Video gaming has been rising rapidly to become one of the primary entertainment media, especially during the COVID-19 pandemic. Playing video games has been reported to associate with many psychological and behavioral traits. However, little is known about the connections between game players' behaviors in the virtual environment and environmental perceptions. Thus, the current data set offers valuable resources regarding environmental worldviews and behaviors in the virtual world of 640 Animal Crossing: New Horizons (ACNH) game players from 29 countries around the globe. The data set consists of six major categories: 1) socio-demographic profile, 2) COVID-19 concern, 3) environmental perception, 4) game-playing habit, 5) in-game behavior, and 6) game-playing feeling. By making this data set open, we aim to provide policymakers, game producers, and researchers with valuable resources for understanding the interactions between behaviors in the virtual world and environmental perceptions, which could help produce video games in compliance with the United Nations (UN) Sustainable Development Goals.

See more: https://doi.org/10.1162/dint_a_00111

Other repository: Quan-Hoang Vuong; Manh-Toan Ho; Viet-Phuong La; Tam-Tri Le; Thanh Huyen T. Nguyen; Minh-Hoang Nguyen. A multinational dataset of game players’ behaviors in a virtual world and environmental perceptions(V1). 2021. Science Data Bank. 2021-10-09. cstr:31253.11.sciencedb.j00104.00098; https://datapid.cn/31253.11.sciencedb.j00104.00098

This dataset provides detailed information about 120 different video games. Each entry in the dataset represents a video game with the following attributes:

• Title: The name of the video game.
• Genre: The category or type of gameplay, such as Action-Adventure, First-Person Shooter, RPG, etc.
• Platform: The gaming system(s) on which the game can be played, such as PC, PlayStation, Xbox, Switch, or Multi--platform.
• ReleaseYear: The year in which the game was released.
• NumPlayers: The maximum number of players that can play the game simultaneously.
• AvgRating: The average rating of the game, typically on a scale from 0 to 10

Includes 24 hour recall data that children were instructed to fill-out describing the previous day’s activities at baseline, weeks 2 and 4 of the intervention, after the intervention (6 weeks), and after washout (10 weeks). Includes accelerometer data using an ActiGraph to assess usual physical and sedentary activity at baseline, 6 weeks, and 10 weeks. Includes demographic data such as weight, height, gender, race, ethnicity, and birth year. Includes relative reinforcing value data showing how children rated how much they would want to perform both physical and sedentary activities on a scale of 1-10 at baseline, week 6, and week 10. Includes questionnaire data regarding exercise self-efficacy using the Children’s Self-Perceptions of Adequacy in and Predilection of Physical Activity Scale (CSAPPA), motivation for physical activity using the Behavioral Regulations in Exercise Questionnaire, 2nd edition (BREQ-2), motivation for active video games using modified questions from the BREQ-2 so that the question refers to motivation towards active video games rather than physical activity, motivation for sedentary video games using modified questions from the BREQ-2 so that the question refers to motivation towards sedentary video games behavior rather than physical activity, and physical activity-related parenting behaviors using The Activity Support Scale for Multiple Groups (ACTS-MG). Resources in this dataset:Resource Title: 24 Hour Recall Data. File Name: 24 hour recalldata.xlsxResource Description: Children were instructed to fill out questions describing the previous day's activities at baseline, week 2, and week 4 of the intervention, after the intervention (6 weeks), and after washout (10 weeks).Resource Title: Actigraph activity data. File Name: actigraph activity data.xlsxResource Description: Accelerometer data using an ActiGraph to assess usual physical and sedentary activity at baseline, 6 weeks, and 10 weeks.Resource Title: Liking Data. File Name: liking data.xlsxResource Description: Relative reinforcing value data showing how children rated how much they would want to perform both physical and sedentary activities on a scale of 1-10 at baseline, week 6, and week 10.Resource Title: Demographics. File Name: Demographics (Birthdate-Year).xlsxResource Description: Includes demographic data such as weight, height, gender, race, ethnicity, and year of birth.Resource Title: Questionnaires. File Name: questionnaires.xlsxResource Description: Questionnaire data regarding exercise self-efficacy using the Children's Self-Perceptions of Adequacy in and Predilection of Physical Activity Scale (CSAPPA), motivation for physical activity using the Behavioral Regulations in Exercise Questionnaire, 2nd edition (BREQ-2), motivation for active video games using modified questions from the BREQ-2 so that the question refers to motivation towards active video games rather than physical activity, motivation for sedentary video games using modified questions from the BREQ-2 so that the question refers to motivation towards sedentary video games behavior rather than physical activity, and physical activity-related parenting behaviors using The Activity Support Scale for Multiple Groups (ACTS-MG).

