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Novel experimental protocol to capture movement data and predict shot execution in cricket batting

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conference contribution
posted on 12.03.2020 by Pubudu Dias, Sean Mitchell, Andy Harland
Shot execution in cricket batting is reliant on intricate movement patterns of crucial body segments. When there is a substantial amount of batting movement data available, supervised machine learning can be used to classify when a batting shot execution takes place in a cricket batting cycle. An automated approach to identify and assess cricket batting could be useful for the applications including performance evaluation, talent identification and injury prevention. Current evaluation of movements and shot execution are generally undertaken in an artificial environment with camera-based, motion tracking systems to collect batting movement data, which require careful preparation, data collection and post-processing, and risk changing the natural gameplay of a batsman. By training a model based on data obtained from a close representation of a cricket batting innings, supervised machine learning was found to be capable of reliably predicting cricket batting shot execution.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Proceedings 2020

Volume

49

Issue

1

Source

13th Conference of the International Sports Engineering Association

Publisher

MDPI

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Acceptance date

02/03/2020

Publication date

2020-06-15

Copyright date

2020

Notes

The 13th conference on the Engineering of Sport on behalf of the International Sports Engineering Association (ISEA) took place online due to COVID-19.

eISSN

2504-3900

Language

en

Location

Tokyo, Japan [Online]

Event dates

22nd June 2020 - 26th June 2020

Depositor

Mr Pubudu Dias. Deposit date: 11 March 2020

Article number

41

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