Pub Date : 2021-10-14DOI: 10.1093/oso/9780192897879.003.0008
M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen
This chapter focuses on two specific steps in the machine learning process, called model validation and model evaluation. Specifically, model validation is the step used to tune the hyperparameters of the model. Here, we often integrate a cross-validation process, which we discuss in detail in this chapter. Model evaluation, on the other hand, is the process of testing the performance of the model using unseen data, the test dataset. These processes are used to ensure that the model we developed through the algorithms discussed in Chapter 6 are reliable, given our data. The chapter will include labs to give you a practical introduction to these steps, given the modeling techniques discussed in the last chapter.
{"title":"Supervised Learning in Game Data Science: Model Validation and Evaluation","authors":"M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen","doi":"10.1093/oso/9780192897879.003.0008","DOIUrl":"https://doi.org/10.1093/oso/9780192897879.003.0008","url":null,"abstract":"This chapter focuses on two specific steps in the machine learning process, called model validation and model evaluation. Specifically, model validation is the step used to tune the hyperparameters of the model. Here, we often integrate a cross-validation process, which we discuss in detail in this chapter. Model evaluation, on the other hand, is the process of testing the performance of the model using unseen data, the test dataset. These processes are used to ensure that the model we developed through the algorithms discussed in Chapter 6 are reliable, given our data. The chapter will include labs to give you a practical introduction to these steps, given the modeling techniques discussed in the last chapter.","PeriodicalId":137223,"journal":{"name":"Game Data Science","volume":"85 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":"131656719","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}
Pub Date : 2021-10-14DOI: 10.1093/oso/9780192897879.003.0001
M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen
This chapter introduces the topic of this book: Game Data Science. Game data science is the process of developing data-driven techniques and evidence to support decision-making across operational, tactical, and strategic levels of game development, and this is why it is so valuable. This chapter introduces this topic as well as outlines the process of game data science from instrumentation, data collection, data processing, data analysis, to reporting. Further, the chapter also discusses the application of game data science, as well as its utility and value, to the different stakeholders. The chapter also includes a section discussing the evolution of this process over time, which is important to situate the field and the techniques discussed in the book. The chapter also outlines established industry terminologies and defines their use in the industry and academia.
{"title":"Game Data Science: An Introduction","authors":"M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen","doi":"10.1093/oso/9780192897879.003.0001","DOIUrl":"https://doi.org/10.1093/oso/9780192897879.003.0001","url":null,"abstract":"This chapter introduces the topic of this book: Game Data Science. Game data science is the process of developing data-driven techniques and evidence to support decision-making across operational, tactical, and strategic levels of game development, and this is why it is so valuable. This chapter introduces this topic as well as outlines the process of game data science from instrumentation, data collection, data processing, data analysis, to reporting. Further, the chapter also discusses the application of game data science, as well as its utility and value, to the different stakeholders. The chapter also includes a section discussing the evolution of this process over time, which is important to situate the field and the techniques discussed in the book. The chapter also outlines established industry terminologies and defines their use in the industry and academia.","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":"116273451","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}
Pub Date : 2021-10-14DOI: 10.1093/oso/9780192897879.003.0005
M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen
This chapter discusses the topic of how one can use visualization techniques to analyze game data. Specifically, the chapter delves into the development of heatmaps to analyze spatio-temporal data. The chapter also discusses spatio-temporal visualizations and state-action transition visualizations. We also discuss two visualization systems that we have developed within the GUII lab: Stratmapper and Glyph. We provide you with a link that allows you to explore the use of these visualizations with real game data. This chapter is written in collaboration with Riddhi Padte and Varun Sriram, based on their work in Dr. Seif El-Nasr’s game data science class at Northeastern University; Erica Kleinman, PhD student at University of California at Santa Cruz; and Andy Bryant, software engineer at GUII Lab. The chapter also includes labs where you get to experience the analysis of game data through visualization.