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.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15126770%2F7d5374215511bb7cf264fab8a294bc3a%2Fheader.jpg?generation=1704969406449875&alt=media" alt="">

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

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15126770%2F9bb6a4f0ee6d69ea160b12f4d1ca3e30%2Fdata_1.jpg?generation=1704968884700538&alt=media" alt="">

About backloggd.com

"Backloggd is a place to keep your personal video game collection. Every game from every platform is here for you to log into your journal. Follow friends along the way to share your reviews and compare ratings. Then use filters to sort through your collection and see what matters to you. Keep a backlog of what you are currently playing and what you want to play, see the numbers change as you continue to log your playthroughs. There's Goodreads for books, Letterboxd for movies, and now Backloggd for games." - from the site backloggd.com.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15126770%2F4e12014a1f38e1167a5cf66202ebf9d7%2Fdata_2.jpg?generation=1704968935015630&alt=media" alt="">

"All game related metadata comes from the community driven database IGDB. This includes all game, company and platform data you see on the site." - from the site backloggd.com

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15126770%2F799831cb18c3b74f1c3f6e8a023af723%2Fdata_3.jpg?generation=1704968996471054&alt=media" alt="">

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? - 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 rating of a video game on the number of reviews left or the total number of players? - which game genres, platforms and developers are the most common (the most video games released of all time)? - which game genres, platforms and developers have the highest total number of players (have the highest total number of players ever)? - which game genres, platforms and developers have the highest average video game ratings?

If you have enough experience, try solving a multi-label classification problem. Train a model that can classify a video game description into one or more genres: - which models are best suited for this, and which should not be used? - what is the best way to convert text to features? How will lemmatization of text affect the predictive ability of the model? - which metric should be chosen to evaluate the model? - Is the model calibrated enough after training to trust its probabilistic forecasts? - can adding new data improve the predictive ability of the model?

Field descriptions:

The data contains the following fields: 1. games - basic data: - id - video game identifier (primary key); - name - name of the video game; - date - release date of the video game; - rating - average rating of the video game; - reviews - number of reviews; - plays - total number of players; - playing - number of players currently; - backlogs - the number of additions of a video game to the backlog; - wishlists - the number of times a video game has been added to “favorites”; - description - description of the video game. 2. developers - developers (publishers): - id - video game identifier (foreign key); - developer - developer (publisher) of a video game. 3. platforms - gaming platforms: - id - video game identifier (foreign key); - platform - gaming platform. 4. genres - game genres: - id - video game identifier (foreign key); - genre - video game genre. 5. scores - user ratings: - id - video game identifier (foreign key); - score - score (from 0.5 to 5 in increments of 0.5); - amount - number of users. 6. Video game posters.

Data update

The website backloggd.com contains detailed roadmap with changes that may be implemented over time on the website, among them: - additional information about the game: DLC status, all companies, alternative names and other extensive information about the game; - categorization of games: which games are DLC, demo versions, canceled, beta versions, etc.; - personalized game covers: IGDB now supports localized covers; - release dates: games with one date are too easy, in this case, multiple release dates will be shown for different stages/regions.

!...

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.

Context: Valorant, developed by Riot Games, has quickly become one of the most popular tactical first-person shooter games since its release. The game emphasizes strategic team play, individual skills, and tactical execution, making it a fascinating subject for performance analysis. Understanding the various metrics that contribute to player success can offer insights into effective strategies and gameplay techniques. This dataset was created to help players, coaches, and analysts delve into the detailed aspects of player performance and identify key areas for improvement.

