• Title/Summary/Keyword: Game Data Analysis

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Expert Review and Analysis of the Game's Testing Process -Focus on balance testing- (게임의 테스트 프로세스에 따른 전문가 검토 및 분석 -밸런스 테스트를 중심으로-)

  • Lee, Yoon-Yim;Rhee, Dea-Woong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.7
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    • pp.1013-1018
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    • 2022
  • Game Industry sustained growth for some time, but the lifespan of a game is shortening. Various efforts to improve the quality of services for the game players which play a role in extending the lifespan of games. When a game is serviced, the server of the game starts to store log informations, and the stored data became important measures to predict game user's activities. As the game's data gathers, it becomes highly useful big data. By analyzing the data of the game stored in this way, a game service issue analysis procedure is proposed to improve the quality of the game service and to proceed with a better service, and based on the analysis in this way, it was applied to the balance test process and verified through expert to the balance test process. If the log analysis process is applied through this paper, it will be a basic data that can improve the quality of game services.

An Exploratory Study on the Extraction of Game Addiction Factors (게임 중독 요인추출에 관한 탐색적 연구)

  • Park, Jeong-Eun;Kwon, Hyeog-In
    • Journal of Information Technology Services
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    • v.6 no.3
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    • pp.163-177
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    • 2007
  • This research is the concept of a game addiction absorbed in the game based on the review and analysis of factors that affect the characteristics of game addiction, it is appropriate to extract the purpose of factors. Game addiction factor is composed of a total of 32 questions, and a total of 356 people, to collect data through surveys. Factor analysis of the collected data to the target as a result of physical and mental problems, loss of control, tolerance, and avoidance of real world consists of three sub-factors. Factors that affect flow of tolerance and loss of control, and avoid the real world, including two sub-factors that could determine. Diagnostic game addiction factor in the reliability coefficients (Cronbach alpha) is a robust .966 aspects in the event. The game addiction scale score of 90-game addiction by category 'regular user', 90 points and 114 between the terms 'potentially dangerous user' and 13 percent of the overall. Finally, more than 115 points in the 'high-risk user' has been classified as 5% of the overall distribution of the notice that. Such factors extract game is a game addict, addicted users of the game and tend to properly evaluate and navigate game addiction-related problems early in the game addiction and found it could be used properly.

A Realtime Analytical System of Football Game

  • Min, Dae-kee
    • Communications for Statistical Applications and Methods
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    • v.8 no.2
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    • pp.557-564
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    • 2001
  • The objective of he study is to record the real conditions along with the soccer ball that is, each player's ball keeping time, the number football keeping, accuracy of passing to other player, direction, etc., on a real-time basis, measure them in numbers and get necessary analyzed output as much as one needs. The study consists of the following stages: (1) Record the data by drawing through Visual Interface on a real-time basis; (2) Graphic windows to display the recorded data item by item in graphic; (3) Form windows to display the individual or team scores anytime when needed; (4) Windows to display the analyzed data in visualized form. The effect of the study is threefold: (1) It inputs all the game-related data on a real-time basis, which was impossible before and shows analyzed contents during the game enabling each tea manager o use; (2) In cse of TV broadcasting or newspaper articles, it explains objectively the situations of he game to the TV viewers or readers; (3) After the game, it provides importance information on each team's playing ability and individual player's technical improvement through data analysis.

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Traffic Analysis and Modeling for Network Games (네트워크 게임 트래픽 분석 및 모델링)

  • Park Hyo-Joo;Kim Tae-Yong
    • Journal of Korea Multimedia Society
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    • v.9 no.5
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    • pp.635-648
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    • 2006
  • As the advances of Internet infra structure and the support of console and mobile for network games, the industry of online game has been growing rapidly, and the online game traffic in the Internet has been increasing steadily. For design and simulation of game network, the analysis of online game traffic have to be preceded. Therefore a number of papers have been proposed for the purpose of analyzing the traffic data of network games and providing the models. We make and use GameNet Analyzer as a dedicated tool for game traffic measurement and analysis in this paper. We measure the traffic of FPS Quake 3, RTS Starcraft and MMORPG World of Warcraft (WoW), and analyze the packet size, packet IAT(inter-arrival time), data rate and packet rate according to the number of players and in-game behaviors. We also present the traffic models using measured traffic data. These analysis and models of game traffic can be used for effective network simulation, performance evaluation of game network and the design of online games.

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A Study on Utilization of Facial Recognition-based Emotion Measurement Technology for Quantifying Game Experience (게임 경험 정량화를 위한 안면인식 기반 감정측정 기술 활용에 대한 연구)

  • Kim, Jae Beom;Jeong, Hong Kyu;Park, Chang Hoon
    • Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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    • v.7 no.9
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    • pp.215-223
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    • 2017
  • Various methods for creating interesting games are used in the development process. Because the empirical part is difficult to measure and analyze, it usually only measures and analyzes the parts where data are easy to quantify. This is a clear limit to the fact that the experience of the game is important.This study proposes a system that recognizes the face of a game user and measures the emotion change from the recognized information in order to easily quantify the experience of the user who is playing the game. The system recognizes emotions and records them in real time from the face of the user who is playing the game. These recorded data include time and figures related to the progress of the game, and numerical values for emotions recognized from the face. Using the recorded data, it is possible to judge what kind of emotion the game induces to the user at a certain point in time. Numerical data on the recorded empirical part using the system of this study is expected to help develop the game according to the developer 's intention.

