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

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