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.

As of June 2024, 21 percent of U.S. adults aged 18 to 29 years spent six to ten hours per week playing video games. Overall, respondents from this age group were also more likely than others to be heavy gamers - a total of eight percent played video games more than 20 hours in an average week. In total, weekly gaming usage was more likely among younger adults in the United States.

On average, people aged 15 to 24 years in the United States spent almost ** minutes per day playing games in 2023. People in this age group spent more time on average playing games than any other age group in 2023.

Video gaming is no longer a hobby exclusively enjoyed by the young. As generations have grown up with video games a normal part of life, the age of the average gamer also increases. During a 2023 survey, 25 percent of video game players still come from the 27 to 42 years age demographic, and 19 percent are 59 years and older. Time spent gaming In 2023, Americans aged between 15 to 19 years spent 98.4 minutes on gaming or leisurely computer use during an average day. The age demographic which devoted the least amount of time to gaming was the 55 to 64 years category. Members of this age demographic spent an average of just 17.4 minutes playing on the computer during an average day.

A January 2021 of global gaming markets found that 10.4 percent of gamers aged 18 to 25 years spent an average of more than 20 hours per week on gaming activities. However, gamers in that particular age group only averaged 7.97 weekly hours of gaming. In contrast, 12.6 percent of responding gamers aged 60 years and over spent more than 20 hours a week on gaming.

A January 2021 survey found that gamers in ***** spent the most time playing games per week, averaging about ***** weekly hours of gaming time. Vietnamese gamers were ranked second with about ***** hours of gaming time per week. The global average was **** weekly hours of gaming.

According to a survey on gaming in Singapore in 2020, **** percent of respondents stated that they spent about *** to **** hours a week playing video games. This figure was expected to increase this year due to the COVID-19 pandemic.

Frequency of gaming

Gaming is a popular past time in Singapore, and many Singaporeans engage in playing online or video games daily. For the majority of Singaporeans, an average gaming session would last between less than an hour to ***** hours. However, this changed significantly with age – the majority of older gamers average only up to *** hours per session. Singapore ranks among the top countries in Asia for time spent playing video games, and this has had consequences on everyday life.

Gaming in COVID-19

Too much time spent playing video games was seen by Singaporeans as being detrimental to mental health ; however, the effects could be just the opposite during the COVID-19 pandemic. Playing online games has become a popular and even healthy form of spending time during lockdowns and quarantines. Furthermore, socially interactive online games such as multiplayer games provide people with an outlet to communicate with others during a time when social interaction has been limited.

When asked about "Hours spent on playing video games per week", ** percent of U.S. respondents answer "1 to * hours". This online survey was conducted in 2025, among ****** consumers. Looking to gain valuable insights about video gamers worldwide? Check out our reports on customers of video game stores and subscription services. These reports give readers a thorough picture of the people who use these platforms, including their identities, preferences, opinions, and methods of communication.

