[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122382-en":3,"doc-seo-122382-105":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},122382,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","TRAINING LOAD AND PHYSICAL PERFORMANCE ANALYSIS IN SOCCER - A MACHINE LEARNING APPROACH","Training load monitoring is a common on-field practice to optimize soccer training prescription by enhancing physical performance and reducing injury risk. Both external load (physical work in the plan, e.g., distance in speed bands, accelerations, metabolic power) and internal load (the athlete’s physiological response) support informed decisions on training dose and recovery. Evidence is shaped by Big Data from GPS, IMU, heart-rate devices, and RPE, where machine learning helps predict injury, clarify performance relationships, and refine data-informed training strategies across microcycle schedules.","Ph.D. Program in Health Promotion and Cognitive Sciences  \nSport and Exercise Sciences Research Unit  \nDepartment of Psychology, Educational Science and Human Movement  \nTRAINING LOAD AND PHYSICAL PERFORMANCE ANALYSIS IN SOCCER: A MACHINE  \nLEARNING APPROACH  \nPhD Coordinator  \nProfessor GIUSEPPA CAPPUCCIO  \nPhD student  \nDott. GUGLIELMO PILLITTERI  \nSupervisor  \nProfessor GIUSEPPE BATTAGLIA  \nCo-supervisors  \nProfessor FILIPE MANUEL BATISTA CLEMENTE & Professor HUGO SARMENTO  \nPhD CICLE XXXVI  \nAcademic Year 2024/2025  \nINDEX  \nAbstract Acknowledgements List of abbreviations  \nList of table and figures  \nChapter I-Introduction  \n1.0 An Updated Conceptual Framework on Physical Performance and Training in Soccer. A narrative review  \n2.0 Injury in soccer: from risk factors to prevention strategies  \n3.0 Implication of Machine Learning in soccer  \n4.0 Aims and Objectives ofthe thesis  \nChapter II – Understanding soccer Physical Performance and Training Load  \n2.0 Chapter II overview  \n2.1 Toward a New Conceptual Approach to “Intensity” in Soccer Player’s Monitoring: A Narrative Review  \n2.2 Translating player monitoring into training prescriptions: Real world soccer scenario and practical proposals  \n2.3 Discussion  \nChapter III-The relationship between Training Load and Training Outcomes in soccer: a Machine learning approach  \n3.0 Chapter III overview  \n3.1 Relationship between external and internal load indicators and injury using machine learning in professional soccer: a systematic review and meta-analysis  \n3.2 Association between match-related physical activity profiles and playing positions in different tasks: a data driven approach  \n3.3 Elite Soccer Players' Weekly Workload Assessment Through a New Training Load and Performance Score  \n3.4 Machine Learning Analysis of Intensity Profiles and Key Indicators in Standard Microcycle of Professional Soccer Players  \n3.5 Association between internal load responses and recovery ability in U19 professional soccer players: A machine learning approach  \n3.6 Training load and injuries prediction  \n3.7 ANew Conceptual Framework for Managing Hamstring Injury Risk In Soccer – Implementing A Data-Informed Approach: A Brief Review  \n3.8 Discussion  \nChapter IV-Personal skills and achievements  \n4.0 Learning experiences and skills at the Sport and Exercise Sciences Research Unit  \n4.1 Learning experiences and skills at Palermo FC  \n4.2 International period and international collaborations  \n4.3 Other published papers  \nChapter V-Conclusion  \n5.1 Conclusion  \n5.2 Limitation  \n5.3 Practical application  \nReferences Appendix  \n1. Articles under review  \n2. Co-authored abstracts  \n3. Co-supervised master thesis  \nAbstract  \nTraining load monitoring represent a “on-field” common practice to optimize soccer training prescription aiming to enhance physical performance trying to reduce the injury risk. Understanding both external load, considered as the physical work prescribed in the training plan (e.g., measures of total distance covered (or in specific speed bands), accelerations, or metabolic power and internal load (i.e., the athlete’s physiological response to a given external load performed), offer a necessary practitioners’ support in informed-making concerning the training prescription. Worth noting, in order to prescribe optimal training (i.e., balancing the training dose and recovery,) to reach the desired adaptation, whole components of soccer performance must be considered such as training and recovery aspects, the athlete’s fitness status (i.e., physiological assessment), keeping into account the training methodologies principles as well as the match-day target derived from microcycle schedule. Several studies have been dealt with training load monitoring, physical performance, and injury risk reduction in soccer. The “era” of Big Data, is