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Recent computer vision and machine learning enable prediction of cricket strokes from video. This study proposes a computer-vision and ML pipeline to predict eight batting strokes—pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. MediaPipe extracts video features, followed by ML and deep learning models including random forest (RF), SVM, k-nearest neighbors, decision tree, linear regression, and LSTM. RF attains 99.77% accuracy and 95.0% k-fold validation with low deviation, supporting improved coaching and performance.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/enhancing-cricket-performance-analysis-with-human-pose-estimation-and-machine-learning/290628/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/enhancing-cricket-performance-analysis-with-human-pose-estimation-and-machine-learning/290628.png","ImageObject",300,407,{"name":92,"@type":93},"Ezra","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What strokes are predicted in the study?","Question",{"text":112,"@type":113},"The study predicts eight batting strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are video features extracted?",{"text":117,"@type":113},"The approach uses the MediaPipe library to extract features from cricket videos before applying machine learning and deep learning models.",{"name":119,"@type":110,"acceptedAnswer":120},"Which algorithm achieved the best performance and what accuracy was reported?",{"text":121,"@type":113},"Random forest (RF) achieved the highest accuracy of 99.77% and 95.0% k-fold validation with a standard deviation of 0.07.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290628,1789642232,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":36,"preview_page_count":4},1099514068035,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","sensors   \nArticle  \nEnhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning  \nHafeez Ur Rehman Siddiqui 1, Faizan Younas 1, Furqan Rustam 2, Emmanuel Soriano Flores 3,4,5, Julién Brito Ballester 3,6,7, Isabel de la Torre Diez 8, *, Sandra Dudley 9 and Imran Ashraf 10, *  \nCitation: Siddiqui, H.U.R.; Younas, F.; Rustam, F.; Flores, E.S.; Ballester, J.B.; Diez, I.d.l.T.; Dudley, S.; Ashraf, I. Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning. Sensors 2023, 23, 6839. [https://](https://)[ ](https://)[doi.org/10.3390/s23156839](doi.org/10.3390/s23156839)  \nAcademic Editor: Kah Phooi Seng  \nReceived: 13 June 2023  \nRevised: 21 July 2023  \nAccepted: 29 July 2023  \nPublished: 1 August 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan; [hafeez@kfueit.edu.pk](hafeez@kfueit.edu.pk) (H.U.R.S.); [younasfaizan97@gmail.com](younasfaizan97@gmail.com) (F.Y.)  \n2 School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland; [furqan.rustam1@gmail.com](furqan.rustam1@gmail.com)  \n3 Engineering Research & Innovation Group, Universidad Europea del Atlántico, Isabel Torres 21,  \n39011 Santander, Spain; [emmanuel.soriano@uneatlantico.es](emmanuel.soriano@uneatlantico.es) (E.S.F.); [julien.brito@uneatlantico.es](julien.brito@uneatlantico.es) (J.B.B.)  \n4 Department of Project Management, Universidad Internacional Iberoamericana Campeche, Campeche 24560, Mexico  \n5 Department of Projects, Universidad Internacional Iberoamericana Arecibo, Puerto Rico, PR 00613, USA  \n6 Project Management, Universidade Internacional do Cuanza, Cuito EN250, Angola  \n7 Fundación Universitaria Internacional de Colombia Bogotá, Bogotá 11001, Colombia  \n8 Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain  \n9 Bioengineering Research Centre, School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK; [dudleyms@lsbu.ac.uk](dudleyms@lsbu.ac.uk)  \n10 Department of Information and Communication Engineering, Yeungnam University, Gyongsan-si 38541, Republic of Korea  \n* Correspondence: [isator@tel.uva.es](isator@tel.uva.es) (I.d.l.T.D.); [imranashraf@ynu.ac.kr](imranashraf@ynu.ac.kr) (I.A.)  \nAbstract: Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes eight strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. The study uses the MediaPipe library to extract features from videos and several machine learning and deep learning algorithms, including random forest (RF), support vector machine, knearest neighbors, decision tree, linear regression, and long short-term memory to predict the strokes. The study achieves an outstanding accuracy of 99.77% using the RF algorithm, outperforming the other algorithms used in the study. The k-fold validation of the RF model is 95.0% with a standard deviation of 0.07, highlighting the potential of computer vision and machine learning techniques for predicting batsman strokes in cricket. The study'","cbCaie8Ko04rsVxc","https://ap.wps.com/l/cbCaie8Ko04rsVxc","pdf",23514062,16,"English","# Introduction\n## Human pose estimation in sports analytics\n## Cricket stroke prediction approach\n## Methods and models","[{\"question\":\"What strokes are predicted in the study?\",\"answer\":\"The study predicts eight batting strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep.\"},{\"question\":\"How are video features extracted?\",\"answer\":\"The approach uses the MediaPipe library to extract features from cricket videos before applying machine learning and deep learning models.\"},{\"question\":\"Which algorithm achieved the best performance and what accuracy was reported?\",\"answer\":\"Random forest (RF) achieved the highest accuracy of 99.77% and 95.0% k-fold validation with a standard deviation of 0.07.\"}]","Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning | PDF"]