Validating Game Design with Game Analytics in a Location-based Game

Game analytics has grown from a trend to a norm in almost all types of games, and with the availability of tools and information, leveraging benefits has never been easier. Additionally, mobile location-based games are a great platform for applying game analytics due to location information being bound to almost every facet of such games, thus enabling developers to understand where and how their games are played. This thesis is based on a research project at the University of Turku, of which a part was to create a pirate-themed location-based mobile game. The game was then studied in terms of how it was played with the help of telemetry data from the game. In this thesis, the player data is used to find out how the game's design can be validated in terms of healthiness and whether a game's design can have a positive influence on a player's health. The player data was plotted as routes for each individual player, and then inspected for different kinds of behaviour. Players were found out to play in different kinds of areas, and play during or after riding a vehicle, and play the game on the same routes repeatedly. Physical activity of players can be increased by making walking between locations a requirement, but the game mechanics have to be interesting enough, as the novelty of walking in itself is not sufficient. Ultimately, players were shown to increase their physical activity by playing the game, which meant travelling between objectives

Analise de técnicas de clusterização em MMO com dados restritos : o caso de Final Fantasy XIV

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2019.A utilização de dados de uma sessão de jogo para melhor compreensão do comportamento do jogador e os possíveis melhoramentos que podem ser realizados é um dos objetos de estudo da Game Analytics, domínio de pesquisa multidisciplinar que vem se difundindo amplamente entre pesquisadores da área de jogos eletrônicos. Entretanto, no caso dos MMOGs (Massive Multiplayer Online Games), os tipos de dados disponibilizados para análise não são padronizados, usualmente variando de um jogo para outro. Assim, um dos desafios desta área consiste em determinar o tipo de informação que pode ser obtida de um jogo MMO específico, assim como qual técnica de mineração de dados utilizar ou desenvolver em função da especificidade de sua base de dados. O objetivo deste trabalho é o estudo de técnicas de clusterização aplicadas ao Final Fantasy XIV, jogo que conta com uma base de milhões de jogadores mas disponibiliza apenas uma limitada quantidade de dados para análise e, portanto, tem sido pouco estudado na literatura. Os resultados obtidos poderão contribuir para uma melhor compreensão sobre os grupos de jogadores contidos em Final Fantasy XIV e fornecer uma base para o desenvolvimento de trabalhos futuros, além de prover um estudo de caso sobre técnicas de clusterização aplicadas sobre um limitado conjunto de dados de jogo.CAPESUsing data from a game session to better understand player behavior and possible improvements that can be made in a game is one of the objects of study at Game Analytics, a multidisciplinary research domain that has been widely spread among researchers in the field of electronic games. However, for Massive Multiplayer Online Games (MMOGs), the types of data available for analysis are not standardized, usually varying from game to game. Thus, one of the challenges in this area is to determine the type of information that can be obtained from a specific MMO game, as well as which data mining technique to use or develop depending on the specificity of its database. The aim of this paper is the study of clustering techniques applied to Final Fantasy XIV, a game that has a player base of millions but provides only a limited amount of game data for analysis and, therefore, has been little studied in the literature. The results obtained may contribute to a better understanding of the Final Fantasy XIV player groups and provide a basis for future work, as well as provide a case study on clustering techniques applied over a limited set of game data