| Description |
1 online resource (xvii, 249 pages) : illustrations |
| Contents |
Intro -- Prologue -- Contents -- Contributors -- I: History -- 1: History -- 1.1 Introduction -- 1.2 The Institutional Constitution of Sports Informatics -- 1.2.1 The Pre-institutional Phase (Before 1995) -- 1.2.2 The Phase of the dvs Section Sports Informatics (1995-2003) -- 1.2.3 The Phase of IACSS (2003-2019) -- 1.2.4 The Institutional Integration Phase of Informatics Working Groups (from 2019) -- References -- II: Data -- 2: Artificial Data -- 2.1 Example Sport -- 2.2 Background -- 2.2.1 Limits of Real-World Data -- 2.2.2 The Idea of Artificial Data |
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2.2.3 Random Numbers and Monte Carlo Simulation -- 2.2.4 Advantages and Disadvantages of Artificial Data Sets -- 2.3 Applications -- References -- 3: Text Data -- 3.1 Introduction -- 3.2 Applications -- 3.2.1 Evaluation of Technological Officiating Aids -- 3.2.2 Match Predictions -- 3.2.3 Talent Scouting -- References -- 4: Video Data -- 4.1 Example Sport -- 4.2 Background -- 4.3 Basics and Definition -- 4.4 Applications -- References -- 5: Event Data -- 5.1 Example Sport -- 5.2 Background -- 5.3 Application -- 5.3.1 Event Data to Extend Box Score Statistics |
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5.3.2 Event Data to Value in-Game Actions and Player Impact -- 5.3.3 Event Data to Understand Player Interactions -- References -- 6: Position Data -- 6.1 Example Sport -- 6.2 Background -- 6.3 Applications -- References -- 7: Online Data -- 7.1 Example Sport -- 7.2 Background -- 7.3 Application -- References -- III: Modeling -- 8: Modeling -- 8.1 Example Sport -- 8.2 Background -- 8.3 Application -- References -- 9: Predictive Models -- 9.1 Example Sport -- 9.2 Background -- 9.2.1 Looking into the Future -- 9.2.2 Predictive Models in Sports -- 9.2.3 Creation of Predictive Models -- Step 1: Goal |
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Step 2: Data -- Step 3: Methodological Approach -- Step 4: Evaluation of Predictive Quality -- 9.2.4 Exemplary Methods -- Model 1: Statistical Model to Forecast Soccer Results (Hvattum & Arntzen, 2010) -- Model 2: Computer Science Model for Forecasting Horse Racing (Lessmann et al., 2010) -- 9.3 Applications -- References -- 10: Physiological Modeling -- 10.1 Example Sport -- 10.2 Background -- 10.3 Applications -- References -- IV: Simulation -- 11: Simulation -- 11.1 Example Sport -- 11.2 Background -- 11.3 Applications -- References -- 12: Metabolic Simulation -- 12.1 Example Sport |
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12.2 Background -- 12.3 Applications -- References -- 13: Simulation of Physiological Adaptation Processes -- 13.1 Example Sport -- 13.2 Background -- 13.3 Applications -- References -- V: Programming Languages -- 14: An Introduction to the Programming Language R for Beginners -- 14.1 History and Philosophy -- 14.2 Concept and Programming Paradigms -- 14.3 Resources on R -- 14.4 R Community and Packages -- 14.5 Introduction to Working with R -- 14.6 An Example Workflow in R -- References -- 15: Python -- 15.1 Example Sport -- 15.2 Background -- 15.3 Applications -- References -- VI: Data Analysis |
| Summary |
In recent years, computer science in sport has grown extremely, mainly because more and more new data has become available. Computer science tools in sports, whether used for opponent preparation, competition, or scientific analysis, have become indispensable across various levels of expertise nowadays. A completely new market has emerged through the utilization of these tools in the four major fields of application: clubs and associations, business, science, and the media. This market is progressively gaining importance within university research and educational activities. This textbook aims to live up to the now broad diversity of computer science in sport by having more than 30 authors report from their special field and concisely summarise the latest findings. The book is divided into four main sections: data sets, modelling, simulation and data analysis. In addition to background information on programming languages and visualisation, the textbook is framed by history and an outlook. Students with a connection to sports science are given a comprehensive insight into computer science in sport, supported by a didactically sophisticated concept that makes it easy to convey the learning content. Numerous questions for self-testing underpin the learning effect and ensure optimal exam preparation. For advanced students, the in-depth discussion of time series data mining, artificial neural networks, convolution kernels, transfer learning and random forests offers additional value. The Editor Prof. Dr Daniel Memmert is the executive director and professor at the Institute of Exercise Training and Sport Informatics at the German Sport University Cologne. He is the editor and author of numerous textbooks with a focus on exercise science, sports psychology and informatics. His institute organises two certificate programmes (Game Analysis Team Cologne / Sports Director in Youth and Amateur Soccer) as well as the first international Master's degree programme "Match Analysis" |
| Notes |
Description based upon print version of record |
| Subject |
Sports -- Research
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Sports -- Computer simulation
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| Genre/Form |
Electronic books
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| Form |
Electronic book
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| Author |
Memmert, Daniel.
|
| ISBN |
9783662683132 |
|
366268313X |
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