strongly characterized by the need to analyse a huge amount of data daily collected through Micro Electro-Mechanical Systems (MEMS) devices suc","cbCaitE6gW1nv29f","https://ap.wps.com/l/cbCaitE6gW1nv29f","pdf",6914998,1,304,"English","en",105,"# Abstract\n# Chapter I-Introduction\n## 1.0 An Updated Conceptual Framework on Physical Performance and Training in Soccer. A narrative review\n## 2.0 Injury in soccer: from risk factors to prevention strategies\n## 3.0 Implication of Machine Learning in soccer\n## 4.0 Aims and Objectives ofthe thesis\n# Chapter II – Understanding soccer Physical Performance and Training Load\n## 2.1 Toward a New Conceptual Approach to “Intensity” in Soccer Player’s Monitoring: A Narrative Review\n## 2.2 Translating player monitoring into training prescriptions: Real world soccer scenario and practical proposals\n## 2.3 Discussion\n# Chapter III-The relationship between Training Load and Training Outcomes in soccer: a Machine learning approach\n## 3.1 External and internal load indicators and injury using machine learning in professional soccer: a systematic review and meta-analysis\n## 3.2 Match-related physical activity profiles and playing positions: a data driven approach\n## 3.3 Elite Soccer Players' Weekly Workload Assessment Through a New Training Load and Performance Score\n## 3.4 Machine Learning Analysis of Intensity Profiles and Key Indicators in Standard Microcycle of Professional Soccer Players\n## 3.5 Internal load responses and recovery ability in U19 professional soccer players: A machine learning approach\n## 3.6 Training load and injuries prediction\n## 3.7 A New Conceptual Framework for Managing Hamstring Injury Risk In Soccer – Implementing A Data-Informed Approach: A Brief Review\n## 3.8 Discussion\n# Chapter IV-Personal skills and achievements\n## 4.1 Learning experiences and skills at Palermo FC\n## 4.2 International period and international collaborations\n## 4.3 Other published papers\n# Chapter V-Conclusion\n## 5.1 Conclusion\n## 5.2 Limitation\n## 5.3 Practical application\n# References and Appendix","[{\"question\":\"What is the role of external and internal load monitoring in soccer training?\",\"answer\":\"External load describes the physical work prescribed in the training plan (e.g., distance, accelerations, metabolic power), while internal load reflects the athlete’s physiological response. Together, they support decisions about training prescription and balancing training dose with recovery.\"},{\"question\":\"How does machine learning contribute to understanding training load and performance outcomes?\",\"answer\":\"Machine learning is used to analyze large datasets from GPS/IMU, heart-rate monitors, and subjective scales like RPE. It supports prediction of injuries and better clarification of relationships between training load, performance, recovery ability, and training outcomes.\"},{\"question\":\"Which injury-related topics are addressed in the thesis framework?\",\"answer\":\"The content includes injury risk and prevention strategies, training load and injuries prediction, and a data-informed conceptual framework for managing hamstring injury risk in soccer.\"}]","TRAINING LOAD AND PHYSICAL PERFORMANCE ANALYSIS IN SOCCER - A MACHINE LEARNING APPROACH | PDF",1785810343,766,{"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},"training-load-and-physical-performance-analysis-in-soccer-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/training-load-and-physical-performance-analysis-in-soccer-a-machine-learning-approach/122382/",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 role of external and internal load monitoring in soccer training?","Question",{"text":75,"@type":76},"External load describes the physical work prescribed in the training plan (e.g., distance, accelerations, metabolic power), while internal load reflects the athlete’s physiological response. Together, they support decisions about training prescription and balancing training dose with recovery.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning contribute to understanding training load and performance outcomes?",{"text":80,"@type":76},"Machine learning is used to analyze large datasets from GPS/IMU, heart-rate monitors, and subjective scales like RPE. It supports prediction of injuries and better clarification of relationships between training load, performance, recovery ability, and training outcomes.",{"name":82,"@type":73,"acceptedAnswer":83},"Which injury-related topics are addressed in the thesis framework?",{"text":84,"@type":76},"The content includes injury risk and prevention strategies, training load and injuries prediction, and a data-informed conceptual framework for managing hamstring injury risk in soccer.","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"]