardizing genres and platforms).
• Handle missing or inconsistent entries.
• Normalize categorical values.
• Prepare scraped data for machine learning or visualization tasks.
• It's especially useful for students, junior data analysts, or anyone learning to work with messy data in Python using tools - like pandas, numpy, and regex.

New in This Version: Enhanced Data and Platform Explosion

This updated version of the dataset includes expanded coverage and a key pre-processed transformation to enhance its utility for analysis.

Previously, the dataset provided core information about game titles, platforms, release dates, genres, developers, publishers, and descriptions. In this release, we've focused on two significant improvements:

1. Expanded Game Coverage: We've broadened the scope of the original dataset to include a more comprehensive list of Tecmo Koei titles, ensuring a richer and more complete view of their extensive game catalog. This means you'll find even more games to analyze, providing a deeper understanding of their history and output.

2. Pre-processed Platform Data (Exploded View): To facilitate more granular analysis, particularly for games released on multiple platforms, we've included a transformed version of the dataset where the Platforms column has been "exploded."

Originally, the Platforms column might contain comma-separated values (e.g., "PC, PlayStation, Nintendo Switch"). This structure can be challenging for direct analysis when you want to count games per individual platform. The "exploded" dataset now presents each unique platform for a game as a separate row, duplicating the other game details. This means if "Ninja Gaiden" was released on "Xbox, PlayStation 3," it will appear as two separate rows in the exploded dataset—one for "Xbox" and one for "PlayStation 3."

This transformation significantly simplifies tasks like:

Counting games released on specific platforms. Analyzing platform-specific trends in genres or release dates. Creating more accurate visualizations of platform distribution. The original, untransformed data is also included, allowing users to practice the exploding technique themselves if desired.

This dataset is gathered from Metacritic.com using a web scraper. It includes 18 csv files for six gaming platforms (PC, Xbox One, Xbox Series X, PlayStation 4, PlayStation 5, and Nintendo Switch) each platform having three csv files for Details, Scores, and Reviews.

• 01_[platform]_details.csv columns:

Name: Name of the game

Link: The link of the game on Metacritic.com

Distributer: The distributer of the game

Developer: The developer of the game

Release Date: When the game released.

Also On: If available on other platform.

Summary: A description of the game.

Metascore: The overall score from critics.

Meta Status: The status of the Metascore.

# of Critic Reviews: Number of critics who gave the game a score.

Critic Positive: Number of positive critic reviews.

Critic Mixed: Number of mixed critic reviews.

Critic Negative: Number of negative critic reviews.

Userscore: The overall score from users.

Userscore Status: The status of the Userscore.

# of User Reviews: Number of users who gave the game a score.

User Positive: Number of positive user reviews.

User Mixed: Number of mixed user reviews.

User Negative: Number of negative user reviews.

Genres: The genre(s) of the game.

# Of Players: Maximum number of players.

Rating: The age restrictions.

Awards: The awards concerning the rankings of the game on metacritic.com

• 02_[platform]_scores.csv: This file includes the scores from each critic.
• 03_[platform]_reviews.csv: This file includes the reviews (just the short ones on Metacritic.com) from each critic.