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Machine Learning in Team Sports: Performance Analysis and Talent Identification in Beach Soccer & Sepak-takraw

Machine Learning in Team Sports: Performance Analysis and Talent Identification in Beach Soccer & Sepak-takraw

Anwar P.P. Abdul Majeed
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This brief highlights the application of performance analysis tools in data acquisition, and various machine learning algorithms for evaluating team performance as well as talent identification in beach soccer and sepak takraw. Numerous performance indicators and human performance parameters are considered based on their relevance to each sport. The findings presented here demonstrate that the key performance indicators as well as human performance parameters can be used in the future evaluation of team performance as well as talent identification in these sports. Accordingly, they offer a valuable resource for coaches, club managers, talent identification experts, performance analysts and other relevant stakeholders involved in performance assessments. 
Language
English
Pages
61
Format
Kindle Edition
Release
February 17, 2020

Machine Learning in Team Sports: Performance Analysis and Talent Identification in Beach Soccer & Sepak-takraw

Anwar P.P. Abdul Majeed
0/5 ( ratings)
Read Download
This brief highlights the application of performance analysis tools in data acquisition, and various machine learning algorithms for evaluating team performance as well as talent identification in beach soccer and sepak takraw. Numerous performance indicators and human performance parameters are considered based on their relevance to each sport. The findings presented here demonstrate that the key performance indicators as well as human performance parameters can be used in the future evaluation of team performance as well as talent identification in these sports. Accordingly, they offer a valuable resource for coaches, club managers, talent identification experts, performance analysts and other relevant stakeholders involved in performance assessments. 
Language
English
Pages
61
Format
Kindle Edition
Release
February 17, 2020

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