Match Analysis How to Use Data in Professional Sport 1st Edition by Daniel Memmert ISBN 9780367750947 0367750945

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Product details:

ISBN 10: 0367750945
ISBN 13: 9780367750947
Author: Daniel Memmert

Match Analysis How to Use Data in Professional Sport 1st Edition Table of contents:

Part I Introduction

1 Match Analysis in 2020

1.1 Application Examples

1.2 Career Opportunities in Match Analysis

1.3 Outline of the Book

1.4 Conclusion

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2 History of Match Analysis

2.1 Steps of Development

2.1.1 From the Beginnings of Organized Football, 1863 to 1920

2.1.2 1920 – Trainer Development – First Scientific Approaches

2.1.3 Scientific Development

2.1.4 Developments in Football – First Pioneers

2.1.5 Match Analysis in Germany

2.1.6 German National Football Team as “Trendsetter”

2.2 Creation of a New Profession

2.2.1 Position Data – “Big Data”

2.2.2 Development in Recent Years

2.3 Practical Relevance

2.4 Conclusion

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3 Match Analysis in Practice: Football

3.1 Theoretical Background

3.2 Methods

3.3 Results and Discussion

3.4 Practical Relevance

3.5 Conclusion

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4 Match Analysis in Practice: Beach Volleyball

4.1 Theoretical Background

4.1.1 Scientific Match Analysis versus Practical Match Analysis

4.1.2 Player Profiles

4.2 Methodology of Qualitative Match Analysis

4.2.1 Work Steps

4.2.2 Observation System

4.2.3 Software Support

4.3 Practical Relevance

4.4 Conclusion

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Part II Match Analysis on the Basis of Video Data

5 Match Analysis in American Football

5.1 Theoretical Background

5.2 Methods

5.3 Results and Discussion

5.4 Practical Relevance

5.5 Conclusion

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6 Match Analysis in Basketball

6.1 Theoretical Background

6.2 Results and Discussion

6.3 Practical Relevance

6.4 Conclusion

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7 Match Analysis in Cricket

7.1 Theoretical Background

7.1.1 How Is Cricket Played?

7.1.2 Cricket Formats – The Context of Analysis

7.2 Methods

7.2.1 Performance Analysis in Cricket

7.2.2 Descriptive Statistics in Broadcast Data

7.2.3 Notational Analysis and Pro-Analyst Data

7.2.4 Translating Notations into Tactics

7.3 Discussion

7.3.1 Application Insights

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8 Match Analysis in Field Hockey

8.1 Introduction

8.2 Theoretical Background

8.3 Methods

8.3.1 Match Observation and Data Recording

8.3.2 Data Evaluation and Data Analysis

8.4 Scientific Results and Discussion

8.5 Conclusion

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9 Opponent Analysis in Football

9.1 Theoretical Background

9.2 Methods

9.3 Practical Relevance

9.3.1 Presentation Media Research

9.3.2 Presentation Team Analysis

9.3.3 Presentation Single Player Analysis

9.3.4 Presentation of Set Piece Analysis

9.3.5 Presentation of the Analysis of Tactical Formations

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10 Visual Scanning in Football

10.1 Theoretical Background

10.2 Methods

10.3 Results and Discussion

10.4 Practical Relevance

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11 Match Analysis in Ice Hockey

11.1 Theoretical Background

11.2 Criteria in Professional Ice Hockey

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12 Match Analysis in Rugby

12.1 Theoretical Background

12.2 Methods

12.3 Results and Discussion

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13 Match Analysis in Squash

13.1 Theoretical Background

13.2 Performance Architecture

13.3 Methods

13.4 Practical Tips

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14 Match Analysis in Table Tennis

14.1 Theoretical Background

14.2 Fundamental Theoretical Forms of Systematic Observation of Table Tennis Matches

14.3 Application-Related Forms of Systematic Observation of Table Tennis Matches

14.4 Practical Relevance

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15 Match Analysis in Team Handball

