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9781492598534 Academic Inspection Copy

Applied Sport Business Analytics

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Text for upper-level undergraduate courses in sport management and sport business; reference for academic libraries and researchers in sport management and sport business analytics.
Christopher Atwater is an assistant professor of sport management in the School of Hospitality, Sport, and Tourism Management at Troy University. He teaches analytics courses at the graduate and undergraduate levels. Robert Baker is a professor and the director of the Center for Sport Management at George Mason University. He is a former president of the North American Society for Sport Management (NASSM), a founding board member of the World Association for Sport Management (WASM), and a founding commissioner of the Commission on Sport Management Accreditation (COSMA). He has conducted numerous presentations and produced articles on sport analytics. Ted Kwartler earned his MBA from Notre Dame. He is an instructor at Harvard University. He has conducted many presentations and written articles on sport analytics. He also authored a popular book entitled Text Mining.
Chapter 1. Foundations of Analytics for Sport Managers A Brief History of Analytics in Sport Evolution of Sport Analytics and the MIT Sloan Sports Analytics Conference Data and Decision-Making Systems and Analytics Emerging Applications of Sport Analytics Summary Online Activities References Chapter 2. Working With Quantitative Data in R R Basics Exploring Datasets Isolating Variables With Brackets, c(), and Operators Descriptive Statistics Inferential Statistics Summary Online Activities References Chapter 3. Plotting Data in R Base Plotting Structures in R Setting and Mapping Plot Elements Plotting Data With ggplot2() Map Plots Summary Online Activities References Chapter 4. Data-Driven Decision-Making Machine Learning Analysis: WNBA Players' Positions Analytics Application Esport Analytics Application European Football Analytics Application NFL Player Evaluations Analytics Application Comparative Analysis of Male and Female Prize Monies and Salaries Analytics Application Online Activities References Chapter 5. Natural Language Processing and Text Mining Language as Object Classes and Strings Basic Text Processing Workflow Identify Text Sources, Preprocessing, and Feature Extraction Analytics Insight and Recommendations Summary Online Activities References
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