This is a dataset of mobile video games that have generated at least $100 million in gross revenue. Among them, there are more than 30 mobile games that have grossed more than $1 billion. The video game company with the highest number of titles on the list is Tencent, which publishes and/or owns 12 games on the list, including three in the top ten.

Tabular data includes:

• Game
• Revenue
• Initial release
• Publisher(s)
• Genre(s)

This dataset provides detailed, session-level metrics for mobile games, including user identifiers, session timing, device and OS information, in-app purchases, and gameplay events. It is ideal for retention modeling, player segmentation, and optimizing game design for studios and publishers seeking actionable insights on user engagement and monetization.

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https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3936637%2Fa8cf639255debf504eafc36c4409f425%2Fpubg_PNG37.png?generation=1600176293699043&alt=media" alt="">

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!

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.

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.

This dataset contains demographic information and mobile gaming behavior of Indonesian Gen-Z. It includes responses from mostly Gen-Z participants with varying income and education levels. Additionally, the data captures frequently played games, gaming experience, and top-up preferences. The author creates a questionnaire and posts it online for the population and sample that have been predetermined. In this study, the questionnaire approach was utilized for data collection. The questionnaires of this study were distributed via Google forms with a distribution period of 3 months.

BackgroundThis study aimed to explore the adverse influences of mobile phone usage on pilots’ status, so as to improve flight safety.MethodsA questionnaire was designed, and a cluster random sampling method was adopted. Pilots of Shandong Airlines were investigated on the use of mobile phones. The data was analyzed by frequency statistics, linear regression and other statistical methods.ResultsA total of 340 questionnaires were distributed and 317 were returned, 315 of which were valid. The results showed that 239 pilots (75.87%) used mobile phones as the main means of entertainment in their leisure time. There was a significant negative correlation between age of pilots and playing mobile games (p

Here are a few use cases for this project:

1. Educational Gaming: The "Car Detection Game" model can be used in mobile or web-based educational games. In the game, users are shown images and have to correctly identify the class of car for points. This can serve as a fun way to educate users about car types and their distinguishing features.

2. Specialized Training: People in professions that require car knowledge such as automobile engineers, car mechanics, or car salesmen, could use this game for specialized training to improve their recognition and understanding of various car classes.

3. Data Collection for Manufacturing Companies: Car manufacturing companies can use this model in an interactive game context to gather data about user familiarity with various car classes. This can inform design and marketing decisions.

4. Children's Interactive Learning Platforms: The game can be integrated into children's learning platforms to introduce them to car types in an engaging way.

5. Driver's Education Programs: The "Car Detection Game" can be used as part of driver's education curriculum, helping new drivers familiarize themselves with different types of vehicles they may encounter on the road.

The following is a short explanation of each of the columns of the table and its contents, self-explanatory titles are omitted. Database: source or publisher of the paper Type: description of the kind of publication between conference paper, book chapter, or journal paper. Oldest Reference: year of publication of the oldest reference cited in the paper. Newest Reference: year of publication of the newest reference cited in the paper. Citation: Classification of the number of citations that the paper has. Values: none, 1-5, 5-10, +10. Participants: Classification of the number of participants that the paper reports. Values: none, 1-5, 5-10, +10. Method Description: Short description of the method reported by the paper. Reported Method: Classification of the reported method of the paper in three main categories. Design, Experiment / Test, Literature review, Survey. Gamification - Motivation: Classification of the sources of motivation reported. Values: Intrinsic Motivation, Extrinsic Motivation, Mixed. Gamification - Negative Issues: Filled when the paper is reporting the analysis of negative consequences of using gamification: Gamification: Classification of the kind of technique reported. Values: Gamified, not Gamified Device and Location Usage: Classification of the use of mobile devices, wearables and location technologies. Values: No device, Device enabled, Mobile and location enabled, Device and location enabled. App Name: The name of the application reported by the paper when it exists.

The data set contains performance data (mainly frame times) for rendering spherical glyphs and scalar fields on Xbox Series consoles, mobile game consoles and a reference PC with different GPUs.

