[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122131-en":3,"doc-seo-122131-105":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},122131,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Exploring MotoGP Race Dynamics - A Machine Learning Study Using Fictional Data - Master Thesis","Sports analytics has progressed with advances in technology, making race data collection more efficient and dependable. This master thesis analyzes MotoGP racing performance by examining how track characteristics, motorcycle specifications, and rider information influence race outcomes through machine learning and predictive modelling. The study aims to assess whether these factors can predict overall MotoGP Championship results. Multiple machine learning models are compared within the SRP-CRISP-DM framework using a fictional dataset due to confidentiality.","Master Degree Program in  \nData Science and Advanced Analytics  \nMDSAA  \nExploring MotoGP Race Dynamics: A Machine Learning Study  \nUsing Fictional Data  \nMaria Margarida Santos Graça  \nMaster Thesis  \npresented as partial requirement for obtaining a Master’s Degree in Data Science and Advanced Analytics  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nExploring MotoGP Race Dynamics: A Machine Learning Study Using Fictional Data  \nby  \nMaria Margarida Santos Graça  \nMaster Thesis presented as partial requirement for obtaining the Master’s degree in Data Science and Advanced Analytics, with a specialization in Data Science  \nSupervised by  \nMijail Naranjo-Zolotov, PhD, NOVA IMS  \nSTATEMENT OF INTEGRITY  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \nLisbon, July 2024  \nABSTRACT  \nOver the years, sports analytics has evolved alongside technological advancements, with data collection becoming more efficient and dependable. This study focuses on racing performance analysis in MotoGP, exploring how track characteristics, motorcycle specifications and rider information may affect race outcomes, by applying Machine Learning techniques and predictive modelling. The primary objective is to determine if these factors can predict the overall MotoGP Championship results. The relationships between these features were investigated, comparing various machine learning models to predict race winners and podium finishers. This research was conducted using the SRP-CRISP-DM framework. Due to the confidential nature of actual motorcycle specifications, a fictional dataset was created. Machine Learning algorithms were applied to predict race outcomes, using models to determine the best predictors. The performance of these models was evaluated to identify the most accurate and reliable ones. The Random Forest model achieved the highest accuracy for predicting race winners, with a validation accuracy score of 0.93 and a training accuracy score of 1.00. For the podium prediction, predicting whether rider could finish on the podium, regardless of the specific position, the Gradient Boosting model was the best, with a validation accuracy score of 0.83 and a training accuracy score of 1.00. This research illustrates the significant potential of enhancing decision-making processes in MotoGP, allowing teams and stakeholders to gain a competitive edge, optimize race strategies and improve on-track performance, as well as highlight the connections between riders, teams, motorcycles and tracks.  \nKEYWORDS  \nSports Analytics; Racing Performance; Machine Learning; Data-driven; MotoGP  \nSustainable Development Goals (SDG):  \nTABLE OF CONTENTS  \n1. Introduction ............................................................................................................. 1  \n2. Literature Review..................................................................................................... 3  \n2.1. Sports Analytics................................................................................................. 3  \n2.1.1. Racing Performance ................................................................................... 3  \n2.2. Machine Learning in Sports Performance Analysis ............................................ 4  \n2.3. MotoGP ............................................................................................................ 5  \n2.3.1. Regulations and Rules for 2020 MotoGP .................................................... 7  \n3. Met","cbCaidceh2107DNk","https://ap.wps.com/l/cbCaidceh2107DNk","pdf",1038554,1,50,"English","en",105,"# 1. Introduction\n# 2. Literature Review\n## 2.1. Sports Analytics\n## 2.2. Machine Learning in Sports Performance Analysis\n## 2.3. MotoGP\n# 3. Methodology\n## 3.1. Business Understanding\n## 3.2. Data Understanding\n## 3.3. Data Preparation\n## 3.4. Modelling\n## 3.5. Evaluation\n## 3.6. Deployment\n# 4. Results and Discussion","[{\"question\":\"What is the main research objective of the MotoGP study?\",\"answer\":\"To determine whether track characteristics, motorcycle specifications, and rider information can predict MotoGP Championship results and race outcomes.\"},{\"question\":\"Why does the thesis use a fictional dataset?\",\"answer\":\"Actual motorcycle specifications are confidential, so a fictional dataset was created to support the modelling and evaluation.\"},{\"question\":\"Which machine learning models performed best for winner and podium predictions?\",\"answer\":\"Random Forest achieved the highest accuracy for predicting race winners (validation 0.93), while Gradient Boosting performed best for podium prediction (validation 0.83).\"}]","Exploring MotoGP Race Dynamics - A Machine Learning Study Using Fictional Data - Master Thesis | PDF",1785808969,126,{"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},"exploring-motogp-race-dynamics-a-machine-learning-study-using-fictional-data-master-thesis","",{"@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/exploring-motogp-race-dynamics-a-machine-learning-study-using-fictional-data-master-thesis/122131/",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","2026-08-04",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},"What is the main research objective of the MotoGP study?","Question",{"text":75,"@type":76},"To determine whether track characteristics, motorcycle specifications, and rider information can predict MotoGP Championship results and race outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why does the thesis use a fictional dataset?",{"text":80,"@type":76},"Actual motorcycle specifications are confidential, so a fictional dataset was created to support the modelling and evaluation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models performed best for winner and podium predictions?",{"text":84,"@type":76},"Random Forest achieved the highest accuracy for predicting race winners (validation 0.93), while Gradient Boosting performed best for podium prediction (validation 0.83).","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]