{"title":"Data Analysis through Visualization","authors":"M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen","doi":"10.1093/oso/9780192897879.003.0005","DOIUrl":"https://doi.org/10.1093/oso/9780192897879.003.0005","url":null,"abstract":"This chapter discusses the topic of how one can use visualization techniques to analyze game data. Specifically, the chapter delves into the development of heatmaps to analyze spatio-temporal data. The chapter also discusses spatio-temporal visualizations and state-action transition visualizations. We also discuss two visualization systems that we have developed within the GUII lab: Stratmapper and Glyph. We provide you with a link that allows you to explore the use of these visualizations with real game data. This chapter is written in collaboration with Riddhi Padte and Varun Sriram, based on their work in Dr. Seif El-Nasr’s game data science class at Northeastern University; Erica Kleinman, PhD student at University of California at Santa Cruz; and Andy Bryant, software engineer at GUII Lab. The chapter also includes labs where you get to experience the analysis of game data through visualization.","PeriodicalId":137223,"journal":{"name":"Game Data Science","volume":"1 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":"129058689","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}
Pub Date : 2021-10-14DOI: 10.1093/oso/9780192897879.003.0006
M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen
This chapter discusses different clustering methods and their application to game data. In particular, the chapter details K-means, Fuzzy C-Means, Hierarchical Clustering, Archetypical Analysis, and Model-based clustering techniques. It discusses the disadvantages and advantages of the different methods and discusses when you may use one method vs. the other. It also identifies and shows you ways to visualize the results to make sense of the resulting clusters. It also includes details on how one would evaluate such clusters or go about applying the algorithms to a game dataset. The chapter includes labs to delve deeper into the application of these algorithms on real game data.
{"title":"Clustering Methods in Game Data Science","authors":"M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen","doi":"10.1093/oso/9780192897879.003.0006","DOIUrl":"https://doi.org/10.1093/oso/9780192897879.003.0006","url":null,"abstract":"This chapter discusses different clustering methods and their application to game data. In particular, the chapter details K-means, Fuzzy C-Means, Hierarchical Clustering, Archetypical Analysis, and Model-based clustering techniques. It discusses the disadvantages and advantages of the different methods and discusses when you may use one method vs. the other. It also identifies and shows you ways to visualize the results to make sense of the resulting clusters. It also includes details on how one would evaluate such clusters or go about applying the algorithms to a game dataset. The chapter includes labs to delve deeper into the application of these algorithms on real game data.","PeriodicalId":137223,"journal":{"name":"Game Data Science","volume":"13 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":"123676347","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}
Pub Date : 2021-10-14DOI: 10.1093/oso/9780192897879.003.0007
M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen
This chapter discusses several classification and regression methods that can be used with game data. Specifically, we will discuss regression methods, including Linear Regression, and classification methods, including K-Nearest Neighbor, Naïve Bayes, Logistic Regression, Linear Discriminant Analysis, Support Vector Machines, Decisions Trees, and Random Forests. We will discuss how you can setup the data to apply these algorithms, as well as how you can interpret the results and the pros and cons for each of the methods discussed. We will conclude the chapter with some remarks on the process of application of these methods to games and the expected outcomes. The chapter also includes practical labs to walk you through the process of applying these methods to real game data.