Level Up Your Speaking Skills: Unleashing the Power of Duolingo's Mobile Game-Based Language Learning Data Set

The data comprises of in-depth interviews with two groups. The first is 20 parents and carers of children and young people who spend money in digital games and have purchased loot boxes (or similar). These interviews explored how parents view their child’s gaming and in-game purchases, how they understand paid reward systems in digital games, and what would help them navigate these systems with their children. The second group are 10 game designers who have experience of designing and developing digital games that contain paid reward systems. The focus here was to investigate how designers make decisions and how they understand the effects paid reward systems have on players. The aim of this data collection was to provide in- depth qualitative evidence of how children and young people engage with, understand, and experience paid reward systems in digital games (across console, mobile, and PC). Commonly called loot boxes, card packs, or spins, these digital items give randomised rewards of uncertain value in exchange for in-game currency purchased with real world money. Their success is largely predicated upon the use of techniques borrowed from regulated gambling to engage players and encourage repeated use of these mechanisms. The motivation for the study was therefore to collect data to investigate the link between paid reward systems in digital games and their relationship to techniques drawn from regulated gambling. These interviews were supplements to video ethnography with 42 families in the North East of England that were conducted in the family home to understand children and young people's practices and activities involving paid reward systems. These files are not uploaded to ReShare due to ethical considerations of recorded footage of children and young people in homes, as per our institutional ethical approval.Gambling style systems in digital games, such as loot boxes, cards, micro-transactions and forms of currency used to purchase game specific content have become widely adopted in a range of digital games. These models of revenue generation can take many forms, from free to play smart phone games that encourage the purchase of additional digital content, to full price videogame console releases that utilise chance based cards or 'loot' paid for with real currency. These systems are highly profitable, with publishers such as Activision earning over $4 billion from this aspect of their games in 2017 alone (Makuch 2018). But, their success is predicated upon the use of techniques and mechanics borrowed from machine gambling to encourage repeated use of these systems. While gambling is a highly regulated activity in the UK that is restricted to adults over the age of 18, many of these games are actively marketed and sold to children and young people under 18. This is problematic and the Gambling Commission (2017) has recently pointed out that 25,000 children between 11 and 16 are problem gamblers, 'with many introduced to betting via computer games and social media'. These systems thus raise important questions about their design and regulation, especially if they act as a gateway to other forms of gambling such as online casinos or fixed odds betting terminals. Despite the widespread nature of gambling style systems in digital games, no academic work has explicitly: 1. Investigated how children and young people use these systems in their everyday lives and whether they create any problems or issues for these groups. 2. Investigated how parents and guardians understand and regulate their children's use of these systems. To investigate these issues and fill this gap in knowledge the project researches three groups. 1. Digital reward system designers. Through interviews with 10 digital interface designers the project will identify the key mechanics and systems utilised in the games they have worked on and the aims of this design. 2. Children and young people who use gambling style systems in digital games. Through 100 hours of video ethnography across 40 families (equalling approximately 2.5 hours of footage per family), the project will investigate how children and young people use gambling style systems in digital games. In addition, 20 semi-structured interviews with children and young people will be conducted to understand how they use gambling style systems outside of the home, for example on mobile devices. 3. Parents of children and young people who use these systems. 20 interviews with parents will investigate how they understand these systems and whether they regulate their use of these systems and what form this regulation might take. Through research with these groups, the project develops a theoretical model of gambling style systems in digital games that investigates whether the success of their underlying mechanics is fundamentally linked to the space-times where they are used. It then examines how children and young people use these systems in practice and how they make sense of them. Utilising this body of evidence, the study will then offer recommendations as to whether these systems should be regulated and what form this regulation could take. The data comprises of qualitative semi-structured interviews with two groups. The first is parents and guardians of children and young people in the North East of England who have used loot boxes and bought in-game content in digital games and apps. Discussions focus on how and when children and young people spend money, and how parents and guardians understand and manage spending. The second group is games designers who create loot boxes and in-game spending systems in a range of games and apps. Here, discussion focuses on the techniques of design in relation to encouraging children and young people to spend money and how effective these techniques are. Sampling procedures involved snowball sampling.

Here are a few use cases for this project:

1. Sports Analytics: The model could be used to analyze team strategies and players' performance in basketball games, by identifying times when various players possess the basketball.

2. Activity Tracking: Players, coaches, and fitness professionals could use this model to track training sessions, observing how often individuals possess the ball, trajectory of the ball, and interactions between players.

3. Video Production: Sports broadcasters could use the model during live coverage or replay analysis. It can automatically highlight the person with the ball, making it easier for audience to follow the game.

4. Surveillance and Security: The model can monitor public basketball courts to ensure users' safety or to track usage statistics for park and recreation planning.

5. Interactive Games and Apps: The model could be used to design mobile apps or video games that allow users to virtually 'play' basketball, identifying when the user or another player has the 'ball'.

Here are a few use cases for this project:

1. Assistive Technology for Visually Impaired: The MDP model can be used in the development of assistive technologies or applications for visually impaired individuals. This can include real-time identification and audio description of playing cards or other labeled objects to improve their daily living or recreational activities.

