{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T23:31:32Z","timestamp":1781134292728,"version":"3.54.1"},"reference-count":26,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European University of the Atlantic"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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, k-nearest 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\u2019s results could help improve coaching techniques and enhance batsmen\u2019s performance in cricket, ultimately improving the game\u2019s overall quality.<\/jats:p>","DOI":"10.3390\/s23156839","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T09:32:35Z","timestamp":1690882355000},"page":"6839","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0671-2060","authenticated-orcid":false,"given":"Hafeez Ur Rehman","family":"Siddiqui","sequence":"first","affiliation":[{"name":"Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6266-0600","authenticated-orcid":false,"given":"Faizan","family":"Younas","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8403-1047","authenticated-orcid":false,"given":"Furqan","family":"Rustam","sequence":"additional","affiliation":[{"name":"School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8747-5679","authenticated-orcid":false,"given":"Emmanuel Soriano","family":"Flores","sequence":"additional","affiliation":[{"name":"Engineering Research & Innovation Group, Universidad Europea del Atl\u00e1ntico, Isabel Torres 21, 39011 Santander, Spain"},{"name":"Department of Project Management, Universidad Internacional Iberoamericana Campeche, Campeche 24560, Mexico"},{"name":"Department of Projects, Universidad Internacional Iberoamericana Arecibo, Puerto Rico, PR 00613, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juli\u00e9n Brito","family":"Ballester","sequence":"additional","affiliation":[{"name":"Engineering Research & Innovation Group, Universidad Europea del Atl\u00e1ntico, Isabel Torres 21, 39011 Santander, Spain"},{"name":"Project Management, Universidade Internacional do Cuanza, Cuito EN250, Angola"},{"name":"Fundaci\u00f3n Universitaria Internacional de Colombia Bogot\u00e1, Bogot\u00e1 11001, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3134-7720","authenticated-orcid":false,"given":"Isabel de la Torre","family":"Diez","sequence":"additional","affiliation":[{"name":"Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, Paseo de Bel\u00e9n, 15, 47011 Valladolid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6431-5357","authenticated-orcid":false,"given":"Sandra","family":"Dudley","sequence":"additional","affiliation":[{"name":"Bioengineering Research Centre, School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-6496","authenticated-orcid":false,"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyongsan-si 38541, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103275","DOI":"10.1016\/j.cviu.2021.103275","article-title":"A review of 3D human pose estimation algorithms for markerless motion capture","volume":"212","author":"Desmarais","year":"2021","journal-title":"Comput. 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