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Data Mining and Machine Learning in Sports

Overview of attention for book
Cover of 'Data Mining and Machine Learning in Sports'

Table of Contents

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    Book Overview
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    Chapter 1 Recent Advancements in Data Mining and Machine Learning Applications in Evaluating Goalkeepers’ Performances in Elite Football
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    Chapter 2 The Influence of Anthropometrics on Goalkeepers’ Penalty-Saving Performance in Elite European Football
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    Chapter 3 The Relationship Between Anthropometrics Parameters and Clean Sheets of Goalkeepers in Elite European Football
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    Chapter 4 A Machine Learning Analysis of Technical Skills and Tactical Awareness as Performance Predictors for Goalkeepers in European Football League
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    Chapter 5 An Unsupervised Analysis of Identifying Key Technical and Tactical Skills Awareness Among Top European Goalkeepers
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    Chapter 6 Identification of Dominant Types of Shots Received and Saves Among Top European Goalkeepers: A Significant Attribute Evaluation Technique
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    Chapter 7 Evaluation of Goalkeepers’ Goals Conceptions from Different Saves and Shots Indicators
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    Chapter 8 Summary, Conclusion, Current Status, and Future Direction for Goalkeeper’s Performance in European Football Leagues
Attention for Chapter 6: Identification of Dominant Types of Shots Received and Saves Among Top European Goalkeepers: A Significant Attribute Evaluation Technique
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Chapter title
Identification of Dominant Types of Shots Received and Saves Among Top European Goalkeepers: A Significant Attribute Evaluation Technique
Chapter number 6
Book title
Data Mining and Machine Learning in Sports
Published in
SpringerBriefs in Applied Sciences and Technology, January 2024
DOI 10.1007/978-981-99-7762-8_6
Book ISBNs
978-9-81-997761-1, 978-9-81-997762-8
Authors

Musa, Rabiu Muazu, Majeed, Anwar P. P. Abdul, Ab Rasid, Aina Munirah, Abdullah, Mohamad Razali

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 100%
Readers by discipline Count As %
Engineering 1 100%

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