[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120280-en":3,"doc-seo-120280-105":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},120280,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Predicting special forces dropout via explainable machine learning","Selecting the right individuals for a sports team, organization, or military unit strongly affects future outcomes, yet common selection approaches either lack predictive reporting or remain non-explainable “black box” models. This study analyzes 274 special forces recruits, 196 dropouts, based on physical and psychological tests. Four machine learning models are compared for predictive performance, explainability, and stability. A stable rule-based SIRUS model best classifies dropouts with an AUC around 0.70. Physical and psychological variables both relate to dropout, especially 2800 m time, need for connectedness, and skin folds.","University of Groningen  \nPredicting special forces dropout via explainable machine learning  \nHuijzer, Rik; de Jonge, Peter; Blaauw, Frank J. ; Baatenburg de Jong, Maurits; de Wit, Age; Den Hartigh, Ruud J. R.  \nPublished in:  \nEuropean Journal of Sport Science  \nDOI:  \n10.1002/ejsc.12162  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2024  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nHuijzer, R. , de Jonge, P. , Blaauw, F. J. , Baatenburg de Jong, M. , de Wit, A. , & Den Hartigh, R. J. R. (2024) . Predicting special forces dropout via explainable machine learning. European Journal of Sport Science, 24(11), 1564-1572 . [https://doi.org/10.1002/ejsc.12162](https://doi.org/10.1002/ejsc.12162)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 29-12-2025  \nDOI: 10. 1002/ejsc.12162  \nORIGINAL PAPER  \nApplied Sport Science  \nPredicting special forces dropout via explainable machine learning  \nRik Huijzer1  | Peter de Jonge1 | Frank J. Blaauw2 | Maurits Baatenburg de Jong3 | Age de Wit3 | Ruud J. R. Den Hartigh1  \n1Faculty of Behavioural and Social Sciences, Department of Developmental Psychology, University of Groningen, Groningen, the Netherlands  \n2Research and Innovation, Researchable BV, Assen, the Netherlands  \n3Ministry of Defence, Den Haag, the Netherlands  \nCorrespondence  \nRuud J. R. Den Hartigh. Email: [j.r.den.hartigh@rug.nl](j.r.den.hartigh@rug.nl)  \nFunding information  \nMinisterie van Defensie  \nAbstract  \nSelecting the right individuals for a sports team, organization, or military unit has a large influence on the achievements of the organization. However, the approaches commonly used for selection are either not reporting predictive performance or not explainable (i.e., black box models). In the present study, we introduce a novel approach to selection research, using various machine learning models. We examined 274 special forces recruits, of whom 196 dropped out, who performed a set of physical and psychological tests. On this data, we compared four machine learning models on their predictive performance, explainability, and stability. We found that a stable rule‐based (SIRUS) model was most suitable for classifying dropouts from the special forces selection program. With an averaged area under the curve score of 0. 70, this model had good predictive performance, while remaining explainable and stable. Furthermore, we found that both physical and psychological variables were related to dropout. More specifically, a higher score on the 2800 m time, need for connectedness, and ","cbCaiqJz1rUyyskH","https://ap.wps.com/l/cbCaiqJz1rUyyskH","pdf",1572598,1,10,"English","en",105,"# Abstract\n## Study aims and dataset\n## Models compared\n## Key predictors and practical implications","[{\"question\":\"What problem does the study address in special forces selection?\",\"answer\":\"It addresses the need for selection methods that provide both predictive performance and interpretability instead of relying on non-explainable black box models.\"},{\"question\":\"Which machine learning model performed best for predicting dropout?\",\"answer\":\"A stable rule-based SIRUS model was most suitable, achieving an averaged AUC score around 0.70 while remaining explainable and stable.\"},{\"question\":\"Which variables were most strongly associated with dropout?\",\"answer\":\"Higher 2800 m time, need for connectedness, and skin folds showed the strongest association with dropout.\"}]","Predicting special forces dropout via explainable machine learning | PDF",1785729218,25,{"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},"predicting-special-forces-dropout-via-explainable-machine-learning","",{"@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/predicting-special-forces-dropout-via-explainable-machine-learning/120280/",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 special forces selection?","Question",{"text":75,"@type":76},"It addresses the need for selection methods that provide both predictive performance and interpretability instead of relying on non-explainable black box models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning model performed best for predicting dropout?",{"text":80,"@type":76},"A stable rule-based SIRUS model was most suitable, achieving an averaged AUC score around 0.70 while remaining explainable and stable.",{"name":82,"@type":73,"acceptedAnswer":83},"Which variables were most strongly associated with dropout?",{"text":84,"@type":76},"Higher 2800 m time, need for connectedness, and skin folds showed the strongest association with dropout.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]