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Applied Sport Business Analytics

Applied Sport Business Analytics

9781492598534
585,90 zł
556,60 zł Zniżka 29,30 zł Brutto
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Opis
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 texts 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 womens 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 all new print books.
Szczegóły produktu
Human Kinetics
100790
9781492598534
9781492598534

Opis

Rok wydania
2022
Numer wydania
1
Oprawa
miękka foliowana
Liczba stron
216
Waga (g)
726
  • 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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