M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen
{"title":"Conclusions and Remarks","authors":"M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen","doi":"10.1201/b21590-11","DOIUrl":"https://doi.org/10.1201/b21590-11","url":null,"abstract":"This chapter summarizes the takeaways from the different chapters of this book on the game data science process we introduced in Chapter 1. It shares some notes and experiences that can help you when embarking on using the methods discussed in this book. As a conclusion chapter, it will also delve into important topics that are not discussed in other chapters in this book, such as ethics, reproducibility problems, dealing with distributed big data, building bots from game data, using probabilistic models, etc. The chapter will also discuss the overall applications of game data science within the production process and will conclude by discussing where we see the future of the field going.","PeriodicalId":137223,"journal":{"name":"Game Data Science","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131990869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Case Study","authors":"Magy Seif El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen","doi":"10.1093/oso/9780192897879.003.0012","DOIUrl":"https://doi.org/10.1093/oso/9780192897879.003.0012","url":null,"abstract":"This chapter discusses Social Network Analysis, a technique used to analyze social networks within social games as a method to enhance retention in games. We will show how one can use this method by applying it to the problem of retention within the game Tom Clancy’s The Division (TCTD). Using the game and the analysis will help you understand how to use SNA to understand types of players and influential players, and, as a result, understand how to engage different players, especially influencers, to increase retention. While the chapter will focus on the use of SNA for TCTD as a case study, the methods discussed under SNA can be applied to other types of games. Please note that this chapter is an extension of the work done by several collaborators to the authors, including Casper Harteveld (professor, Northeastern University), Sebastian Deterding (professor, York University), and Ahmad Azadvar (User Research Lead at Ubisoft Massive), and the work was accomplished with the support of Ubisoft, the Games Lab, and the Live Ops team at Massive Entertainment.","PeriodicalId":137223,"journal":{"name":"Game Data Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128827024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Game Data SciencePub Date : 2021-10-14DOI: 10.1093/oso/9780192897879.003.0010
M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen
{"title":"Sequence Analysis of Game Data","authors":"M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen","doi":"10.1093/oso/9780192897879.003.0010","DOIUrl":"https://doi.org/10.1093/oso/9780192897879.003.0010","url":null,"abstract":"This chapter is devoted to sequence game data analysis. It will first define what sequence data is and how it is represented, and then delve more deeply into how to develop models from such data. Sequence analysis has a lot of utility and is important as it conserves the sequence of player actions and can shed light on how players solved different problems within the different game levels. Further, sequence analysis can also be a great way to develop a more robust and accurate player model. The chapter will discuss such advantages in light of showcasing the use of sequential analysis for DOTA 2. Further, the chapter will also be a practical guide on how to develop models from sequence data using practical step-by-step labs. Please note that this chapter was written with Erica Kleinman (a PhD student at University of California at Santa Cruz).","PeriodicalId":137223,"journal":{"name":"Game Data Science","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127670505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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