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.

The Arena of Valor game dataset contains information on individual player performance during matches of the popular mobile multiplayer online battle arena (MOBA) game. The dataset includes details on player IDs, team IDs, chosen heroes, positions played, game stats (such as level, gold, KDA, damage dealt, damage taken, and time played), and match IDs.

Column Details

• Match ID: The unique identifier for each Arena of Valor match.

• Player ID: A unique identifier for each player participating in a match.

• Team ID: A unique identifier for each team in a match.

• Hero: The hero chosen by the player for the match.

• Position: The position played by the player in the match (such as top, mid, jungle, or bottom).

• Level: The level of the player's hero at the end of the match.

• Gold: The amount of gold earned by the player during the match.

• KDA: A measure of the player's performance, including kills, deaths, and assists.

• Damage Dealt: The amount of damage dealt by the player to enemy players during the match.

• Damage Taken: The amount of damage taken by the player from enemy players during the match.

• Time Played: The amount of time played by the player in the match.

You could use this dataset to analyze how different heroes perform in different positions, which players are the most effective in each position, which teams are the most successful, and many other factors related to Arena of Valor gameplay

Note: The dataset is an example and may not accurately represent the actual data structure of an Arena of Valor game dataset.

** The purpose of creating this dataset is solely for educational use, and any commercial use is strictly prohibited and this dataset was large language models generated and not collected from actual data sources.

cover image: https://www.4gamers.co.th/news/detail/236/rov-battlefield

In 2024, casual mobile game creatives accounted for 30.6 percent of all mobile game creatives on digital platforms worldwide. Puzzles ranked second, with 12.2 percent.

This dataset is a collection of data that explains: 1.MPL ID Season 11 2.M4 World Championship 3.MPL PH Season 11 in the game Mobile Legends: Bang Bang. The dataset includes information related to the game update to version 1.7.68 released on March 22, 2023, along with the latest updates that have been made. Here are the details of the information contained in this dataset:

1.**'Minsitthar' Hero Update**: The dataset includes information about changes or updates made to the hero named 'Minsitthar'. This may include changes to attributes, skills, or other gameplay mechanics related to the hero.

2.**Latest Event 'ALLSTAR'**: The dataset provides details about the latest event called 'ALLSTAR'. This may include information about rules, prizes, event dates, and anything else related to the event.

3.**Latest Map 'Harmonia'**: Information about the new map introduced in the game is included in the dataset. This new map is called 'Harmonia', and the dataset may contain descriptions of the map structure, applicable gameplay strategies, or other changes that have occurred to the map.

4.**Equipment Equalization**: The dataset provides information about adjustments or equalizations of equipment in the game. This may include changes to equipment attributes, prices, or usage mechanisms that affect gameplay.

This dataset is useful for Mobile Legends: Bang Bang players who are interested in learning about the evolution of the game alongside the addition of new content and changes made in game updates. By providing detailed information about these changes, the dataset can be used for gameplay analysis, research, or other purposes related to the game.

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.

