Computer Science in Sport: Modeling, Simulation, Data Analysis and Visualization of Sports-Related Data

✍ Scribed by Gunther Hellmann

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 247
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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
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
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
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
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
16: Logistic Regression
16.1 Example Sport
16.2 Background
16.3 Application
References
17: Time Series Data Mining
17.1 Example Sport
17.2 Background
17.3 Applications
17.3.1 Tasks in Time Series Data Mining
17.3.2 Time Series Data Mining in Medicine
17.3.3 Time Series Data Mining in Sports
References
18: Process Mining
18.1 Example Sport
18.2 Background
18.3 Application
18.3.1 Process Mining in Healthcare
18.3.2 Process Mining in Education
18.3.3 Process Mining in Soccer
References
19: Networks Centrality
19.1 A Network Science in Football
19.2 Background
19.3 Applications
References
20: Artificial Neural Networks
20.1 Example Sport
20.2 Background
20.3 Applications
Study Box
References
21: Deep Neural Networks
21.1 Example Sport
21.2 Background
21.3 Applications
Study Box
References
22: Convolutional Neural Networks
22.1 Example Sport
22.2 Background
22.3 Applications
Study Box
References
23: Transfer Learning
23.1 Example Sport
23.2 Background
23.3 Applications
References
24: Random Forest
24.1 Example Sport
24.2 Background
24.3 Applications
References
25: Statistical Learning for the Modeling of Soccer Matches
25.1 Example Sport
25.2 Background
25.3 Applications
References
26: Open-Set Recognition
26.1 Example Sport
26.2 Background
26.3 Applications
References
VII: Visualization
27: Visualization: Basics and Concepts
27.1 Example Sport
27.2 Background
27.3 Applications
Study Box
References
VIII: Outlook
28: Outlook
28.1 Trends
28.2 Sensors
28.3 Wearables und Intelligent Systems
28.4 Big Data and Cloud
28.5 Machine Learning and Computer Vision
28.6 Virtual und Augmented Reality and Robotics
28.7 Data Protection and Data Misuse
References
Appendix. Third-Party Funds Competitively Acquired by German Sports Scientists from the German Research Foundation (DFG) in the Review Board for Computer Science
Index