This book provides an overview of current research in the fascinating, interdisciplinary field of computer science and sports. It includes papers from the 11th International Symposium on Computer Science in Sport (IACSS 2017), which took place in Constance, Germany, on September 6-9, 2017. The papers represent the state of the art in utilizing the latest developments in computer science to support coaches and athletes. The book covers a broad range of topics, reflecting the diversity of the field. It presents three categories of papers: those on concepts in informatics like modeling, virtual reality, simulation; those describing applications of computer science in sports like running, volleyball, water polo, and football; and contributions discussing the impact of computer science in sports federations and universities.
Invited lectures.- An Empirical Analysis on European Odds of English Premier League.- Study on the "Hot Match" Effect in Professional Football Leagues.- Artificial Neural Networks Predicting the Outcome of a Throwing Task - Effects of input quantity and quality.- Activity Recognition of Local Muscular Endurance (LME) Exercises using an Inertial Sensor.- Gait stability during shod and barefoot walking and running on a treadmill assessed by correlation entropy.- Statistical Models for Predicting short-term HR Responses to Submaximal interval exercise.- Information Systems for Top-Level Football with Focus on Performance Analysis and Healthy Reference Patterns.- Development of a Real-Time Analysis System for Monitoring Playing Time of Water Polo Players.- Reconstruction of 3D ball/shuttle position by two image points from a Single view.- A Comparison of Smoothing and Filtering Approaches Using Simulated Kinematic Data of Human Movements.- Missing Depth Cues in Virtual Reality Decrease Performance of Three-Dimensional Reaching Movements.- Students' use of and attitudes towards information and communication technologies in sport education - Cross-sectional surveys over the past 15 years.- A novel multilocus genetic model can predict Muscle fibers Composition.