[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119102-en":3,"doc-seo-119102-105":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},119102,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Intelligent Classifiers for Football Player Performance Based on Machine Learning Models - Original Scientific Paper","Machine Learning (ML) methods have expanded across many academic fields, with increasing use in football for analysis, injury prediction, market value forecasting, and action recognition. Research remains limited in systematically evaluating football player performance for coaching decisions. This study categorizes players as active, normal, or weak using activity features via the Performance Evaluation Machine Learning Model (PEMLM) with two novel datasets for training and match sessions. Seven ML algorithms are applied, and k-fold cross-validation verifies accuracy, test time, and overfitting robustness.","Intelligent Classifiers for Football Player  \nPerformance Based on Machine Learning Models  \nOriginal Scientific Paper  \nBaydaa M. Merzah  \nDepartment of Computer Science,  \nCollege of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq  \nAl-Nahrain University, Baghdad, Iraq [bai21c1007@uoanbar.edu.iq](bai21c1007@uoanbar.edu.iq)  \n[Muayad S. Croock](Muayad S. Croock)  \nDepartment of Control and Systems University of Technology,  \nBaghdad, Iraq [muayad.s.croock@uotechnology.edu.iq](muayad.s.croock@uotechnology.edu.iq)  \nAhmed N. Rashid  \nDepartment of Computer Networks Systems,  \nCollege of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq [rashidisgr@uoanbar.edu.iq](rashidisgr@uoanbar.edu.iq)  \nAbstract– The remarkable effectiveness of Machine Learning (ML) methodologies has led to a significant increase in their application across various academic domains, particularly in diverse sports sectors. Over the past decade, scholars have utilized Machine Learning (ML) algorithms in football for varied objectives, encompassing the analysis of football players'performances, injury prediction, market value forecasting, and action recognition. Nevertheless, there has been a scarcity of research addressing the evaluation of football players'performance, which is a noteworthy concern for coaches. Hence, the objective of this work is to categorize the performance of football players into active, normal, or weak based on activity features. This will be achieved through the utilization of the Performance Evaluation Machine Learning Model (PEMLM), employing two novel datasets that cover both training and match sessions. To attain this goal, seven machine learning methods are applied, namely Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, Gaussian Naïve Bayes, Multi-Layer Perceptron, and K-Nearest Neighbor. The findings indicate that in the dataset corresponding to match sessions, the Decision Tree classifier attains the highest accuracy (100%) and the shortest test time. In contrast, the K-Nearest Neighbor demonstrates the best accuracy (96%) and a reasonable test time for the training dataset. These reported metrics underscore the reliability and validity of the proposed assessment approach in evaluating the performance of footballplayers in online games. The results are verified and the models are assessed for overfitting through a k-fold cross-validation process.  \nKeywords: Dataset structuring, Football, Machine learning, Player performance  \n1. INTRODUCTION  \nMachine Learning (ML) has emerged as a powerful catalyst, transforming various fields by effectively extracting valuable insights from extensive and complex datasets. Its significance goes beyond technological limitations, profoundly impacting a wide range of industries, including healthcare [1-3], wireless sensor networks [4, 5], sports [6-9], and various other domains [10- 12]. In the realm of football, the applications of ML can be categorized into distinct groups, as depicted in Fig. 1.  \nThe prevention and anticipation of injuries are extremely important in the sports industry, significantly affecting the financial stability of sports clubs and team performance. The absence of essential players from games and training sessions due to injury has a significant financial impact, costing the team a total of EUR 188 million annually. The financial burden incorporates various factors, encompassing expenses associated with player recuperation, efforts in rehabilitation, and the salaries of players [13]. Recent empirical studies have  \nVolume 15, Number 2, 2024 173  \nemphasized the effectiveness of ML techniques in the injury prediction domain. Additionally, these techniques have demonstrated excellent outcomes for predicting injuries in adult handball and football players [14].  \nFig. 1. ML applications in football  \nFurthermore, ML has proven to be more sensitive in forecasting injuries among young foo","cbCaidIa4nKkuYB9","https://ap.wps.com/l/cbCaidIa4nKkuYB9","pdf",1523262,1,11,"English","en",105,"# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"What problem does the study address in football analytics?\",\"answer\":\"It targets the lack of research focused on evaluating football players’ performance in a way that can support coaches’ decisions.\"},{\"question\":\"How is player performance classified in the proposed approach?\",\"answer\":\"Players are categorized into active, normal, or weak based on activity features using the Performance Evaluation Machine Learning Model (PEMLM).\"},{\"question\":\"Which models and results are reported for match-session and training datasets?\",\"answer\":\"For match sessions, a Decision Tree classifier achieves the highest accuracy (100%) with the shortest test time. For the training dataset, K-Nearest Neighbor yields the best accuracy (96%) with reasonable test time, and results are validated with k-fold cross-validation.\"}]","Intelligent Classifiers for Football Player Performance Based on Machine Learning Models - Original Scientific Paper | PDF",1785722403,28,{"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},"intelligent-classifiers-for-football-player-performance-based-on-machine-learning-models-original-scientific-paper","",{"@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/intelligent-classifiers-for-football-player-performance-based-on-machine-learning-models-original-scientific-paper/119102/",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","2026-08-03",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 problem does the study address in football analytics?","Question",{"text":75,"@type":76},"It targets the lack of research focused on evaluating football players’ performance in a way that can support coaches’ decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is player performance classified in the proposed approach?",{"text":80,"@type":76},"Players are categorized into active, normal, or weak based on activity features using the Performance Evaluation Machine Learning Model (PEMLM).",{"name":82,"@type":73,"acceptedAnswer":83},"Which models and results are reported for match-session and training datasets?",{"text":84,"@type":76},"For match sessions, a Decision Tree classifier achieves the highest accuracy (100%) with the shortest test time. For the training dataset, K-Nearest Neighbor yields the best accuracy (96%) with reasonable test time, and results are validated with k-fold cross-validation.","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"]