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Full Description
Applied Sport Business Analytics With HKPropel Access provides a practical explanation of the use of data analytic metrics in sport, exploring selected techniques and tools as well as addressing fundamental applications of analytics within modern sports organizations. Current and aspiring sport managers will develop their understanding of how analytics can be used strategically to make data-informed decisions by selecting and translating data into evidence and meaningful metrics.
The text begins with an introduction to the world of analytics, exploring the social, economic, and business foundations that form the history of data analytics. Different strategies used to make data-driven decisions are discussed to demonstrate the importance of analytics in a modern sport context. The text explains terms and methods that are typical in sport analytics, bridging the gap between sport managers and sport analysts to help them understand the perceptions and needs of one another.
The text's focus on quantitative statistical analysis-with its exploration of modeling, predictive analytics, and forecasting-helps students learn how to analyze data and make use of it. Students will then learn to turn data into visual representations such as cluster diagrams to reveal clear results. With practical exercises that utilize five included datasets and are heavily support by related video tutorials delivered through HKPropel, even those without programming experience will learn how to program and transform complex statistical data into easy-to-understand visuals.
Case studies exploring real-world scenarios-including player position analysis in women's professional basketball, esport player popularity and market analysis, and prospective player evaluation for the NFL draft-examine managerial implications to help develop understanding of what questions to ask, how to interpret data, and how to use data to make informed decisions. Finally, an in-depth look at how cutting-edge analytics mechanisms were used to analyze over one million tweets associated with the NBA over an entire season will illustrate how to successfully work with large amounts of data to achieve results.
Concepts throughout the book are made easy to understand through exercises, datasets, and video lectures on key topics, all accessible through HKPropel. These tools combine to provide valuable experience and practical understanding. Interview With a Professional sidebars offer additional real-world glimpses into the use of analytics by practitioners in sport business.
Applied Sport Business Analytics will provide a broader and deeper knowledge of the use of sport analytics for aspiring sport managers, data analysts, and practitioners alike. It will prepare them to translate metrics in a useful way that allows them to make data-informed and data-driven decisions to achieve desired outcomes in their organization.
Note: A code for accessing HKPropel is included with this ebook.
Contents
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