15.1 Theoretical Background

15.2 Methods of Match Analysis in Handball

15.3 Practical Relevance

15.4 Conclusion

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16 Match Analysis in Tennis

16.1 Theoretical Background

16.2 Methods

16.3 Results and Discussion

16.4 Practical Relevance

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17 Match Analysis in Volleyball

17.1 Theoretical Background

17.2 Methods

17.3 Results and Discussion

17.4 Practical Relevance

17.5 Conclusion

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Part III Match Analysis on the Basis of Event Data

18 KPIs on the Basis of Match Events Data

18.1 Theoretical Background

18.2 Methods

18.2.1 Match Events Data Management

18.2.2 Match Events Data Preprocessing

18.3 Results and Discussion

18.3.1 Analyzing the Distribution of Actions in a Match by Space and Time

18.3.2 Valuing Match Actions

18.4 Practical Relevance

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19 Scouting

19.1 Theoretical Background

19.2 Methods

19.2.1 Subsection 1: Creating an Event Data-Based Database

19.2.1.1 Influence of the Game Philosophy

19.2.1.2 Influence of Different Nations and Leagues

19.2.1.3 Merging of Requirement Profiles and Factorization

19.2.2 Subsection 2: Extension of the Database

19.2.2.1 Qualitative Extension

19.2.2.2 EVP Model

19.2.2.3 Merging of EVP Rating and Requirement Profiles

19.2.3 Subsection 2: Using Database

19.2.3.1 Unknown Opinion

19.2.3.2 Known Options

19.3 Practical Relevance

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20 Normalizing KPIs Based on Possession

20.1 Theoretical Background

20.2 Methods

20.2.1 Normalization

20.2.2 Multiple Linear Regression

20.3 Results and Discussion

20.4 Practical Relevance

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Part IV Match Analysis on the Basis of Position Data

21 Model-Based Performance Analysis in Football

21.1 Theoretical Background: Modeling

21.2 Methods

21.2.1 Data Acquisition

21.2.2 Simulation

21.2.3 Analysis and Forecast

21.3 Results and Discussion

21.3.1 Space Control

21.3.2 Ball Control

21.3.3 Attack Success

21.4 Practical Implication

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22 Collective tactical behaviours in football

22.1 Theoretical Background

22.2 Measures of Tactical Behaviours

22.2.1 Position

22.2.2 Dispersion

22.2.3 Distances

22.2.4 Nonlinear Analysis

22.3 Tactical Behaviours in Small-Sided Games

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23 KPIs in the German Bundesliga

23.1 Theoretical Background

23.2 Methods

23.3 Results and Discussion

23.4 Practical Relevance

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24 Collective Behavior in Football

24.1 Theoretical Background

24.2 Methods

24.2.1 Highly Correlated Segments Calculation

24.3 Results and Discussion

24.4 Practical Relevance

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Note

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25 Applying Machine Learning in Football The Identification of Counterpressing in Football

25.1 Theoretical Background

25.2 Methods

25.3 Results and Discussion

25.4 Practical Relevance

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26 Physical KPIs

26.1 Theoretical Background

26.2 Methods

26.3 Results and Discussion

26.4 Practical Relevance

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Part V Outlook

27 Communication of Match Analysis

27.1 Theoretical Background

27.1.1 Drawing Conclusions Based on Data

27.1.2 “Too Much of a Good Thing”

27.2 Communication of Match Analysis Data

27.2.1 How Much Information Should Players Be Given Before a Match?

27.3 Methods, Results and Discussion

27.4 Practical Relevance

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28 Limits of Match Analysis

28.1 Theoretical Background

28.1.1 Dealing with Random Events

28.1.2 Overconfidence

28.1.3 Influence of Internal and External Expectations

28.1.4 Consequences

28.2 Methods, Results and Discussion

28.2.1 Data-Based Verification with Scientific Methods

28.2.2 Quantification of the Influence of Randomness

28.3 Practical Relevance

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29 Match Analysis in 2030

29.1 Theoretical Background

29.1.1 Position Data

29.1.2 Contextualization of Game Actions

29.1.3 Automated Analyses

29.1.4 Theoretical Models

29.2 Practical Relevance

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Index

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Tags: Daniel Memmert, Match Analysis, Use Data, Professional Sport