Machine Learning in Sports - Identifying Potential Archers (Paperback, 1st ed. 2019)

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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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Product Description

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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Product Details

General

Imprint

Springer Verlag, Singapore

Country of origin

Singapore

Series

SpringerBriefs in Applied Sciences and Technology

Release date

September 2018

Availability

Expected to ship within 10 - 15 working days

First published

2019

Authors

, , ,

Dimensions

235 x 155mm (L x W)

Format

Paperback

Pages

45

Edition

1st ed. 2019

ISBN-13

978-981-13-2591-5

Barcode

9789811325915

Categories

LSN

981-13-2591-X



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