2. Security Systems: This model can be used in security systems to identify access cards. Different classes could represent various access levels, triggering appropriate actions (e.g., opening doors, logging entry and exit times).

3. Educational Games: The model can be implemented in card-based educational games where a computer or mobile device's camera can accurately recognize card symbols, ensuring a fair and improve interactive gameplay experience.

4. Automated Card Dealer for Casino Games: It can be used to create an automated card dealing machine in casinos or other gambling establishments. The machine could identify the type of card being dealt, ensuring fair play and preventing human error.

5. Augmented Reality Applications: MDP model could be integrated into AR applications which provide user interaction with the real world via card identifications. For example, each identified card can trigger a different AR experience according to its class.

Smoking is a major cause of worldwide morbidity and mortality. Almost no evidence-based intervention programs are available to help youth quit smoking. We argue that ineffective targeting of peer influence and engagement difficulties are significant barriers to successful youth smoking cessation. To address these barriers, we developed the mobile game intervention HitnRun. A two-armed randomized controlled trial (RCT; n = 144) was conducted and young smokers (Mage = 19.39; SDage = 2.52) were randomly assigned to either play HitnRun or read a psychoeducational brochure. Prior to, directly following the intervention period, and after three-month follow-up, weekly smoking behavior, abstinence rates, intervention dose, and peer- and engagement-related factors were assessed. Results indicated similar reductions in weekly smoking levels and similar abstinence rates for both groups. Yet, we found a dose effect with HitnRun only: The longer participants played HitnRun, the lower their weekly smoking levels were. In the brochure group, a higher dose was related to higher weekly smoking levels at all measurement moments. Exploratory analyses showed the most powerful effects of HitnRun for participants who connected with and were engaged by the intervention. Future work should build on the promising potential of HitnRun by increasing personalization efforts and strengthening peer influence components.

This dataset was created by Harry Field and contains the labelled images for capturing the game state of a draughts/checkers 8x8 board.

This was a fun project to develop a mobile draughts applciation enabling users to interact with draughts-based software via their mobile device's camera.

The data captured consists of: * White Pieces * White Kings * Black Pieces * Black Kings * Bottom left corner square * Top left corner square * Top right corner square * Bottom right corner square

Corner squares are captured so the board locations of the detected pieces can be estimated.

https://github.com/ShippingTycoon/roboflow-draughts/blob/main/PXL_20210603_093949805_jpg.rf.30e2a64a0a646e8ea8e121727cf0f1ee.jpg?raw=true" alt="Results of Yolov5 model after training with this dataset">

From this data, the locations of other squares can be estimated and game state can be captured. The image below shows the data of a different board configuration being captured. Blue circles refer to squares, numbers refer to square index and the coloured circles refer to pieces. https://github.com/ShippingTycoon/roboflow-draughts/blob/main/pieces.png?raw=true" alt="">

Once game state is captured, integration with other software becomes possible. In this example, I created a simple move suggestion mobile applciation seen working here.

The developed application is a proof of concept and is not available to the public. Further development is required in training the model accross multiple draughts boards and implementing features to add vlaue to the physical draughts game.

The dataset consists of 759 images and was trained using Yolov5 with a 70/20/10 split.

The output of Yolov5 was parsed and filtered to correct for duplicated/overlapping detections before game state could be determined.

I hope you find this dataset useful and if you have any questions feel free to drop me a message on LinkedIn as per the link above.

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

Nowadays, events are spread rapidly along social networks. We are interested in whether people’s responses to an event are affected by their friends’ characteristics. For example, how soon will a person start playing a game given that his/her friends like it? Studying social network dependence is an emerging research area. In this work, we propose a novel latent spatial autocorrelation Cox model to study social network dependence with time-to-event data. The proposed model introduces a latent indicator to characterize whether a person’s survival time might be affected by his or her friends’ features. We first propose a score-type test for detecting the existence of social network dependence. If it exists, we further develop an EM-type algorithm to estimate the model parameters. The performance of the proposed test and estimators are illustrated by simulation studies and an application to a time-to-event dataset about playing a popular mobile game from one of the largest online social network platforms. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Overview

PokemonGo is a mobile augmented reality game developed by Niantic inc. for iOS, Android, and Apple Watch devices. It was initially released in selected countries in July 2016. In the game, players use a mobile device's GPS capability to locate, capture, battle, and train virtual creatures, called Pokémon, who appear on the screen as if they were in the same real-world location as the player.

