Machine Learning in Sports

Identifying Potential Archers

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing, Sports
Cover of the book Machine Learning in Sports by Rabiu Muazu Musa, Zahari Taha, Anwar P.P.Abdul Majeed, Mohamad Razali Abdullah, Springer Singapore
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Author: Rabiu Muazu Musa, Zahari Taha, Anwar P.P.Abdul Majeed, Mohamad Razali Abdullah ISBN: 9789811325922
Publisher: Springer Singapore Publication: September 18, 2018
Imprint: Springer Language: English
Author: Rabiu Muazu Musa, Zahari Taha, Anwar P.P.Abdul Majeed, Mohamad Razali Abdullah
ISBN: 9789811325922
Publisher: Springer Singapore
Publication: September 18, 2018
Imprint: Springer
Language: English

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 potential archers is carried out by means of conventional statistical techniques. Nonetheless, such methods often fall short in associating non-linear relationships between the variables. This book explores the notion of machine learning that is capable of mitigating the aforesaid issue. This book is valuable for coaches and managers in identifying potential archers during talent identification programs.​

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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 potential archers is carried out by means of conventional statistical techniques. Nonetheless, such methods often fall short in associating non-linear relationships between the variables. This book explores the notion of machine learning that is capable of mitigating the aforesaid issue. This book is valuable for coaches and managers in identifying potential archers during talent identification programs.​

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