Success Factors of Game Products by Using a Diffusion Model and Cluster Analysis (확산모형과 군집분석을 이용한 게임제품의 흥행요소 분석)

  • Song, Sungmin;Cho, Nam-Wook;Kim, Taegu
    • Journal of Korean Institute of Industrial Engineers
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    • v.42 no.3
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    • pp.222-230
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    • 2016
  • As the global game market has been more competitive, it has been important to analyze success factors of game products. In this paper, we applied a Bass Diffusion Model and Clustering Analysis to identify the success factors of games based on data from Steam, an international game platform. By using a diffusion model, we first categorize game products into two groups : successful and unsuccessful games. Then, each group has been analyzed by using clustering analysis based on product features such as genres, price, and minimum system requirements. As a result, success factors of a game have been identified. The result shows that customers in game industry appreciate sophisticated contents. Unlike many other industries, price is not considered as a key success factor in the game industry. Expecially, advanced independent video games (commonly referred to as indie games) with killer contents show competitiveness in the market.

Design and Application of a Winning Forecast Model of the AOS Genre Game (AOS 장르 게임의 승패 예측 모형의 설계와 활용)

  • Ku, Ji-Min;Yu, Kyeonah
    • KIISE Transactions on Computing Practices
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    • v.23 no.1
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    • pp.37-44
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    • 2017
  • Games of the AOS genre are classified as an e-sport rather than a recreational computer game. The involved statistical analyses such as game playing patterns and the season's characters gain importance due to the expertise-requiring nature of sports. In this study, the strategic analysis of computer games was conducted by using data mining techniques on League of Legend, a representative AOS game. We designed and tested a winning forecast model using winning percentage prediction techniques such as logistic regression analysis, discriminant analysis, and artificial neural networks. The game data analysis results were represented by a probabilistic graph and used in the visualization tool for game play. Experimental results of the winning forecast model showed a high classification rate of 95% on average with potential for use in establishing various strategies for game play with the visualization tool.

An Analysis of Game Strategy and User Behavior Pattern Using Big Data: Focused on Battlegrounds Game (빅데이터를 활용한 게임 전략 및 유저 행동 패턴 분석: 배틀그라운드 게임을 중심으로)

  • Kang, Ha-Na;Yong, Hye-Ryeon;Hwang, Hyun-Seok
    • Journal of Korea Game Society
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    • v.19 no.4
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    • pp.27-36
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    • 2019
  • Approaches to find hidden values using various and enormous amount of data are on the rise. As big data processing becomes easier, companies directly collects data generated from users and analyzes as necessary to produce insights. User-based data are utilized to predict patterns of gameplay, in-game symptom, eventually enhancing gaming. Accordingly, in this study, we tried to analyze the gaming strategy and user activity patterns utilizing Battlegrounds in-game data to detect the in-game hack.

Development of game indicators and winning forecasting models with game data (게임 데이터를 이용한 지표 개발과 승패예측모형 설계)

  • Ku, Jimin;Kim, Jaehee
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.2
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    • pp.237-250
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    • 2017
  • A new field of e-sports gains the great popularity in Korea as well as abroad. AOS (aeon of strife) genre games are quickly gaining popularity with gamers from all over the world and the game companies hold game competitions. The e-sports broadcasting teams and webzines use a variety of statistical indicators. In this paper, as an AOS genre game, League of Legends game data is used for statistical analysis using the indicators to predict the outcome. We develop new indicators with the factor analysis to improve existing indicators. Also we consider discriminant function, neural network model, and SVM (support vector machine) for make winning forecasting models. As a result, the new position indicators reflect the nature of the role in the game and winning forecasting models show more than 95 percent accuracy.

A Study on Game Satisfaction and Game Balance of MOBA Game in New Season Update (MOBA 게임 뉴시즌 Update를 위한 게임 만족 및 밸런스 연구)

  • Li, Jing;Cho, Dong-Min
    • Journal of Korea Multimedia Society
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    • v.24 no.8
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    • pp.1161-1170
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    • 2021
  • The MOBA game is updated every year with a new season, the new season update brings fun to the game users and also makes them feel unfamiliar with the new gameplay. In order to let game users better adapt to the new season, this study will extract the new season balance evaluation factors. Firstly, we used one-on-one review to collect the unbalanced factors that game users had encountered at the beginning of the new season of the MOBA game, and secondly, we organized the collected review data into a questionnaire and conducted a survey. The first step of the experiment was to filter out the lower factors through exploratory factor analysis and extract the balance evaluation factors of the MOBA game new season. The second step of the experiment was to examine the correlation between the factors through confirmatory factor analysis, as well as to confirm the appropriateness and explanatory value of the factors. The analysis resulted in "Game character experience", "Game view's expression", "Game level", and "Composition of the game setting" are the four factors. Finally, through correlation analysis, the most relevant factor for game satisfaction is the "Game character experience", and each factor is correlated with each other.