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Machine Learning in Sports : Identifying Potential Archers

Author: Rabiu Muazu Musa; Zahari Taha; Maje Anwar P. P.Abdul.; Mohamad Razali Abdullah
Publisher: Singapore : Springer Nature : Springer, 2018.
Series: SpringerBriefs in applied sciences and technology.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This brief highlights the association of different performance variables that influences archery performance and the employment of different machine learning algorithms in the identification of potential archers. The sport of archery is often associated with a myriad of performance indicators namely bio-physiological, psychological, anthropometric as well as physical fitness. Traditionally, the determination of  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Musa, Rabiu Muazu.
Machine Learning in Sports.
Singapore : Springer Nature : Springer, 2018
(OCoLC)1048938837
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Rabiu Muazu Musa; Zahari Taha; Maje Anwar P. P.Abdul.; Mohamad Razali Abdullah
ISBN: 9789811325922 9811325928
OCLC Number: 1053888139
Description: 1 online resource
Contents: Intro; Acknowledgements; Contents; About the Authors; 1 Introduction; 1.1 Nature ofArchery; 1.2 Performance Parameters Influencing Archery Performance; 1.3 Machine Learning inSporting Activities; 1.4 K-Means Clustering; 1.5 Classification Algorithms; 1.5.1 K-Nearest Neighbour (K-NN); 1.5.2 Support Vector Machine (SVM); 1.5.3 Artificial Neural Network (ANN); 1.5.4 Logistic Regression (LR); 1.5.5 Model Performance Evaluation; 1.6 Study Participants; 1.7 Archery Shooting Test; References; 2 Bio-Physiological Indicators inEvaluating Archery Performance; 2.1 Overview; 2.2 Clustering 2.3 Classification2.4 Results andDiscussion; 2.5 Summary; References; 3 Psychological Variables inAscertaining Potential Archers; 3.1 Overview; 3.2 Clustering; 3.3 Classification; 3.4 Results andDiscussion; 3.5 Summary; References; 4 Anthropometry Correlation Towards Archery Performance; 4.1 Overview; 4.2 Clustering; 4.3 Classification; 4.4 Results andDiscussion; 4.5 Summary; References; 5 Psycho-Fitness Parameters intheIdentification ofHigh-Potential Archers; 5.1 Overview; 5.2 Clustering; 5.3 Classification; 5.4 Results andDiscussion; 5.5 Summary; References; 6 Concluding Remarks
Series Title: SpringerBriefs in applied sciences and technology.
Responsibility: Rabiu Muazu Musa [and 3 others].

Abstract:

This brief highlights the association of different performance variables that influences archery performance and the employment of different machine learning algorithms in the identification of  Read more...

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