Author: Víctor Yeste. Universitat Politècnica de Valencia.The object of this study is the design of a cybermetric methodology whose objectives are to measure the success of the content published in online media and the possible prediction of the selected success variables.In this case, due to the need to integrate data from two separate areas, such as web publishing and the analysis of their shares and related topics on Twitter, has opted for programming as you access both the Google Analytics v4 reporting API and Twitter Standard API, always respecting the limits of these.The website analyzed is hellofriki.com. It is an online media whose primary intention is to solve the need for information on some topics that provide daily a vast number of news in the form of news, as well as the possibility of analysis, reports, interviews, and many other information formats. All these contents are under the scope of the sections of cinema, series, video games, literature, and comics.This dataset has contributed to the elaboration of the PhD Thesis:Yeste Moreno, VM. (2021). Diseño de una metodología cibermétrica de cálculo del éxito para la optimización de contenidos web [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176009Data have been obtained from each last-minute news article published online according to the indicators described in the doctoral thesis. All related data are stored in a database, divided into the following tables:tesis_followers: User ID list of media account followers.tesis_hometimeline: data from tweets posted by the media account sharing breaking news from the web.status_id: Tweet IDcreated_at: date of publicationtext: content of the tweetpath: URL extracted after processing the shortened URL in textpost_shared: Article ID in WordPress that is being sharedretweet_count: number of retweetsfavorite_count: number of favoritestesis_hometimeline_other: data from tweets posted by the media account that do not share breaking news from the web. Other typologies, automatic Facebook shares, custom tweets without link to an article, etc. With the same fields as tesis_hometimeline.tesis_posts: data of articles published by the web and processed for some analysis.stats_id: Analysis IDpost_id: Article ID in WordPresspost_date: article publication date in WordPresspost_title: title of the articlepath: URL of the article in the middle webtags: Tags ID or WordPress tags related to the articleuniquepageviews: unique page viewsentrancerate: input ratioavgtimeonpage: average visit timeexitrate: output ratiopageviewspersession: page views per sessionadsense_adunitsviewed: number of ads viewed by usersadsense_viewableimpressionpercent: ad display ratioadsense_ctr: ad click ratioadsense_ecpm: estimated ad revenue per 1000 page viewstesis_stats: data from a particular analysis, performed at each published breaking news item. Fields with statistical values can be computed from the data in the other tables, but total and average calculations are saved for faster and easier further processing.id: ID of the analysisphase: phase of the thesis in which analysis has been carried out (right now all are 1)time: "0" if at the time of publication, "1" if 14 days laterstart_date: date and time of measurement on the day of publicationend_date: date and time when the measurement is made 14 days latermain_post_id: ID of the published article to be analysedmain_post_theme: Main section of the published article to analyzesuperheroes_theme: "1" if about superheroes, "0" if nottrailer_theme: "1" if trailer, "0" if notname: empty field, possibility to add a custom name manuallynotes: empty field, possibility to add personalized notes manually, as if some tag has been removed manually for being considered too generic, despite the fact that the editor put itnum_articles: number of articles analysednum_articles_with_traffic: number of articles analysed with traffic (which will be taken into account for traffic analysis)num_articles_with_tw_data: number of articles with data from when they were shared on the media’s Twitter accountnum_terms: number of terms analyzeduniquepageviews_total: total page viewsuniquepageviews_mean: average page viewsentrancerate_mean: average input ratioavgtimeonpage_mean: average duration of visitsexitrate_mean: average output ratiopageviewspersession_mean: average page views per sessiontotal: total of ads viewedadsense_adunitsviewed_mean: average of ads viewedadsense_viewableimpressionpercent_mean: average ad display ratioadsense_ctr_mean: average ad click ratioadsense_ecpm_mean: estimated ad revenue per 1000 page viewsTotal: total incomeretweet_count_mean: average incomefavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesterms_ini_num_tweets: total tweets on the terms on the day of publicationterms_ini_retweet_count_total: total retweets on the terms on the day of publicationterms_ini_retweet_count_mean: average retweets on the terms on the day of publicationterms_ini_favorite_count_total: total of favorites on the terms on the day of publicationterms_ini_favorite_count_mean: average of favorites on the terms on the day of publicationterms_ini_followers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the terms on the day of publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms on the day of publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who spoke about the terms on the day of publicationterms_ini_user_age_mean: average age in days of users who have spoken of the terms on the day of publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms on the day of publicationterms_end_num_tweets: total tweets on terms 14 days after publicationterms_ini_retweet_count_total: total retweets on terms 14 days after publicationterms_ini_retweet_count_mean: average retweets on terms 14 days after publicationterms_ini_favorite_count_total: total bookmarks on terms 14 days after publicationterms_ini_favorite_count_mean: average of favorites on terms 14 days after publicationterms_ini_followers_talking_rate: ratio of media Twitter account followers who have recently posted a tweet talking about the terms 14 days after publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms 14 days after publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who have spoken about the terms 14 days after publicationterms_ini_user_age_mean: the average age in days of users who have spoken of the terms 14 days after publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms 14 days after publication.tesis_terms: data of the terms (tags) related to the processed articles.stats_id: Analysis IDtime: "0" if at the time of publication, "1" if 14 days laterterm_id: Term ID (tag) in WordPressname: Name of the termslug: URL of the termnum_tweets: number of tweetsretweet_count_total: total retweetsretweet_count_mean: average retweetsfavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesfollowers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the termuser_num_followers_mean: average followers of users who were talking about the termuser_num_tweets_mean: average number of tweets published by users who were talking about the termuser_age_mean: average age in days of users who were talking about the termurl_inclusion_rate: URL inclusion ratio

As of March 2025, 27 percent of female adults in the United States spent one to five hours per week playing video games. A further 20 percent of female respondents stated that they spent six to ten hours on video gaming in an average week. In contrast, 12 percent of male respondents indicated that they spent 11 to 15 hours per week on video games. Overall, 17 percent of responding women were non-gamers and did not spend any time on video games but only 14 percent male adults in the United States were a non-gamer.

The game has attracted a diverse group of players ranging from young children all the way through to older adults. Here’s the full breakdown of Minecraft user statistics.

When asked about "Hours spent on playing video games per week", ** percent of UK respondents answer "1 to * hours". This online survey was conducted in 2025, among ***** consumers. Looking to gain valuable insights about video gamers worldwide? Check out our reports on customers of video game stores and subscription services. These reports give readers a thorough picture of the people who use these platforms, including their identities, preferences, opinions, and methods of communication.

Minecraft has recorded worldwide sales of over 300 million units since its release.

In 2023, men in the United States spent approximately 42 minutes per day on games and using a computer for leisure. General video gaming use among the U.S. population increased significantly during the COVID-19 pandemic. Between May and December 2020, U.S. men spent an average 49.2 daily minutes on playing games and using computers for leisure, up from 36 minutes per day in the corresponding period of 2019. Women spent an average 26.4 minutes per day on gaming and leisure computer use.

Minecraft is a truly global game with players from all over the world. But the United States has the most players with 21.21% of daily usage recorded coming from the US alone.

These are the key Minecraft user statistics you need to know.

2014 was the worst year in terms of revenue. It dropped to $165M in global annual revenue just as Microsoft purchased the game. But in 2021, Minecraft generated $380M.

These Minecraft user statistics show just how big Minecraft has become and why it’s still so popular today.

Between February 2023 and 2024, mobile gamers aged 50 or above spent an average of 20.76 minutes playing on gaming apps daily. By comparison, users aged between 40 and 49 years spent 21.96 minutes on mobile gaming daily. In the examined period, users aged 19 years old and younger represented the age group spending the least amount of time on mobile games daily.

In 2024, Minecraft generated $98.02 million in mobile revenue. Put simply – Mobile is an essential channel for Minecraft. Here is the full breakdown of global mobile revenue generated by Minecraft from 2015.