Sources: The data for this dataset was collected from various online sources, including:

In-Game Statistics: Aggregated from player profiles and match histories available within the game client. Third-Party Valorant Trackers: Websites and tools that track player statistics and match performance, such as Tracker.gg and Blitz.gg. Community Contributions: Insights and data shared by the Valorant community, including professional players, streamers, and analysts, who often provide detailed breakdowns of their gameplay. Inspiration: The inspiration for compiling this dataset stems from several key areas:

Performance Analysis: In competitive gaming, understanding the granular details of player performance is crucial for improvement. Metrics like win rate, damage per round, and headshot percentage provide actionable insights. Strategic Development: By analyzing this data, players and teams can develop better strategies, identify strengths and weaknesses, and tailor their training regimes accordingly. Predictive Modeling: The dataset serves as a foundation for building predictive models to forecast future performance, which can be useful for coaching, match preparation, and scouting new talent. Community Engagement: Providing this dataset to the wider Valorant community fosters engagement and encourages collaborative analysis. It allows enthusiasts to test hypotheses, share findings, and contribute to a deeper understanding of the game. Educational Purposes: For educators and students in data science, sports analytics, and game design, this dataset offers a real-world application of data analysis techniques and methodologies. Future Directions: The dataset can be expanded by including additional metrics such as agent pick rates, map-specific performance, and team composition analysis. Incorporating more granular data over longer periods can also enhance the depth of analysis and provide a more comprehensive view of player performance trends.

By sharing this dataset, we aim to empower the Valorant community with data-driven insights that can elevate gameplay, inform strategic decisions, and contribute to the overall growth of the esports ecosystem.

In this study, we investigated the extent to which adolescents who spend time playing violent video games exhibit higher levels of aggressive behaviour when compared with those who do not. A large sample of British adolescent participants (n = 1004) aged 14 and 15 years and an equal number of their carers were interviewed. Young people provided reports of their recent gaming experiences. Further, the violent contents of these games were coded using official E.U. and US ratings, and carers provided evaluations of their adolescents' aggressive behaviours in the past month. Following a preregistered analysis plan, multiple regression analyses tested the hypothesis that recent violent game play is linearly and positively related to carer assessments of aggressive behaviour. Results did not support this prediction, nor did they support the idea that the relationship between these factors follows a nonlinear parabolic function. There was no evidence for a critical tipping point relating violent game engagement to aggressive behaviour. Sensitivity and exploratory analyses indicated these null effects extended across multiple operationalizations of violent game engagement and when the focus was on another behavioural outcome, namely, prosocial behaviour. The discussion presents an interpretation of this pattern of effects in terms of both the ongoing scientific and policy debates around violent video games, and emerging standards for robust evidence-based policy concerning young people's technology use.

Overview

Information of more than 110,000 games published on Steam. Maintained by Fronkon Games. This dataset has been created with this code (MIT) and use the API provided by Steam, the largest gaming platform on PC. Data is also collected from Steam Spy. Only published games, no DLCs, episodes, music, videos, etc. Here is a simple example of how to parse json information:

Simple parse of the 'games.json' file.

import os import json

dataset = {} if… See the full description on the dataset page: https://huggingface.co/datasets/FronkonGames/steam-games-dataset.

Analysis of ‘Video Games Sales Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sidtwr/videogames-sales-dataset on 20 November 2021.

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

Context

Motivated by Gregory Smith's web scrape of VGChartz Video Games Sales, this data set simply extends the number of variables with another web scrape from Metacritic. Unfortunately, there are missing observations as Metacritic only covers a subset of the platforms. Also, a game may not have all the observations of the additional variables discussed below. Complete cases are ~ 6,900

Content

Alongside the fields: Name, Platform, Year_of_Release, Genre, Publisher, NA_Sales, EU_Sales, JP_Sales, Other_Sales, Global_Sales, we have:-

Critic_score - Aggregate score compiled by Metacritic staff Critic_count - The number of critics used in coming up with the Critic_score User_score - Score by Metacritic's subscribers User_count - Number of users who gave the user_score Developer - Party responsible for creating the game Rating - The ESRB ratings

Acknowledgements

This repository, https://github.com/wtamu-cisresearch/scraper, after a few adjustments worked extremely well!

Inspiration

It would be interesting to see any machine learning techniques or continued data visualisations applied on this data set.#

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

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.

These datasets contain reviews from the Steam video game platform, and information about which games were bundled together.

