This dataset contains information about the average & peak monthly number of players per 6725 unique games placed on Steam. This dataset was inspired by another similar one, but it was not updated for the last 5 years, so I decided to make a bit more fresh one: https://www.kaggle.com/datasets/michau96/popularity-of-games-on-steam

The data was received by web scraping from https://steamcharts.com website by usage the basic script pandas read_html command to be launched on Kaggle side. The list of steam id to be scrapped was grabbed from Steam All Games Data dataset: https://www.kaggle.com/datasets/fmpugliese/steam-all-games-data.

The reason there are only 6725 unique name values is related to limitations of Steam Charts portal: when the game has minimal audience volume, the page for that game is not rendered in almost all cases. Also, some major games also could not be presented due to limitations of the selected scraping method.

A structure of the dataset: - Month - month-year of observation - avg_players - average players count (float) - gain - difference comparing to previous month (float) - gain_percent - difference comparing to previous month in percents (float) - peak_players - highest value of players at the same time for selected month (float) - name - name of the game (string) - steam_appid - steam ID of the game (string)

Please feel free to combine this dataset with any other video games-related sources on Kaggle.

This dataset was inspired by another Kaggle dataset: Popular Video Games 1980 - 2023 🎮, acting as a comprehensive collection of information about some of the most popular video games released. It serves as a valuable resource for researchers, gamers, and enthusiasts interested in exploring the evolution of the gaming industry over the past decades.

This dataset provides a wealth of information about each game, including its title, release date, genre, platform(s), summary, among other data. With this dataset, users can analyze trends, identify patterns, and gain insights into the popularity and commercial success of video games across different platforms and genres.

The difference between this dataset and its inspiration is that the amount of data is greater (60k rows), in addition to having the new column "Platforms", indicating which platforms the game was released on. Also, the "Reviews" column (with the texts of user reviews) has been removed, keeping only the column with the total number of reviews that the game has.

📅 - The data was collected in mid-June 2023, so some values may be slightly different today.

Discovering Hidden Trends in Global Video Games Sales

Platforms, Genres, and Profitable Regions

By Andy Bramwell [source]

About this dataset

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

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How to use the dataset

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

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

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

Research Ideas

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

Acknowledgements

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

License

See the dataset description for more information.

Columns

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

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.

Global Video Game Sales and Reviews

Global Video Game Performance: Sales, Reviews, and Rankings

By Andy Bramwell [source]

About this dataset

The elements covered in this well-curated dataset include: The ranking of the game based on global sales under the column 'Rank'. This metric provides perspective on how popular or successful a particular game has been across countries in comparison to others during its time. Noting that video games' popularity could vary greatly from one geography to another due to factors like cultural nuances, gamer preferences, etc., regional sales have been marked separately for North America (North America), Europe (Europe), Japan (Japan) as well as for other parts of the World excluding these three regions under the column 'Rest of World'.

For easy identification among massive chunks of data, we've included each game's title (Game Title) along with additional categorization based on their genre (Genre). From action-packed adventures to strategic board-like scenarios or enchanted magic realms - classifications cover it all! In addition, detailed information about publishers can be found under 'Publisher', which grants insights about leading companies dominating market shares.

Further details expand into mentioning platforms such as PS4, Xbox, PC where these games can be played under 'Platform'. A unique attribute covered in this database is ‘Review’. Given that critique ratings play an influential role in engaging new players into trying out a particular video game or boosting existing user morale regarding their choice; this numeric representation ranging typically from 1-10 vividly captures public opinion about them.

Lastly, just for keeping tabs on ever-evolving gaming technology standards where newer versions often outshine predecessors irrespective of actual gameplay quality itself; having release years mentioned ('Year') proves beneficial for categorizing them chronologically. This helps correlate whether higher sales figures can sometimes merely be indicative of more people having access to necessary high-end gaming hardware during later periods.

In essence, this dataset titled ‘Video Games Sales.csv’ holds immense potential for informative deep-dives into the Video Game industry's trends and paradigms, forming a solid foundation for market research, academic purposes or personal projects

How to use the dataset

This dataset provides extensive information about various video game titles, their sales performance across multiple regions, publisher details and game reviews. Follow the steps outlined below to make the most out of this remarkable dataset!

