[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119847-en":3,"doc-seo-119847-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119847,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Applying Machine Learning to Forecast Formula 1 Race Outcomes - Master’s Thesis","Pit stop prediction is a core determinant of performance in Formula 1 racing. This master’s thesis develops a predictive machine learning model to determine optimal pit stop timing for specific laps by leveraging historical race data from the 2019–2022 seasons. The approach incorporates key race factors such as tire degradation, car positions, and race dynamics. Three algorithms—SVM, Random Forest, and Artificial Neural Networks—are trained and compared using performance metrics centered on the F1 score, yielding realistic but inherently uncertain predictions that support decision-making rather than acting as a standalone predictor.","Master’s programme in ICT Innovation-Data Science  \nApplying Machine Learning to Forecast Formula 1 Race Outcomes  \nLoreto García Tejada  \nMaster’s Thesis 2023  \n© 2023  \nThis work is licensed under a Creative Commons“Attribution-NonCommercial-ShareAlike 4 .0 International” license.  \n| Author Loreto García Tejada |\n| --- |\n| Title Applying Machine Learning to Forecast Formula 1 Race Outcomes |\n| Degree programme ICT Innovation-Data Science |\n| Major Data Science |\n| Supervisor Prof. Alex Jung |\n| Advisor Prof. Alex Jung |\n| Date 31 July 2023 Number of pages 69+1 Language English |\n| Abstract\u003Cbr>Pit stops are integral to the success of drivers in Formula 1 racing. This thesis aims to develop a predictive model that effectively determines the optimal timing for pit stops during specific laps of Formula 1 races. By employing machine learning algorithms and analyzing historical race data from the 2019 to 2022 seasons, this study creates a reliable system that considers various race factors, including tire degradation, car positions, and overall race dynamics. Three machine learning algorithms, namely Support Vector Machines (SVM), Random Forest, and Artificial Neural Networks are utilized and compared based on performance metrics, primarily the F1 score. The objective is to identify the most suitable algorithm capable of accurately predicting pit stop requirements. The findings of this thesis highlight the challenging nature of pit stop prediction in Formula 1 . While the models demonstrate reasonable accuracy in predicting pit stops, achieving precise predictions remains complex due to the multitude of variables and inherent uncertainties involved. The results emphasize the models’ potential as valuable decision-support tools rather than standalone predictors, emphasizing the importance of incorporating additional information and expert knowledge into the decision-making process. |\n| Keywords Machine learning, Data science, Artificial neural networks, Random forest, Support vector machine, Pit stops, Formula 1, Predictive model |\n\nPreface  \nI would like to begin by expressing my deepest gratitude to my parents, who have been unwavering pillars of support throughout my life. Their unconditional love, guidance, and motivation have played a crucial role in shaping my educational and personal journey.  \nI am deeply grateful to my brother Diego for introducing me to the world of Formula 1. Through our shared enthusiasm for this sport, our bond has grown stronger, and it has increased the range of things that we enjoy together.  \nI extend my sincere appreciation to the remarkable people I have had the privilege of knowing during my two-year master’s program. To those who have shared my experiences in Madrid and Helsinki, thank you for the invaluable friendships, bonds and memorable moments we have shared.  \nSpecial gratitude goes to Professor Alex Jung for his guidance and mentorship as my advisor and supervisor. I am immensely grateful for the opportunity he has given me to undertake this thesis and for placing his trust in my idea.  \nLoreto García Tejada  \nContents  \n1 Introduction 1  \n1. 1 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \n1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . 2  \n1.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2  \n1.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \n1.5 Industrial relevance . . . . . . . . . . . . . . . . . . . . . . . . . . 4  \n1.6 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5  \n2 Background 6  \n2.1 Machine learning ........................... 6  \n2.1.1 Approaches .......................... 6  \n2.1.2 Evaluation and validation . . . . . . . . . . . . . . . . . . . 8  \n2.2 Machine learning methods . . . . . . . . . . . . . . . . . . . . . . 14  \n2.2. 1 Random Forest . . . . . . . . . . . . . . . . . . . . . . . . 14  \n2.2.2 Support Vector Machine . . . . . . ","cbCaimvQ41rly7DQ","https://ap.wps.com/l/cbCaimvQ41rly7DQ","pdf",3508398,1,78,"English","en",105,"# Contents\n## 1 Introduction\n## 2 Background\n## 3 Methods\n## 4 Results\n## 5 Conclusions\n## References\n## A Appendix","[{\"question\":\"What problem does the thesis address in Formula 1 racing?\",\"answer\":\"It addresses how to predict the optimal timing of pit stops during specific laps of Formula 1 races to support racing success.\"},{\"question\":\"Which machine learning algorithms are compared?\",\"answer\":\"The thesis uses and compares Support Vector Machines (SVM), Random Forest, and Artificial Neural Networks.\"},{\"question\":\"Why are pit stop predictions still challenging despite reasonable accuracy?\",\"answer\":\"Precise predictions remain complex because pit stop outcomes depend on many variables and contain inherent uncertainties.\"}]","Applying Machine Learning to Forecast Formula 1 Race Outcomes - Master’s Thesis | PDF",1785726634,197,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"applying-machine-learning-to-forecast-formula-1-race-outcomes-masters-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@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/applying-machine-learning-to-forecast-formula-1-race-outcomes-masters-thesis/119847/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address in Formula 1 racing?","Question",{"text":76,"@type":77},"It addresses how to predict the optimal timing of pit stops during specific laps of Formula 1 races to support racing success.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning algorithms are compared?",{"text":81,"@type":77},"The thesis uses and compares Support Vector Machines (SVM), Random Forest, and Artificial Neural Networks.",{"name":83,"@type":74,"acceptedAnswer":84},"Why are pit stop predictions still challenging despite reasonable accuracy?",{"text":85,"@type":77},"Precise predictions remain complex because pit stop outcomes depend on many variables and contain inherent uncertainties.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]