Metadata includes

• reviews

• purchases, plays, recommends (likes)

• product bundles

• pricing information

Basic Statistics:

• Reviews: 7,793,069

• Users: 2,567,538

• Items: 15,474

• Bundles: 615

This is a dataset of the video game sales from 1978 to current 01/11/2024. Sales are in millions, there are 2 categories with special requirements one is the All platform games. These are sales for the game across all platforms, due to how the website stores this information not all platforms may be listed on that line (divided by |) but each game should be in the dataset so you can determine this with coding. The second is Series, this is the total sales of a video game series, all games should be listed for these divided by |.

I may updated this at times but it should not be expected.

This dataset was scraped using this scraper I forked on Github located here https://github.com/JasonR0124/vgchartzScrape.

A global consumer survey conducted in March 2024 found that 18 percent of respondents were more likely to buy a video game if it was advertised as a collector or limited edition. However, 45 percent of respondents stated that they were not interested in limited edition releases.

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.

This data-based effort documents and categorizes the relevant digital games in gaming history by analyzing two types of documental sources: (A) the most playable games (based on reports on sales) and (B) the most acclaimed Digital games by the specialized Critic. (A) The approach to ascertain the most played Digital games was thought by the analysis of the Entertainment Software Association (ESA) (ESA, 2009, 2010, 2011, 2012, 2013,2014, 2016, 2018). Regarding (B), six specialized critics' opinions on the most acclaimed digital games were selected for document analysis. From specialized Digital game journalism, the list of best games on the websites Euro Gamer, GamesRadar, IGN, and Polygon. Two Generalist News Media fonts were added: the British newspaper The Guardian and the American news magazine Time. The last source selected was Metacritic, a specialized entertainment evaluation and criticism website. (A) is justified considering that ESA is a nonprofit association with goals to observe, analyze, and unify the gaming industry, helping in terms of justice support by creating expansion opportunities for associates. ESA has several associates such as ElectronicArts, Konami, Microsoft, Bandai Namco Entertainment, Nintendo, Sony Interactive Entertainment, Square Enix Take-Two Interactive, Ubisoft, Warner Bros, and other companies with a less economic impact on the digital games business. Since 2008, ESA has published its annual report with data illustrating the industry's state in the corresponding year by reporting data as its total revenue. With the digital games selected in (B), it is possible to construct a broader sample and bridge the gaps in the ESA reports. The sources chosen in (B) are based on geographical representation covering both digital and printed press: Eurogamer is a British digital game journalism website publishing in-depth analyses and criticism of digital games and gaming culture. The top 10 games of the generation is a list published in September 2020 by the editor-in-chief. The author invited 19 persons from the game business: game developers, critics, and journalists. They were asked to submit a list of their five favorite games, any kind or platform. "Games Radar", or "GamesRadar+" in its original denomination brand, is an entertainment website dedicated to digital game-related news, previews, and reviews and is a member of the Independent Press Standards Organisation. In its list of The 100 best games ever, the editorial team (50 specialized journalists) picked the most-critical digital games, privileging fun and enjoyment for the Player before the historical significance of the Digital game. "Polygon" is an American Digital game specialized website that publishes reviews, guides, videos, and news on popular culture and entertainment-related. For their list, the editorial staff asked everyone (including the audience) to vote for the best Digital game based on innovation, polish, and durability rather than personal taste. They excluded games released in 2017 to eliminate recency bias (the year before the list was published). They left out sequels and were too similar to the games before them. In addition to gathering votes from the "Polygon" team, the editorial staff worked with a group of external and freelance writers to pull in their input. Collecting all those votes, they combined the data and the final list. "The Guardian" and "TIME" are generalistic press companies from the United Kingdom and the United States of America, respectively. The Guardian's list reflects the authors' vision. (journalists specializing in digital games) on the best 50 video games of the 21st century. TIME's list reports the tech's team of multiple generations of gamers, resulting in a list of 50 digital games. The last documentary source analyzed, "Metacritic," is a specialized critic website on entertainment (digital games, television, cinema, and music). To evaluate media content, "Metacritic" attributes a "Metascore." Any content featured on "Metacritic" gets a "Metascore" when had collected at least four critics' reviews. It was picked from the website the top 100 meta-scores from Metacritic's top-ranked digital games. In addition, the Digital games were selected by their titles, meaning that repeated games (different platforms) were disregarded. The list culminated in 393 different digital games; on the data based, it is possible to check: the documental sources where the digital games were cited; the year in which digital games were published for the first time; the series or franchise were each gama belongs; the Playability mode of each game; the Spatial Dimensionality; The number of platforms louched; the name of Digital Game Protagonist (if applicable); the name of the main playable Character (if applicable); the taxonomy of the playable Character; the perspective of the playable Character; check if there is a Narrative and the digital game genre.