Representative applications that can directly collect 5G da-tasets from mobile terminals without using specialized equipment include G-NetTrack Pro and PCAPdroid. The for-mer allows for the monitoring and logging of the header and payload information of the medium access control (MAC) frame passing through the 5G air interface. The latter is an open-source network capture and monitoring tool that works without root privileges, analyzing connections made by ap-plications installed on the user's mobile device. The latter can also dump mobile traffic to PCAP (also known as libpcap) and send it to the well-known Wireshark for further analysis. We created 5G datasets by measuring 5G traffic directly from a major mobile operator in South Korea. The model name of the mobile terminal used for traffic measurement is the Samsung Galaxy A90 5G, and it was equipped with a Qualcomm Snapdragon X50 5G modem. The packet sniffer software used for traffic measurement, PCAPdroid, was in-stalled in the terminal through Google play. Traffic was measured sequentially per application on two stationary ter-minals (only one terminal was used for non-interactive ser-vices) with no background traffic. The collected dataset is representative resource-intensive video traffic that has the greatest impact on 5G network planning and provisioning, and background traffic was not mixed to measure the unique characteristics of each type of traffic. The video streaming dataset includes data directly meas-ured while watching Netflix and Amazon Prime, which are representative over-the-top (OTT) services, on mobile devic-es. The live streaming dataset was measured while watching YouTube Live and South Korea's representative live broad-casts (Naver NOW and Afreeca TV). Video conferencing data were measured by holding an actual meeting on the widely used Zoom, MS Teams, and Google Meet platform. Two types of metaverse traffic were acquired: Zepeto and Roblox. Zepeto traffic was collected while staying in the 'camping world' for 15 hours. Roblox traffic was collected over 25 hours of playing the 'Collect All Pets' game using an auto clicker. We collected two types of mobile network gaming traffic. The first was cloud gaming, an online game setup that runs video games on remote servers and streams them direct-ly to the user's device. The second was a traditional mobile game connected to the Internet. The dataset was collected from May to October 2022, is a massive 328 hours in total, and is provided in the csv file format. The dataset we collected is a timestamp-mapped time series dataset with packet header information, and traffic analysis by application is possible because it includes source and destination addresses. To make it more usable as a traffic source model, Section III describes how to use it as a training dataset for the traffic simulator platform's source generator.

A 5G traffic dataset measured by PCAPdroid has been re-leased and can be used as a training dataset for various ML models. However, since the size of this dataset is very large, it is inconvenient to handle, and additional data preprocessing is required to use it for its intended purpose.

This data set can be used to learn GANs, time-series forcasting deep learning models.

Our implementation is given on GitHub. https://github.com/0913ktg/5G-Traffic-Generator

In 2024, casual mobile game advertisers accounted for 27.7 percent of all mobile game advertisers on digital platforms worldwide. Casino games ranked second, with 21 percent.

Children’s mobile gaming time and SDQ by gaming region and urban/rural areas.

Selling virtual goods and currencies to consumers has grown into a major revenue model in digital games and online services. The Virtual Goods and Currencies Data Set is a freely available data set that describes the prices and other attributes of 11,289 virtual goods and currencies. The data set is drawn from 59 game titles on mobile, social media, and PC platforms.

The Gaming Taxonomy contains a broad scope of Gaming related topics, based on the user's browser and mobile app activity through last 30 days. There are classical Demographic, Game Genre, Title and Studio segments. However, we provide also plenty of specific User Types, which contain e.g. Hardcore Gamers, Big Spenders or Parents of Gamers. There are also audiences categorized by specific Hardware Products and Brands, based on the Intent of these devices' purchase. Moreover, we offer segments for Virtual Reality, interest in Gaming Subscriptions, Payments, Micropayments, Devices and Platforms. We also cover the area of E-sports Enthusiasts and Fandoms Members. In spirit of looking beyond simple game genres, we categorize Games according to their Theme (e.g. Historical), which is definitely important aspects of user experience and purchase decisions. Since Mobile Gaming is a very important part of the Gaming Industry, we distinct special Mobile Gaming segments, which are analogous to the ordinary Gaming segments, with additional categorizations of the Telecommunication Network Providers.

Our data base include millions of profiles divided into popular categories. You can choose which target groups you want to reach. Segments based on users' interests, purchase intentions or demography. Contact us to check all the possibilities: team@oan.pl

How you can use our data?

There are two main areas where you can use our data: • marketers - targeting online campaigns With our high-quality audience data, you can easily reach specific audiences across the world in programmatic campaigns. Show them personalized ads adjusted to their specific profiles. • ad tech companies - enriching 1st party data or using our raw data by your own data science team

We are ready for a cookieless era. We already gather and provide non-cookie ID - for example Universal IDs, CTV IDs or Mobile IDs.