1. Game Research & Evaluation:

With columns such as 'Game Title', 'Genre' and 'Review', you can research on particular games or genres that interest you. You can evaluate a game based on its review scores, delving into what makes a top-rated game.

2. Publisher Analysis:

The 'Publisher' column lets you track which publishers are behind the most successful games in terms of sales and reviews. This analysis could be useful for people interested in business trends in gaming industry or trying to identify potential innovative publishers.

3. Regional Market Trend Identification:

You can use data from columns like ‘North America’, ‘Europe’, ‘Japan’ and ‘Rest of World’ to study regional market trends for certain genres or platforms; it might enable one to recognize patterns over time or cultural preferences with regard to video games.

4. Global Sales Analysis:

Using the 'Global' column, you could observe which games have been globally successful, going beyond regional preferences by genre or platform.

5. Platform Insight:

The platform on which a particular game is available is another significant factor (e.g., PC, PS4, Xbox). By utilizing the data contained in this dataset regarding platforms, one may learn how platform choice impacts global sales as well as discern any correlation between preferred platform types among specific regions.

Remember that every statistical analysis begins with knowing your data - dive deep into each variable; explore patterns within variables before looking at correlations between different fields.

Don't forget - when engaged with comprehensive datasets like these - creativity is your only limit! Happy analyzing!

Research Ideas

• Trend Analysis: This dataset can be used to analyze the trends in video game preferences over the years based on genre, publisher, platform and region. It can provide interesting insights into how consumer tastes have evolved with time and which game genres are becoming more popular.
• Sales Forecasting: U...

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.

This dataset contains a list of video games with sales greater than 100,000 copies. It was generated by a scrape of vgchartz.com.

Fields include

• Rank - Ranking of overall sales

• Name - The games name

• Platform - Platform of the games release (i.e. PC,PS4, etc.)

• Year - Year of the game's release

• Genre - Genre of the game

• Publisher - Publisher of the game

• NA_Sales - Sales in North America (in millions)

• EU_Sales - Sales in Europe (in millions)

• JP_Sales - Sales in Japan (in millions)

• Other_Sales - Sales in the rest of the world (in millions)

• Global_Sales - Total worldwide sales.

The script to scrape the data is available at https://github.com/GregorUT/vgchartzScrape. It is based on BeautifulSoup using Python. There are 16,598 records. 2 records were dropped due to incomplete information.

Cross-sectional survey of individuals aged 16–25 residing in Spain (n = 1,000), fielded in June 2025. The dataset captures demographics, gaming practices, exposure to gamblified mechanics (e.g., loot boxes, randomized rewards, time-limited offers), spending bands, emotional responses, and perceptions among non-players.The survey questionnaire (available as related material) consists of three sections: 1) sociodemographic data; 2) gamers (n=927); and 3) non-gamers (n=73). To distinguish between gamers and non-gamers, a filter question was placed at the end of section 1 (“Do you regularly play video games, mobile games, or digital games?”).The first section collects information on: sex/gender, municipality of residence, province/community of residence according to Nielsen area, size of municipality of residence, level of education completed, and current employment status.Block two collects information on estimated weekly hours of play, the device usually used, estimated monthly money spent on video games, and the two most played games. It also includes five-point Likert scale questions (disagree/agree) on issues related to random mechanisms, payments, or gaming habits.The third section repeats the same pattern of five-point Likert scale questions (disagree/agree) but from the perspective of non-video game players.

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.

**To see detailed metadata see the PDF file on the right **

This dataset contains data on 64 videogames releases between 2012 and 2022. The games have been selected for being best-selling or top-rating games of their year. There are at least 5 games per year and information on the most relevant characters in the storyline.

The relationship between tables is as follows

Games.Game_ID = Characters.Game Characters.Id = Sexualization.Id

Version Update 10/11/22: - A misspelling on the games dataset column Release has been corrected - The abbreviations used to define the relevance of a character in Characters/Relevance have changed for clarity purposes · MC is now PA for protagonist · DA and SK stay the same · CR is now MC for main character · OR is now SC for secondary character · MV is now MA for main antagonist - A PDF file with detailed metadata has been added

This is the dataset for all of the chess enthusiasts and chess.com members. It has been created via the chess.com API. Sourced from: 60,000+ Chess Game Dataset

Here are a few use cases for this project:

1. Sports Training: This model could be used in sports training simulations to identify and track balls in different sports like baseball, football, or soccer. It can help to create efficient training models for players, evaluating their performance in targeting and control over the ball.