{"title":"Supervised Learning in Game Data Science","authors":"M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen","doi":"10.1093/oso/9780192897879.003.0007","DOIUrl":"https://doi.org/10.1093/oso/9780192897879.003.0007","url":null,"abstract":"This chapter discusses several classification and regression methods that can be used with game data. Specifically, we will discuss regression methods, including Linear Regression, and classification methods, including K-Nearest Neighbor, Naïve Bayes, Logistic Regression, Linear Discriminant Analysis, Support Vector Machines, Decisions Trees, and Random Forests. We will discuss how you can setup the data to apply these algorithms, as well as how you can interpret the results and the pros and cons for each of the methods discussed. We will conclude the chapter with some remarks on the process of application of these methods to games and the expected outcomes. The chapter also includes practical labs to walk you through the process of applying these methods to real game data.","PeriodicalId":137223,"journal":{"name":"Game Data Science","volume":"147 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":"133591887","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}
Pub Date : 2021-10-14DOI: 10.1093/oso/9780192897879.003.0003
M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen
This chapter introduces the basics of statistics and probability theory that will be used throughout the book. Specifically, it introduces the concepts behind descriptive statistics, including aspects of using visualization of means, medians, and modes, as well as distribution visualizations to understand your data for further analysis. It also introduces inferential statistics, specifically discussing t-tests and ANOVA, discussing the assumptions used for each of the tests and outputs. The chapter also includes labs where we use real game data to give you a practical understanding of how to apply these concepts and tests and how to interpret the meaning of the results you get from each test and method.
{"title":"Introduction to Statistics and Probability Theory","authors":"M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen","doi":"10.1093/oso/9780192897879.003.0003","DOIUrl":"https://doi.org/10.1093/oso/9780192897879.003.0003","url":null,"abstract":"This chapter introduces the basics of statistics and probability theory that will be used throughout the book. Specifically, it introduces the concepts behind descriptive statistics, including aspects of using visualization of means, medians, and modes, as well as distribution visualizations to understand your data for further analysis. It also introduces inferential statistics, specifically discussing t-tests and ANOVA, discussing the assumptions used for each of the tests and outputs. The chapter also includes labs where we use real game data to give you a practical understanding of how to apply these concepts and tests and how to interpret the meaning of the results you get from each test and method.","PeriodicalId":137223,"journal":{"name":"Game Data Science","volume":"24 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":"130867736","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}
Pub Date : 2021-10-14DOI: 10.1093/oso/9780192897879.003.0009
M. S. El-Nasr, T. Dinh, Alessandro Canossa, Anders Drachen
This chapter will introduce the use of Neural Networks (NN) in game data science. Due to the availability of game data and the increase in computational power, the use of NNs and deep networks is on the rise in data science in general, and specifically within the field of game data science. Complex deep networks are used as they can generalize to highly complex relationships over unseen data and, as a result, provide better performance than traditional models. Such networks have been used to serve many purposes within the game production cycle, including churn predicting, predicting and measuring customer lifetime value, recommending items, as well as discovering and forecasting player behavior patterns. Deep learning has shown good performance and results on these problems. This chapter will detail different types of algorithms used for both Feedforward Neural Networks (FNNs) as well as Convolutional Neural Networks (CNNs). It also includes several case studies and examples of game projects to show the utility of these methods for game design and development. This chapter was written in collaboration with Sabbir Ahmed, a PhD student at Northeastern University.
{"title":"Neural Networks","authors":"M. S. El-Nasr, T. Dinh, Alessandro Canossa, Anders Drachen","doi":"10.1093/oso/9780192897879.003.0009","DOIUrl":"https://doi.org/10.1093/oso/9780192897879.003.0009","url":null,"abstract":"This chapter will introduce the use of Neural Networks (NN) in game data science. Due to the availability of game data and the increase in computational power, the use of NNs and deep networks is on the rise in data science in general, and specifically within the field of game data science. Complex deep networks are used as they can generalize to highly complex relationships over unseen data and, as a result, provide better performance than traditional models. Such networks have been used to serve many purposes within the game production cycle, including churn predicting, predicting and measuring customer lifetime value, recommending items, as well as discovering and forecasting player behavior patterns. Deep learning has shown good performance and results on these problems. This chapter will detail different types of algorithms used for both Feedforward Neural Networks (FNNs) as well as Convolutional Neural Networks (CNNs). It also includes several case studies and examples of game projects to show the utility of these methods for game design and development. This chapter was written in collaboration with Sabbir Ahmed, a PhD student at Northeastern University.","PeriodicalId":137223,"journal":{"name":"Game Data Science","volume":"27 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":"132979376","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}