In the first quarter of the fiscal year 2026, game publisher Electronic Arts (EA) generated over 1.08 billion U.S. dollars in net bookings via live services. Full game sales generated 214 million U.S. dollars in net bookings during the same period. Net bookings are defined as the net number of products and services sold digitally or sold-in physically in the period. Net bookings are calculated by adding total net revenue to the change in deferred net revenue for online-enabled games. Live services are EA’s revenue generator Gaming companies have pivoted to monetizing their titles beyond the initial purchase and are constantly working on keeping online players engaged via new characters, battle seasons and other in-game content. EA’s portfolio of live service games includes sports franchises EA Sports FC, and Madden NFL, as well as Battle Royale title Apex Legends, first-person shooter Battlefield V, and life simulation the Sims. All these titles are long-running series with new installments and downloadable content (DLC) being released regularly. On the flip side, the focus on live-service games means fewer big new releases, leading to a stagnation of the market in regard to new IPs (IP is short for intellectual property and a new IP refers to a new game which is not a spin-off or sequel of an existing game). Sports gamers are likely to spend money EA is constantly seeking to expand its EA Sports portfolio. In July 2025, EA released its first College Football video game after an eleven-year break since NCAA Football 14. This strategic decision is driven by several factors. First, sports gaming audiences are highly engaged and willing to spend money on additional gaming content. Second, any college football game will include rosters without names, images or likenesses or real college players to adhere to NCAA rules, which works in EA’s favor as the company as this keeps licensing costs low. And last, college football is a major pastime in EA’s biggest single market, the United States. All the pieces are lining up for EA to have another major moneymaker on their hands. This strategy paid off, as EA Sports College Football 25 became one of the top-selling video games in the United States in 2024.

Despite the challenges associated with virtually mediated communication, remote collaboration is a defining characteristic of online multiplayer gaming communities. Inspired by the teamwork exhibited by players in first-person shooter games, this study investigated the verbal and behavioral coordination of four-player teams playing a cooperative online video game. The game, Desert Herding, involved teams consisting of three ground players and one drone operator tasked to locate, corral, and contain evasive robot agents scattered across a large desert environment. Ground players could move throughout the environment, while the drone operator’s role was akin to that of a “spectator” with a bird’s-eye view, with access to veridical information of the locations of teammates and the to-be-corralled agents. Categorical recurrence quantification analysis (catRQA) was used to measure the communication dynamics of teams as they completed the task. Demands on coordination were manipulated by varying the ground players’ ability to observe the environment with the use of game “fog.” Results show that catRQA was sensitive to changes to task visibility, with reductions in task visibility reorganizing how participants conversed during the game to maintain team situation awareness. The results are discussed in the context of future work that can address how team coordination can be augmented with the inclusion of artificial agents, as synthetic teammates.

Dataset

This dataset was created by therocket290

Contents

The dataset consists of interview transcripts with people who spend a lot of time playing video games. The interviewees include people who play video games competitively for at least 30 hours a week and people who have sought help for compulsive gaming. The interviews are follow-up interviews, and the same individuals were interviewed for the first time a year earlier. For the dataset containing the first round of interviews, see dataset FSD3678 archived at FSD. In the first part of the follow-up interviews, the interviewees were asked whether there had been any changes in their digital gaming habits compared to a year ago. The interviewees were also asked about any changes in their career, family and friends. Next, they were asked to give a day-by-day description of what a normal week of digital gaming was like for them and to describe in as much detail as possible one digital gaming experience from the previous month. Additionally, the interviews included questions about the interviewees' other hobbies and their satisfaction with their current job. In relation to gaming, the interviewees were asked whether they felt that they spent too much time playing digital games. Background information included, among others, the interviewee's gender, information on which interviewee group the interviewee was part of, and the date of the interview. The interview identifier makes it possible to compare data between each interviewee's first interview and follow-up interview. The data were organised into an easy to use HTML version at FSD.