Computer Science in Sport : Modeling, Simulation, Data Analysis and Visualization of Sports-Related Data / edited by Daniel Memmert.

Format:

Book

Contributor:

Memmert, Daniel, editor.

Language:

English

Subjects (All):

Sports sciences.

Recreation--Equipment and supplies.

Recreation.

Artificial intelligence--Data processing.

Artificial intelligence.

Medical informatics.

Quantitative research.

Sport Science.

Sport Analytics.

Sport Technology.

Data Science.

Health Informatics.

Data Analysis and Big Data.

Local Subjects:

Sport Science.

Sport Analytics.

Sport Technology.

Data Science.

Health Informatics.

Data Analysis and Big Data.

Physical Description:

1 online resource (247 pages)

Edition:

1st ed. 2024.

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2024.

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".

Contents:

I HISTORY

History

II DATA

Artificial data

Text data

Video data

Event data

Position data

Online data

III MODELING

Modeling

Predictive models

Physiological modeling

IV SIMULATION

Simulation

Metabolic simulation

Simulation of physiological adaptation processes

V PROGRAMMING LANGUAGES

An introduction to the programming language R for beginners

Phyton

VI DATA ANALYSIS

Logistic Regression

Time Series Data Mining

Process Mining

Networks Centrality

Artificial Neural Networks

Deep Neural Networks

Convolutional Neural Networks

Transfer Learning

Random Forest

Statistical learning for the modeling of soccer matches

Open-Set Recognition

VII VISUALIZATION

Visualization – Basics and Concepts

VIII OUTLOOK

Outlook. .

Notes:

Includes bibliographical references and index.

ISBN:

9783662683132

366268313X