2. Gaming Applications: The "Target Game" model could be integrated in video game development where players interact with virtual balls. The model could add an improved level of realism by effectively identifying and responding to ball characteristics.

3. Security Monitoring: The model could be used in public or private spaces, like schoolyards or playgrounds, to monitor ball games and identify potential hazards or rule-breaking.

4. Robotics: In robotics, it can be used to train robots for tasks like sorting or transporting balls of different sizes, colors or materials, hence improving their efficiency and accuracy.

5. Physical Therapy: The model could be used in therapeutic scenarios where patients are asked to interact with balls for coordination or strength exercises. It can track patients' interaction and progression effectively.

Overview

This dataset is part of the study titled "Player Experience in Video Game Character Analysis: A Study of Female Characters", conducted at Mapúa University. The research aims to integrate player experience into an existing framework for video game character analysis.

Content

The dataset includes:

A partial transcript of 5 semi-structured interviews with the key informants. Originally, 8 interviews were conducted, but the audio/video recordings for 3 interviews were lost and thus their transcripts are not available.

Significant codes presented in tabulated form.

Data Collection Method

Data were collected through in-depth interviews conducted via Facebook Messenger and Discord from March to April 2024. Participants were various video game players from different backgrounds and age groups, ranging from 20 to 40 years old. Due to technical issues, the recordings of 3 interviews were lost, resulting in only 5 available transcripts.

Data Processing and Analysis

The 5 available interviews were transcribed verbatim. Data were analyzed using thematic analysis, involving initial coding, theme development, and refinement.

Usage data

The dataset is organized into several sections within a single Word document (.docx). This word document has headings for navigation and a definition of terms.

Limitations

The dataset only includes 5 out of 8 due to technical difficulties encountered after the recording of the interview. This may impact the comprehensiveness of the findings.

Contextual Reference

The manuscript associated with this dataset heavily references the works "A Structural Model for Player-Characters as Semiotic Constructs." (DOI: https://doi.org/10.26503/TODIGRA.V2I2.37) and "Object, me, symbiote, other: A social typology of player-avatar relationships." (DOI:https://doi.org/10.5210/FM.V20I2.5433) which explore the foundational frameworks on video game character analysis.

For any further information or clarifications, please contact wbdg2000@gmail.com

Global Video Game Sales and Ratings

Global Overview of Video Game Sales, User and Critic Ratings

By LearnToViz [source]

About this dataset

The Video Game Sales and Ratings Dataset is a comprehensive collection of data points concerning the video game industry. Predominantly prepared with the intent of facilitating data-driven decision making, this dataset serves as an essential tool for game developers, publishers, critics and researchers interested in examining trends and patterns within this burgeoning domain.

Consisting of a wide gamut of variables such as platform availability, genre classification, publishing entities involved, developers responsible for creating the games to understanding their global reach through sales figures across various regions including North America (NA_Sales), Europe (EU_Sales), Japan (JP_Sales) and even other diverse regions consolidated under 'Other_Sales'- this dataset offers an in-depth exploration into the intricacies inherent in video gaming business dynamics.

Furthermore, since it includes both critic reviews and user ratings along with associated count metrics that denote how many individuals voiced their opinion via these ratings; it not only lends performance transparency but also ensures broad inclusivity to make assessment more holistic. Therein lies its uniqueness: aptly capturing both market reception (sales diligence) along with perceptual quality review criteria led by expert critics and public users alike.

To facilitate a thorough understanding our array fields also cover critical detail including 'Year_of_Release', offering temporal insight into when these games arrived on market shelves around the world; how they have been categorized ('Genre'), which platforms host these games ('Platform'); who took responsibility for developing them ('Developer') & then getting them promoted or distributed to target consumer base ('Publisher').

Our 'User_Count' column offers further informed perspective about community engagement levels while inclusion of 'Rating' variable provides standards-based categorical info about age-specific content appropriateness as per internationally recognized rating agency- Entertainment Software Rating Board. With all these multifaceted components combined together in one robust dataset- rich analysis like sales forecasting, trend identification or patterns discernment among others can be most effectively accomplished.