Dataset

Dataset consists of roughly 293,000 pokemon sightings (historical appearances of Pokemon), having coordinates, time, weather, population density, distance to pokestops/ gyms etc. as features. The target is to train a machine learning algorithm so that it can predict where pokemon appear in future. So, can you predict'em all?)

Feature description

• pokemonId - the identifier of a pokemon, should be deleted to not affect predictions. (numeric; ranges between 1 and 151)
• latitude, longitude - coordinates of a sighting (numeric)
• appearedLocalTime - exact time of a sighting in format yyyy-mm-dd'T'hh-mm-ss.ms'Z' (nominal)
• cellId 90-5850m - geographic position projected on a S2 Cell, with cell sizes ranging from 90 to 5850m (numeric)
• appearedTimeOfDay - time of the day of a sighting (night, evening, afternoon, morning)
• appearedHour/appearedMinute - local hour/minute of a sighting (numeric)
• appearedDayOfWeek - week day of a sighting (Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday)
• appearedDay/appearedMonth/appearedYear - day/month/year of a sighting (numeric)
• terrainType - terrain where pokemon appeared described with help of GLCF Modis Land Cover (numeric)
• closeToWater - did pokemon appear close (100m or less) to water (Boolean, same source as above)
• city - the city of a sighting (nominal)
• continent (not always parsed right) - the continent of a sighting (nominal)
• weather - weather type during a sighting (Foggy Clear, PartlyCloudy, MostlyCloudy, Overcast, Rain, BreezyandOvercast, LightRain, Drizzle, BreezyandPartlyCloudy, HeavyRain, BreezyandMostlyCloudy, Breezy, Windy, WindyandFoggy, Humid, Dry, WindyandPartlyCloudy, DryandMostlyCloudy, DryandPartlyCloudy, DrizzleandBreezy, LightRainandBreezy, HumidandPartlyCloudy, HumidandOvercast, RainandWindy) // Source for all weather features
• temperature - temperature in celsius at the location of a sighting (numeric)
• windSpeed - speed of the wind in km/h at the location of a sighting (numeric)
• windBearing - wind direction (numeric)
• pressure - atmospheric pressure in bar at the location of a sighting (numeric)
• weatherIcon - a compact representation of the weather at the location of a sighting (fog, clear-night, partly-cloudy-night, partly-cloudy-day, cloudy, clear-day, rain, wind)
• sunriseMinutesMidnight-sunsetMinutesBefore - time of appearance relatively to sunrise/sunset Source
• population density - what is the population density per square km of a sighting (numeric, Source)
• urban-rural - how urban is location where pokemon appeared (Boolean, built on Population density, <200 for rural, >=200 and <400 for midUrban, >=400 and <800 for subUrban, >800 for urban)
• gymDistanceKm, pokestopDistanceKm - how far is the nearest gym/pokestop in km from a sighting? (numeric, extracted from this dataset)
• gymIn100m-pokestopIn5000m - is there a gym/pokestop in 100/200/etc meters? (Boolean)
• cooc 1-cooc 151 - co-occurrence with any other pokemon (pokemon ids range between 1 and 151) within 100m distance and within the last 24 hours (Boolean)
• class - says which pokemonId it is, to be predicted. Data dump ------------

All pokemon sightings (in JSON file, without features) can be found in Discussion "Datadump"

End-Period-Cash-Flow Time Series for Taiwan Mobile Co Ltd. Taiwan Mobile Co., Ltd. provides wireless communication services in Taiwan, the Republic of China, and internationally. The company operates through four segments: Telecommunications, Retail, Cable Television and Broadband, and Others. It offers mobile communication, mobile phones, and fixed-line services; e-commerce; wireless and fixed-line telecom services; digital TV subscriptions; cable broadband; TV home shopping; and integrated information and communication services. The company also provides pay TV and cable broadband services; travel, property insurance, and life insurance agency services; value-added services; information software services; and TV program provision. In addition, it is involved in building and operating BOT projects, call center services, telephone marketing activities, commissioned maintenance, film production, investment and retail, and the sale of mobile phones, accessories, and games. Further, the company offers data communication application development, cloud and information services, cable TV, wholesale and retail storefront sales, and gaming services. It also engages in branding agency, cosmetics, logistics, and transport businesses, as well as trading, industrial and commercial services, general investment, entrusted maintenance, cloud power palace service, and communication industries. It provides its products and services under the Taiwan Mobile, TWM Broadband, Taiwan Mobile Enterprise Services, and momo brands. Taiwan Mobile Co., Ltd. was incorporated in 1997 and is based in Taipei, Taiwan.