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1: A Brief Overview of Data Mining and Analytics in GamesДЎ.2.1 Data Analytics to Improve Design and Player ExperienceДЎ.2.2 Data Analytics to Inform Business DecisionsДЎ.2.3 Data Analytics to Innovate and Optimize Game TechnologyДЎ.2.4 Data Analytics to Empower Players and Foster Community BuildingДў: Evaluating Gamer Achievements to Understand Player BehaviorДЈ.6.3 Extension: Performance-Based RatingsД¤: A Data Science Approach to Exploring Hero Roles in Multiplayer Online Battle Arena GamesД¤.4.5 Comparison to Classifications of Blizzard Entertainment and Popular Fan SitesДҐ: Predicting Customer Lifetime Value in Free-to-Play GamesДҐ.4.2.1 Recency, Frequency, and Monetary ValueД¦: Advanced Data Science Models for Player Behavioral PredictionД¦.2 Predicting Churn and Player Life Expectancy with Ensemble LearningД¦.2.3 Data Selection, Modeling, and ValidationД¦.2.3.1 Data Selection and KaplanвЂ“Meier VisualizationД¦.2.3.2 Conditional Inference Survival EnsemblesД¦.3 A Machine Learning-Based Lifetime Value Computation: Deep Learning and Parametric ModelsД¦.3.1 Historical and Predictive Approaches to Compute Lifetime ValueД¦.3.1.1 Pareto/Negative Binomial Distribution ModelД¦.3.2 Lifetime Value Using Deep Learning MethodsД¦.3.3 Lifetime Value Using Deep Learning and Parametric Models: A Case StudyД§: Integrating Social and Textual Analytics into Game AnalyticsД§.2.1 Traditional Metrics and Key Performance IndicatorsД§.2.2 Textual Methods and Computational LinguisticsД§.2.2.1 Text Normalization and Social Media Linguistic AnalysisД§.2.2.2 Text Mining and Sentiment AnalysisД§.4.2 Integration of Social and Textual AnalyticsД§.5.4 Activation, Engagement, and Retention of Posters versus Non-PostersДЁ: Social Network Analysis Applied to Game Communities to Identify Key Social PlayersД©: Methodological and Epistemological Reflections on the Use of Game Analytics toward Understanding the Social Relationships of a Video Game CommunityД©.2.1 Shifting from Games-as-a-Product to Games-as-a-ServiceД©.2.2 Predominance of Analytics Tools in the IndustryД©.2.6 Responding to Questions in Communication Studies Using Community MetricsД©.3 Mixed-Methods Research with In-Game and Out-of-Game DataД©.4 Challenges That Arise from the Nature of the Collected Data Inside the GameД©.4.1 Results Whose Meaning Varies According to ContextД©.4.3 Collected Information Aggregated with Other DataДЎ1: A Taxonomy of Visualizations for Gameplay DataДЎ2: Co-Design of an Interactive Analytics System for Multiplayer Online Battle Arena Game OccurrencesДЎ2.2.1 Visual Analysis of Gameplay BehaviorДЎ2.2.2 Data-Driven Approaches to Gameplay DataДЎ2.5.1 Motivations for Deep Model-Based AnalysisДЎ2.5.5 Spatio-Temporal Segments of Interest IdentificationДЎ2.7.1 Case One: Inter-Occurrence ComparisonДЎ2.7.1.1 Observing Differences/Similarities among OccurrencesДЎ2.7.1.2 Reasoning About Potential Activities Behind an OccurrenceДЎ2.7.1.3 Statistical Analysis of an OccurrenceДЎ2.7.2 Case Two: Intra-Occurrence Pattern VerificationДЎ2.7.2.2 Verification of Comeback Time-PointsДЎ2.7.3.2 Visualization Design and InteractionsДata Analytics ApplicationsВ in Gaming andВ EntertainmentДata Analytics Applications Series Editor: Jay Liebowitz PUBLISHED Actionable Intelligence for Healthcare by Jay Liebowitz and Amanda Dawson ISBN: 978-1-4987-6665-4 Analytics and Knowledge Management by Suliman Hawamdeh and Hsia-Ching Chang ISBN 978-1-1386-3026-0 Big Data Analytics in Cybersecurity by Onur Savas and Julia Deng ISBN: 978-1-4987-7212-9 Big Data and Analytics Applications in Government: Current Practices and Future Opportunities by Gregory Richards ISBN: 978-1-4987-6434-6 Big Data in the Arts and Humanities: Theory and Practice by Giovanni Schiuma and Daniela Carlucci ISBN 978-1-4987-6585-5 Data Analytics Applications in Education by Jan Vanthienen and Kristoff De Witte ISBN: 978-1-4987-6927-3 Data Analytics Applications in Latin America and Emerging Economies by Eduardo Rodriguez ISBN: 978-1-4987-6276-2 Data Analytics for Smart Cities by Amir Alavi and William G.