Pub Date : 2021-10-14DOI: 10.1093/oso/9780192897879.003.0011
M. S. El-Nasr, T. Dinh, Alessandro Canossa, Anders Drachen
This chapter discusses more advanced methods for sequence analysis. These include: probabilistic methods using classical planning, Bayesian Networks (BN), Dynamic Bayesian Networks (DBNs), Hidden Markov Models (HMMs), Markov Logic Networks (MLNs), Markov Decision Process (MDP), and Recurrent Neural Networks (RNNs), specifically concentrating on LSTM (Long Short-Term Memory). These techniques are all great but, at this time, are mostly used in academia and less in the industry. Thus, the chapter takes a more academic approach, showing the work and its application to games when possible. The techniques are important as they cultivate future directions of how you can think about modeling, predicting players’ strategies, actions, and churn. We believe these methods can be leveraged in the future as the field advances and will have an impact in the industry. Please note that this chapter was developed in collaboration with several PhD students at Northeastern University, specifically Nathan Partlan, Madkour Abdelrahman Amr, and Sabbir Ahmad, who contributed greatly to this chapter and the case studies discussed.
{"title":"Advanced Sequence Analysis","authors":"M. S. El-Nasr, T. Dinh, Alessandro Canossa, Anders Drachen","doi":"10.1093/oso/9780192897879.003.0011","DOIUrl":"https://doi.org/10.1093/oso/9780192897879.003.0011","url":null,"abstract":"This chapter discusses more advanced methods for sequence analysis. These include: probabilistic methods using classical planning, Bayesian Networks (BN), Dynamic Bayesian Networks (DBNs), Hidden Markov Models (HMMs), Markov Logic Networks (MLNs), Markov Decision Process (MDP), and Recurrent Neural Networks (RNNs), specifically concentrating on LSTM (Long Short-Term Memory). These techniques are all great but, at this time, are mostly used in academia and less in the industry. Thus, the chapter takes a more academic approach, showing the work and its application to games when possible. The techniques are important as they cultivate future directions of how you can think about modeling, predicting players’ strategies, actions, and churn. We believe these methods can be leveraged in the future as the field advances and will have an impact in the industry. Please note that this chapter was developed in collaboration with several PhD students at Northeastern University, specifically Nathan Partlan, Madkour Abdelrahman Amr, and Sabbir Ahmad, who contributed greatly to this chapter and the case studies discussed.","PeriodicalId":137223,"journal":{"name":"Game Data Science","volume":"172 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":"121258722","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}
Pub Date : 2021-10-14DOI: 10.1093/oso/9780192897879.003.0002
M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen
This chapter focuses on the process of cleaning data and preparing it for further processing. Specifically, the chapter discusses various techniques that you will use, including preprocessing, outlier identification, data consistency, and the normalization or standardization process, used to normalize your data. The chapter further discusses different measurement types and what methods can be used for which types. The chapter also discusses ways to deal with issues you may encounter with inconsistent or dirty data. The chapter takes a more practical approach by integrating several labs with actual game data to demonstrate how you can perform these steps on real game data.
{"title":"Data Preprocessing","authors":"M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen","doi":"10.1093/oso/9780192897879.003.0002","DOIUrl":"https://doi.org/10.1093/oso/9780192897879.003.0002","url":null,"abstract":"This chapter focuses on the process of cleaning data and preparing it for further processing. Specifically, the chapter discusses various techniques that you will use, including preprocessing, outlier identification, data consistency, and the normalization or standardization process, used to normalize your data. The chapter further discusses different measurement types and what methods can be used for which types. The chapter also discusses ways to deal with issues you may encounter with inconsistent or dirty data. The chapter takes a more practical approach by integrating several labs with actual game data to demonstrate how you can perform these steps on real game data.","PeriodicalId":137223,"journal":{"name":"Game Data Science","volume":"186 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":"132931023","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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