Computer Science in Sport Modeling, Simulation, Data Analysis and Visualization of Sports-Related Data /
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 nowad...
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| Fformat: | Electronig eLyfr |
| Iaith: | Saesneg |
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Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2024.
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| Rhifyn: | 1st ed. 2024. |
| Pynciau: | |
| Mynediad Ar-lein: | https://doi.org/10.1007/978-3-662-68313-2 |
| Tagiau: |
Ychwanegu Tag
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
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MARC
| LEADER | 00000nam a22000005i 4500 | ||
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| 001 | 978-3-662-68313-2 | ||
| 003 | DE-He213 | ||
| 005 | 20260905080458.0 | ||
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| 008 | 240305s2024 gw | o |||| 0|eng d | ||
| 020 | |a 9783662683132 |9 978-3-662-68313-2 | ||
| 024 | 7 | |a 10.1007/978-3-662-68313-2 |2 doi | |
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| 245 | 1 | 0 | |a Computer Science in Sport |h [electronic resource] : |b Modeling, Simulation, Data Analysis and Visualization of Sports-Related Data / |c edited by Daniel Memmert. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2024. | |
| 300 | |a 1 online resource (XVII, 249 p. 11 illus.). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
| 341 | 0 | |b Use of colour is Not sole means of conveying information |2 onix | |
| 341 | 0 | |b Use of high contrast between text and background colour |2 onix | |
| 341 | 0 | |b Next / Previous structural navigation |2 onix | |
| 341 | 0 | |b All Non-decorative content supports reading without sight |2 onix | |
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | |a 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. . | |
| 520 | |a 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". | ||
| 532 | 8 | |a Accessibility summary: This PDF does Not fully comply with PDF/UA standards but does include limited screen reader support, descriptions for Non-text content (such as images and graphs), bookmarks for easy navigation, and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognise the importance of accessibility and welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us via https://www.springernature.com/gp/info/accessibility | |
| 532 | 8 | |a Inaccessible, or known limited accessibility | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Sports sciences. | |
| 650 | 0 | |a Recreation |x Equipment and supplies. | |
| 650 | 0 | |a Artificial intelligence |x Data processing. | |
| 650 | 0 | |a Medical informatics. | |
| 650 | 0 | |a Quantitative research. | |
| 650 | 1 | 4 | |a Sport Science. |
| 650 | 2 | 4 | |a Sport Analytics. |
| 650 | 2 | 4 | |a Sport Technology. |
| 650 | 2 | 4 | |a Data Science. |
| 650 | 2 | 4 | |a Health Informatics. |
| 650 | 2 | 4 | |a Data Analysis and Big Data. |
| 700 | 1 | |a Memmert, Daniel. |e editor. |0 (orcid)0000-0002-3406-9175 |1 https://orcid.org/0000-0002-3406-9175 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783662683125 |
| 776 | 0 | 8 | |i Printed edition: |z 9783662683149 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-662-68313-2 |
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| 950 | |a Biomedical and Life Sciences (SpringerNature-11642) | ||
| 950 | |a Biomedical and Life Sciences (R0) (SpringerNature-43708) | ||