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Machine Learning and Data Mining for Sports Analytics

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

Table of Contents

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    Book Overview
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    Chapter 1 Measuring Football Players’ On-the-Ball Contributions from Passes During Games
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    Chapter 2 Forecasting the FIFA World Cup – Combining Result- and Goal-Based Team Ability Parameters
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    Chapter 3 Distinguishing Between Roles of Football Players in Play-by-Play Match Event Data
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    Chapter 4 Player Valuation in European Football
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    Chapter 5 Ranking the Teams in European Football Leagues with Agony
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    Chapter 6 Interpreting Deep Sports Analytics: Valuing Actions and Players in the NHL
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    Chapter 7 Player Pairs Valuation in Ice Hockey
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    Chapter 8 Model Trees for Identifying Exceptional Players in the NHL and NBA Drafts
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    Chapter 9 Evaluating NFL Plays: Expected Points Adjusted for Schedule
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    Chapter 10 Real-Time Power Performance Prediction in Tour de France
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    Chapter 11 Automatic Classification of Strike Techniques Using Limb Trajectory Data
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    Chapter 12 Predicting Pass Receiver in Football Using Distance Based Features
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    Chapter 13 Football Pass Prediction Using Player Locations
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    Chapter 14 Deep Learning from Spatial Relations for Soccer Pass Prediction
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    Chapter 15 Predicting the Receivers of Football Passes
Attention for Chapter 11: Automatic Classification of Strike Techniques Using Limb Trajectory Data
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