By providing such a wealth of diverse and detailed information, this dataset opens up a world of possibilities for analysis and investigation that stretches the boundaries of what we can learn about video games- both as entertainment artifacts and as commercial entities. Overall this dataset stands to be an informative resource to better comprehend the complex dynamics shaping the globally-flourishing video game industry

How to use the dataset

This guide will provide tips on how to effectively navigate this dataset for both beginners in Data Science and researchers with more advanced skills.

• Getting Started: Familiarize yourself with the general contents of the data file by checking all column descriptions. This will give you an overarching understanding of what kind of data you can find in this dataset.

• Conceptual Understanding: Understand the context each column provides for every unique video game title:

• Platform: Can help highlight which gaming platforms have optimal sales.

• Year_of_Release: Can provide insights into gaming market trends or allow creation of timeline analysis.

• Genre: Useful for analyzing popular genres or identifying niche markets.

• Publisher / Developer: Monitoring these can reveal industry leaders or developers that consistently produce well-received games.

• NA_Sales / EU_Sales / JP_Sales / Other_Sales / Global_Sales : These columns are instrumental in performing regional market analyses, studying performance indicators globally, or comparing regional popularity differences versus global acceptance levels.

• User Scores Vs Critic Scores: These two columns might present divergent perspectives about a video-game's quality; critics might highly rate games users didn't favor & vice versa. Exploring these discrepancies could prove intriguing!

• Choosing Tasks:

  • Exploratory Data Analysis (EDA): This could be your first step if you're starting out learning how to handle datasets; identify missing values/nulls & their impact on data analysis; extract meaningful summary statistics.

  • Data Visualization: Generate charts, scatter plots, heat maps etc. to encapsulate sale...

https://i.imgur.com/PLS0HB3.gif" alt="Example Video from Deploy Tab">

Here are a few use cases for this project:

1. Sports Analytics: The Soccer Players computer vision model can be used to analyze player performance during games by tracking player and ball positions, individual player actions, and goal-scoring events, allowing coaches and trainers to make data-driven decisions for improving performance and strategies.

2. Automated Highlight Reels: The model can be used to automatically curate soccer match highlights by identifying crucial moments such as goals, outstanding player performances, and referee decisions. This can streamline the video editing process for broadcasting and streaming companies.

3. Virtual Assistant for Soccer Enthusiasts: The Soccer Players model can be integrated into a mobile application, allowing users to take pictures or upload images from soccer matches and receive instant information about the teams (USA, NED), player roles (goalie, outfield player, referee), and other relevant classes such as ball and goal locations, enhancing their understanding and engagement with the sport.

4. Real-Time Augmented Reality (AR) Applications: The model can be used to create AR experiences for soccer fans attending live matches, providing pop-up information about players (such as player stats, team affiliations, etc.) and game events (goals, referee decisions) when viewing the live match through an AR device or smartphone.

5. Training and Scouting Tools: Soccer scouts and trainers can use the Soccer Players model to evaluate potential recruits or assess the performance of their own players during practice sessions. By rapidly identifying key actions (goals, saves, tackles) and providing context for each play, the model can help scouts and trainers make informed decisions faster.

BackgroundThe realm of virtual games, video games, and e-sports has witnessed remarkable and substantial growth, captivating a diverse and global audience. However, some studies indicate that this surge is often linked to a desire to escape from real life, a phenomenon known as escapism. Much like substance abuse, escapism has been identified as a significant motivator, leading to adverse outcomes, including addiction. Therefore, it is crucial to comprehend the existing research on the connection between escapism and engagement in virtual gaming. This understanding can shed light on the reasons behind such practices and their potential impact on mental and public health.PurposeThe objective of this systematic review is investigate the findings pertaining to association between escapism and the practice of virtual games, such as video-games and e-sport.MethodsPUBMED and SCOPUS database were systematically searched. Six independent researchers screened articles for relevance. We extracted data regarding escapism-related measures, emotional/mental health-related measures and demographic information relevant to the review purpose.ResultsThe search yielded 357 articles, 36 were included. Results showed that: (i) Escapist motivation (EM) is one of the main motives for playing virtual games; (ii) EM is related to negative clinical traits; (iii) EM predicts negative psychological/emotional/mental health outcomes; (iv) EM is associated with impaired/negative perception of the real-world life; (v) EM predicts non-adaptive real social life; and (vi) EM is associated with dysfunctional gaming practices in some cases. However, EM can have beneficial effects, fostering confidence, determination, a sense of belonging in virtual communities, and representation through avatars. Furthermore, the reviewed findings suggest that EM was positively linked to mitigating loneliness in anxious individuals and promoting social activities that preserved mental health among typical individuals during the pandemic.ConclusionOur review reinforces the evidence linking EM in the context of virtual games to poor mental health and non-adaptive social behavior. The ensuing discussion explores the intricate connection between escapism and mental health, alongside examining the broad implications of virtual gaming practices on underlying motivations for escapism in the realms of social cognition, health promotion, and public health.

