Game Analytics är ett område som vuxit fram under senare år. Spelutvecklare har möjligheten att analysera hur deras kunder använder deras produkter ned till minsta knapptryckning. Detta kan resultera i stora mängder data och utmaning ligger i att lyckas göra något vettigt av sitt data. Utmaningarna med speldata beskrivs ofta med liknande egenskaper som används för att beskriva Big Data: volume, velocity och variability. Detta borde betyda att det finns potential för ett givande samarbete. Studiens syfte är att analysera och utvärdera vilka möjligheter Big Data ger att utveckla området Game Analytics. För att uppfylla syftet genomförs en litteraturstudie och semi-strukturerade intervjuer med individer aktiva inom spelbranschen. Resultatet visar att källorna är överens om att det finns värdefull information bland det data som kan lagras, framförallt i de monetära, generella och centrala (core) till spelet värdena. Med mer avancerad analys kan flera andra intressanta mönster grävas fram men ändå är det övervägande att hålla sig till de enklare variablerna och inte bry sig om att gräva djupare. Det är inte för att datahanteringen skulle bli för omständlig och svår utan för att analysen är en osäker investering. Även om någon tar sig an alla utmaningar speldata ställer fram finns det en osäkerhet på informationens tillit och användbarheten hos svaren. Framtidsvisionerna inom Game Analytics är blygsamma och inom den närmsta framtiden är det nästan bara effektiviseringar och en utbredning som förutspås vilket inte direkt ställer några nya krav på datahanteringen. / Game Analytics is a research field that appeared recently. Game developers have the ability to analyze how customers use their products down to every button pressed. This can result in large amounts of data and the challenge is to make sense of it all. The challenges with game data is often described with the same characteristics used to define Big Data: volume, velocity and variability. This should mean that there is potential for a fruitful collaboration. The purpose of this study is to analyze and evaluate what possibilities Big Data has to develop the Game Analytics field. To fulfill this purpose a literature review and semi-structured interviews with people active in the gaming industry were conducted. The results show that the sources agree that valuable information can be found within the data you can store, especially in the monetary, general and core values to the specific game. With more advanced analysis you may find other interesting patterns as well but nonetheless the predominant way seems to be sticking to the simple variables and staying away from digging deeper. It is not because data handling or storing would be tedious or too difficult but simply because the analysis would be too risky of an investment. Even if you have someone ready to take on all the challenges game data sets up, there is not enough trust in the answers or how useful they might be. Visions of the future within the field are very modest and the nearest future seems to hold mostly efficiency improvements and a widening of the field, making it reach more people. This does not really post any new demands or requirements on the data handling.

[pt] Game Analytics é uma área que envolve o processamento de dados de videogames com a finalidade de proporcionar uma melhor experiência de jogo para o usuário. Também ajuda a verificar os padrões de comportamento dos jogadores, facilitando a identificação do público-alvo. A coleta de dados dos jogadores ajuda os desenvolvedores de jogos a identificar problemas mais cedo e saber por que os jogadores deixaram o jogo ou continuaram jogando. O comportamento desses jogadores geralmente segue um padrão, fazendo com que se encaixem em diferentes perfis de jogadores. Especialistas em análise de jogos criam e usam modelos de tipos de jogadores, geralmente variantes do modelo de Bartle, para ajudar a identificar perfis de jogadores. Esses especialistas usam algoritmos de agrupamento para separar os jogadores em grupos diferentes e identificáveis, rotulando cada grupo com o tipo de perfil definido pelo modelo proposto. O objetivo principal deste projeto é criar um plugin Unity genérico para ajudar a identificar perfis de jogadores em jogos. Este plugin usa uma API Python, que lida com os dados do jogo armazenados em um banco de dados MongoDB, para agrupar e rotular cada partida ou nível do jogo escolhido enquanto o jogo está em execução. Neste plugin, os desenvolvedores de jogos podem configurar o número de tipos de jogadores que desejam identificar, os rótulos dos jogadores e até os algoritmos que desejam usar. Essa abordagem de agrupamento online não é usual no desenvolvimento de jogos. Até onde sabemos, não há nenhum componente de software na literatura de análise de jogos com a mesma direção e recursos. / [en] Game Analytics is an area that involves the processing of video game data, in order to make a better game experience for the user. It also helps to check the patterns in players behaviour, making it easier to identify the target audience. Gathering player data helps game developers identify problems earlier and know why players left the game or kept playing. These players behavior usually follows a pattern, making them fit in different player profiles. Game analytics experts create and use models of player types, usually variants of Bartle s model, to help identify player profiles. These experts use clustering algorithms to separate players into different and identifiable groups, labeling each group with the profile type defined by the proposed model. The main goal of this project is to create a generic Unity plugin to help identify Player Profiles in games. This plugin uses a Python API, which deals with the game data stored in a MongoDB database, to cluster and label each match or level of the chosen game while the game is running. In this plugin, game developers can configure the number of player types they want to identify, the player labels, and even the algorithms they wish to use. This online clustering approach is not usual in game development. As far as we are aware, there is no software component in the game analytics literature with the same direction and features.

Elements of randomness are a common factor inmodern digital games, from simple rolls of a die tocomplex AI systems. These elements have an impacton how the player experiences a game. We believe thatexploring the field of luck analysis can benefit designersthrough an developed understanding of how such elementsaffect players. The developers of the digital card gameSlay the Spire has released 77 millions of instances ofplay and sample of these are explored in this study. Withthe use of data mining, data clustering and correlationanalysis the effect of elements of randomness present inSlay the Spire are analyzed. In conclusion, three playerskill groups were identified with the use of clustering:Winners, Low skill losers and High skill losers. Weobserved that people who succeeded in beating the game,had an increased amount of randomness in the formof cards by a factor of 1.82. Showing that more skilledplayers do not shy away from randomness but insteadembrace it more than lower skilled players.

Background: Game analytics is a field that aims to analyze games and help in the enhancement of game development. Data mining is a prominent technique for game analytics. Recent advances in the field of process mining have motivated users to apply process mining to real-world scenarios in order to derive process-oriented insights. In this study, We provide a discussion on how process mining can be used in game analytics. Objective: The goal of this study is to apply process mining to player data from a live game, analyze the results, and determine whether these results can be interpreted, whether we can derive any patterns or insights that can be useful for game designers, and whether process mining can be used in-game analytics and, if so, what kind of versatility it can offer. Also, this study provides approaches on how process mining can be used in player churn prediction and determination of abnormal player activity sequences. Method: Firstly, a literature review is performed to comprehend all of the process mining techniques and metrics used to evaluate the discovered process models. Then experiments are conducted by applying process mining on data from a live game, determine a churn predictor using process mining and determining a technique to identify abnormal player sequences. Results: Process discovery algorithms are applied on data from a live game, the results are analyzed. Several process models are discovered to identify player churn and it is compared with a baseline machine learning churn predictor trained on the same data to that of process mining. Abnormal player activity sequences of the gameare determined using process mining and compared with expected player sequences and analyzed with the help of game designers. Conclusion: Process mining can be utilized in game analytics to discover new process-oriented insights. When compared to typical data mining techniques, the results gained by process mining are more versatile. It also has other capabilities such as detecting unusual sequences in data.