[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118517-en":3,"doc-seo-118517-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118517,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Simulation for Cricket - A Machine Learning Approach","Cricket holds major popularity worldwide, yet simulation research remains limited due to the sport’s dynamic gameplay, complex interactions, and the scarcity of high-quality data. This work develops a cricket simulation mechanism powered by machine learning, leveraging a detailed dataset from Cricket Australia. A model predicts the outcome of each “delivery,” enabling scorecard generation and match simulation. The simulator is further used to identify optimal batting positions in Twenty20 cricket, and an interactive web platform is provided for end-user exploration.","Simulation for Cricket: A Machine Learning Approach  \nLasith Chamindu Pranath Pussella  \nDepartment of Mathematics and Statistics  \nSubmitted in partial fulfillment of the requirements for the degree of  \nMaster of Science  \nFaculty of Mathematics and Science, Brock University St. Catharines, Ontario  \n© Lasith Chamindu Pranath Pussella 2024  \nDedication  \nDedicated to my wife, my parents, my brother and my sister. . .  \nAbstract  \nCricket is the second most popular sport in the world with a significant presence in Commonwealth countries. Despite its popularity, cricket is underrepresented in the literature, especially in the domain of simulation. Simulation in cricket is challenging because of its complexity, dynamic nature, and data scarcity. In this research, we develop a simulation mechanism for cricket using machine learning techniques. The construction of the simulator is based on the availability of a detailed dataset from Cricket Australia. We employ machine learning to predict the outcome of a “delivery”, the core element of gameplay, which can further be utilized for scorecard generation and match simulations. Our simulator’s potential is demonstrated by employing it to determine the optimal batting position of a given batter ina team in Twenty20 cricket. Additionally, we develop an interactive web platform to enable the end users to directly interact with the simulator.  \nKeywords: Cricket; machine learning; simulation; random forests; neural networks; T20 cricket;  \nAcknowledgements  \nFirst and foremost, I would like to express my deepest appreciation to my supervisor Dr. Tianyu Guan for her continuous support, guidance, and encouragement over the past 3 years. Her confidence in my abilities has been a constant source of motivation, and her support made it possible for me to pursue the MSc program at Brock University while researching on a topic that I am really interested in. Thank you, Dr. Tianyu.  \nI’m extremely grateful to Robert Nguyen and Cricket Australia for extending their support in sourcing a highly detailed dataset to continue this research. We were able to accomplish improved results, thanks to the quality of their dataset. Special thanks to CANSSI CRT: Sports Analytics for the support given through funding to continue our research.  \nI would also like to extend my sincere thanks to the thesis examining committee members, Dr. Tianyu Guan, Dr. Ejaz Ahmed, Dr. William Marshall, and Dr. Taylor McKee. I must also thank the Brock University for providing an excellent platform to study and conduct research.  \nI am extremely grateful to Dr. Rajitha M. Silva from the University of Sri Jayewardenepura for supervising me during my undergraduate years and encouraging my passion for cricket research. His guidance paved the way for me to begin my Master’s program at Brock University.  \nI am deeply grateful to my wife, Madhushika Fernando for her unconditional love, patience, and support. To my parents, Cyril Pussella and Lalitha Gamage, thank you for always believing in me and encouraging me to pursue my dreams. I am also grateful to my brother (Shanma Pussella), and sister (Hashila Pussella) for their continuous support. I very much appreciate the support given by my father-in-law (Nihal Fernando), mother-in-law (Sriyani De Silva), and sister-in-law (Kulakshi Fernando) throughout the last few years.  \nThank you all for being a part of this journey and for your invaluable support.  \nContents  \nDedication  \nAbstract  \nAcknowledgements  \nContents  \nList of Tables  \nList of Figures  \nList of Abbreviations  \n1 Introduction 1  \n1.1 A Primer on Cricket .................................. 1  \n1.1.1 History and Popularity ........................... 1  \n1.1.2 Field of Play and Major Equipment .................... 2  \n1.1.3 The Basics ................................... 3  \n1.2 Motivation and Problem ............................... 9  \n1.3 Background ...................................... 12  \n1.4 Organization of the Thesis ","cbCaisCZoICXHZau","https://ap.wps.com/l/cbCaisCZoICXHZau","pdf",4022784,1,86,"English","en",105,"# Dedication\n# Abstract\n# Acknowledgements\n# List of Tables\n# List of Figures\n# List of Abbreviations\n# 1 Introduction\n## 1.1 A Primer on Cricket\n## 1.2 Motivation and Problem\n## 1.3 Background\n## 1.4 Organization of the Thesis\n# 2 Simulation Framework\n## 2.1 Introduction\n## 2.2 Dataset\n## 2.3 Architecture\n## 2.4 Prediction of Delivery Outcome\n## 2.5 Results\n# 3 Applications of the Simulation Framework\n## 3.1 Scoring Behaviour of Teams\n## 3.2 Finding the Optimal Batting Position for a Player\n## 3.3 Web Application\n# 4 Discussion and Conclusion\n## 4.1 Discussion\n## 4.2 Future Work\n## 4.3 Conclusion\n# References\n# A Python Code Blocks","[{\"question\":\"Why is cricket simulation difficult and what motivates this research?\",\"answer\":\"Simulation is challenging because cricket is complex and highly dynamic, and because detailed data can be scarce. The work is motivated by the limited representation of simulation in existing literature and the need for a practical simulation mechanism.\"},{\"question\":\"How does the simulator predict a cricket delivery’s outcome?\",\"answer\":\"The simulator uses machine learning models to predict the result of a “delivery,” treating it as the core unit of gameplay. Multiple modeling approaches are explored, including random forests and neural networks, with additional methods such as Gaussian process boosting.\"},{\"question\":\"What are the main applications of the simulation framework?\",\"answer\":\"The framework supports scorecard-oriented simulation, helps determine an optimal batting position for a batter in Twenty20 cricket, and is accompanied by an interactive web application for direct user interaction with the simulator.\"}]","Simulation for Cricket - A Machine Learning Approach | PDF",1785683957,217,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"simulation-for-cricket-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/simulation-for-cricket-a-machine-learning-approach/118517/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is cricket simulation difficult and what motivates this research?","Question",{"text":75,"@type":76},"Simulation is challenging because cricket is complex and highly dynamic, and because detailed data can be scarce. The work is motivated by the limited representation of simulation in existing literature and the need for a practical simulation mechanism.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the simulator predict a cricket delivery’s outcome?",{"text":80,"@type":76},"The simulator uses machine learning models to predict the result of a “delivery,” treating it as the core unit of gameplay. Multiple modeling approaches are explored, including random forests and neural networks, with additional methods such as Gaussian process boosting.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main applications of the simulation framework?",{"text":84,"@type":76},"The framework supports scorecard-oriented simulation, helps determine an optimal batting position for a batter in Twenty20 cricket, and is accompanied by an interactive web application for direct user interaction with the simulator.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]