League of Legends dataset associated with the paper titled: Individual Performance in Team-based Online Games by Sapienza, A., Zeng, Y., Bessi, A., Lerman, K., Ferrara, E. (Royal Society Open Science 5 180329, 2018) The dataset adopted for this study was collected using the League of Legends' Riot Games API (Riot Games API: https://developer.riotgames.com/) It consists of 435,000 matches played by a sample of 1,120 of the most active players, i.e., those who played more than 100 games. The data contains information about matches, including match time and duration, and the number of deaths, kills, earned gold, gold spent, etc. for each player in each match.

This is an educational game that is based in the human body. It is going to have different parts in the series telling different stories for each part of the human body and is going to be realistic as though the player is live in the body operating or flying some sort of fighting ship. The players role is to play the part of for example, a white blood cell fighting viruses, The player will learn while having fun. The game will be authenticated I am going by doctors as consultants to endorse the game features.. There is no other game on the market with all this incorporated into it so will be the first of its kind so is marked as the intellectual property of Naipthan Phillips. Anyone who copies this idea will have to compensate Naipthan Phillips for his idea. All others that contribute to the game will be credited for their input if selected.[NP: The TP IPR games development dataset 2 (health and education)]

The dataset used in the paper is a collection of game recordings from human players playing the racing game RGSK.

The gaming industry is one of the largest in the entertainment markets for the past several decades and is steadily growing with the introduction of emerging technologies such as hardware video encoding and the new generation of broadband cellular networks, 5G. With these advancements, a new gaming paradigm called cloud gaming has emerged that makes gaming possible at any time, on any device, and at any place. Cloud gaming shifts the heavy computational tasks such as rendering to the cloud resources and streams a compressed video of players' gameplay back to the client in real-time. Similar to other telecommunication services, cloud gaming is prone to network and compression degradations such as blockiness, blurring, and network latency. These degradations could negatively affect the Quality of Experience (QoE) of users. Therefore, it is of high interest for service and network providers to measure and monitor the QoE of cloud gaming services to potentially improve the satisfaction of their customers. The present thesis aims at the development of a gaming quality model to predict the gaming QoE of players that could be used for planning the network service or quality monitoring of cloud gaming services. The model is developed following a modular structure approach that keeps the different types of impairment separately. Such a modular structure allows developing a sustainable model as each component can be updated by advances in that specific research area or technology. The gaming quality model takes into account two modules of video quality and input quality. The latter considers the interactivity aspects of gaming. The video quality module offers a series of models that differ depending on the level of access to the video stream information, allowing high flexibility for service providers regarding the positions of measuring points within their system. Before the development of the video quality module, multiple state-of-the-art image and video quality models are evaluated with gaming content. Results revealed a poor performance of No-Reference (NR) models. Thus, a special focus was given to the development of NR models for gaming content. In sum, two planning models, one bitstream model, and three NR models were developed. The models cover typical video compression as well as transmission errors. For their development, either a direct modeling approach or a multidimensional approach was used. The latter approach allows getting insight into diagnostic information of causes for impaired video quality. Among the NR models, two deep learning-based models are proposed that outperform the well-known traditional Full-Reference and NR image/video quality metrics on gaming content. In order to consider the interactivity aspects of gaming, in addition to the video related impairment factors, the impact of network parameters such as delay and packet loss was assessed. To further increase the accuracy of the proposed gaming quality model, a classification of video games according to their sensitivity towards delay and frameloss, as well as video complexity, was proposed. Parts of the core model resulted in the ITU-T Rec. G.1072 that represents a planning model predicting the QoE of cloud gaming services. In summary, the main contributions of the thesis are (1) creation of multiple image/video and cloud gaming quality datasets, (2) development of a gaming video classification, and (3) development of a series of gaming QoE models to predict the gaming QoE depending on the level of access to the video stream information.