Genshin Impact is a free-to-play cross-platform action RPG. On mobile, Genshin Impact is particularly popular in Japan, where players generated an average revenue per download of 102.53 U.S. dollars. Singapore ranked second, with a cumulative RPD of 64.86 U.S. dollars. Genshin Impact – a free-to-play success story With an annual IAP revenue of 1.56 billion U.S. dollars, Genshin Impact is one of the highest-grossing mobile games of 2023. The game was released in September 2020 and initial reviews were mixed, describing the setting and theme as a clone of Nintendo’s The Legend of Zelda: Breath of the Wild. However, this stance was quickly abandoned by critics as Genshin Impact came into its own. The game features regular content releases to keep the player engaged as the story continues to evolve and new characters and regions are revealed. In August 2024, Genshin Impact launched the content update 5.0 which featured the new Natlan region and several new characters, quests, and in-game events. Genshin Impact is a live service game, also known as the Games-as-a-Service (GaaS), which is one of the strongest-growing revenue models in gaming right now. Gacha monetization mechanics In addition to the live service model, Genshin Impact is also a successful example of the gacha monetization trend in mobile games. Similar to loot boxes, a gacha system leads the player to spend in-game currency to receive random in-game items. In Genshin Impact, the gacha system is implemented via several so-called banners featuring characters or weapons which only are available for a limited amount of time. Spending in-game currency on these banners is the main way of unlocking new characters in the game. Players can earn the necessary currency either via completing game quests or through purchases in the virtual item shop with real money.In October 2024, Genshin Impact’s monthly mobile app revenue amounted to 35 million U.S. dollars, with China, Japan, and the United States leading in terms of revenue.

Children’s mobile gaming time and SDQ by gaming preferences.

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

In the fiscal year 2025 (ending March 2025), Sega Sammy sold a total of 31.45 million video games, unchanged from the full game unit sales in the previous fiscal year. Major gaming IPs owned by Sega Sammy include the Sonic series, the Total War series, the Persona series and the Like a Dragon series, which includes the Judgement game spinoffs.

Association between different musculoskeletal pain regions among the mobile game addicts and non-addicts.

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.

OAN helps you reach gamers across the world. Our gaming audience data offers categorized audience segments into gamer behavior and gaming trends. This powerful dataset provides a deep understanding of the gaming industry by delivering unique categories such as: demography, interest, hardware, spenders, genres and titles, e-sports fans and players.

By understanding this data, businesses can make data-driven decisions to optimize their marketing strategies, game development, and monetization efforts.

The Gaming Taxonomy contains a broad scope of Gaming related topics, based on the user's browser and mobile app activity through the last 30 days. There are also gamer audiences categorized by specific Hardware Products and Brands, based on the Intent of these devices' purchase. Furthermore, we offer segments for: - Virtual Reality - Interest in Gaming Subscriptions - Payments - Micropayments - Devices and Platforms.

We also cover the area of E-sports Enthusiasts and Fandoms Members. In spirit of looking beyond simple game genres, we categorize Games according to their Themes (e.g. Historical), which are definitely important aspects of user experience and purchase decisions. Since Mobile Gaming is a very important part of the Gaming Industry, we distinct special Mobile Gaming segments, which are analogous to the ordinary Gaming segments, with additional categorizations of the Telecommunication Network Providers.

Gaming audience data is just a part of all audience data we provide. We deliver millions of users’ profiles gathered globally and grouped into IAB-compliant segments. You can choose which target groups you want to reach. Contact us to check all the possibilities: team@oan.pl

How you can use our data?

There are two main areas where you can use our data: - Marketers - targeting online campaigns With our high-quality audience data, you can easily reach specific audiences across the world in programmatic campaigns. Show them personalized ads adjusted to their specific profiles. - Ad tech companies Enriching 1st party data or using our raw data by your own data science team.

In 2030, the revenue growth is forecast to significantly decrease in all segments compared to the previous time point. In this context, the notably strong decrease of the segment Cloud Gaming towards the end of the forecast period stands out. In comparison to the average decrease of 0.4344 percent, the revenue change is decreasing significantly here, with a value of 2 percent. Find further statistics on other topics such as a comparison of the revenue in India and a comparison of the revenue in the United Kingdom.The Statista Market Insights cover a broad range of additional markets.

In fiscal year 2024, Capcom sold approximately 45.89 million games, 41.35 million of which were digital game downloads. The company's steady increase of digital game software download sales is in line with the general pivot of gaming sales towards digital store solutions. The outbreak of the COVID-19 pandemic has accelerated this process.