{ "abstract": "Background: This study focuses on the quantitative analysis of football players’ performance based on a set of performance data. Objective: The objective of this study was to develop a multivariate analytical model to identify, quantify, and predict the most relevant factors that determine the offensive and defensive profiles of professional football players, thereby optimizing functional segmentation, performance analysis, and decision-making processes in the contexts of scouting and data-based tactical planning. Materials and methods: A dataset was compiled through automated extraction (web scraping) via the Python programming language and its specialized libraries. The full dataset comprised 2,689 player–league records corresponding to 2,530 unique players from Europe’s five major UEFA-affiliated national leagues during the 2022–2023 season (159 players who transferred between two leagues mid-season contributed one record per league, 2,530 + 159 = 2,689). After applying a minimum-eligibility filter of >450 minutes played to ensure stable per-90 metrics, 1,624 player–league records (1,620 unique players) were retained for the modeling workflow. Dimensionality reduction techniques (PCA) and explainable machine learning (Shapley Additive exPlanations [SHAP]) with predictive modeling were applied. Eight algorithms were trained and compared to predict PC1 Offensive and PC1 Defensive. Results: The First Offensive Principal Component (PC1 Offensive) synthesizes actions that generate scoring opportunities, such as actions prior to shots (SCA_p90, 13.3%), progressive receptions (RecProg_p90, 13.0%) and total shots (Shots_p90, 12.7%). The first-defensive principal component (PC1 Defensive) highlights tackles (Tkl_p90, 19.1%), blocks (15.6%) and interceptions (15.6%). Conclusions: Finally, the inclusion of SHAP allows for an in-depth interpretation of the models, identifying the key statistical factors that influence players’ offensive and defensive contributions, which is valuable for scouting, tactical analysis and player development.", "abstract_shown": true, "affiliations": [ "Faculty of Education and Humanities, Universidad Andres Bello, Viña del Mar, Chile", "Department of Sport Sciences, Faculty of Sport and Health Sciences, Fit Generation Research Institute, Andorra la Vella, Andorra", "Universidad de Extremadura, Facultad de Ciencias del Deporte, Caceres, España", "Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso, Chile", "Facultad de Ciencias de la Vida, Universidad Viña del Mar, Viña del Mar, Chile", "Escuela de Kinesiología, Facultad de Salud, Universidad Santo Tomás, Talca, Chile", "Escuela de Kinesiología, Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca, Chile", "Exercise and Rehabilitation Sciences Laboratory, School of Physical Therapy, Faculty of Rehabilitation Sciences, Universidad Andres Bello, Santiago, Chile", "Grupo AFySE, Investigación en Actividad Física y Salud Escolar, Escuela de Pedagogía en Educación Física, Facultad de Educación, Universidad de Las Américas, Santiago, Chile", "Faculty of Physical Education, National Pedagogical University, Bogotá, Colombia", "Department of Physical Activity Sciences, Faculty of Education Sciences, Universidad Católica del Maule, Talca, Chile", "School of Education, Faculty of Human Sciences, Universidad Bernardo O'Higgins, Santiago, Chile", "Faculty of Medicine, Health and Sports, Universidad Europea de Madrid, Madrid, Spain", "Grupo de Investigación en Cultura, Educación y Sociedad, Universidad de la Costa, Barranquilla, Colombia", "School of Medicine, Universidad Espíritu Santo, Samborondón, Ecuador" ], "article_type": "Research Article", "arxiv": null, "author_total": 15, "authors": [ { "affs": [ 0 ], "name": "Rodrigo Yáñez-Sepúlveda" }, { "affs": [ 1 ], "name": "Aldo Vásquez-Bonilla" }, { "affs": [ 2 ], "name": "Rodrigo Olivares" }, { "affs": [ 3 ], "name": "Pablo Olivares" }, { "affs": [ 4 ], "name": "Guillermo Cortés-Roco" }, { "affs": [ 5 ], "name": "Eduardo Guzmán-Muñoz" }, { "affs": [ 6 ], "name": "Marcelo Tuesta" }, { "affs": [ 7 ], "name": "Jorge Olivares-Arancibia" }, { "affs": [ 8 ], "name": "Boryi A. Becerra-Patiño" }, { "affs": [ 9 ], "name": "Juan David Paucar-Uribe" }, { "affs": [ 10 ], "name": "Diego Andrés Rada-Perdigón" }, { "affs": [ 11 ], "name": "Exal Garcia-Carrillo" }, { "affs": [ 12 ], "name": "Tomás Rivera-Kofler" }, { "affs": [ 13 ], "name": "Vicente Javier Clemente-Suárez" }, { "affs": [ 14 ], "name": "José Francisco López-Gil" } ], "bibtex": "https://research.draconitic.com/paper/0sgk25rntgqr2.bib", "cited_by": 0, "doi": "10.1371/journal.pone.0353239", "fulltext_url": null, "id": "0sgk25rntgqr2", "issns": [ "1932-6203" ], "landing_url": "https://doi.org/10.1371/journal.pone.0353239", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "2732|Sports Performance and Training" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive science/Cognition/Decision making" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Decision making" ], [ "plos", "/Biology and life sciences/Psychology/Behavior/Recreation/Sports" ], [ "plos", "/Biology and life sciences/Psychology/Cognitive psychology/Decision making" ], [ "plos", "/Biology and life sciences/Sports science/Sports" ], [ "plos", "/Computer and information sciences/Artificial intelligence/Machine learning" ], [ "plos", "/Computer and information sciences/Software engineering/Preprocessing" ], [ "plos", "/Engineering and technology/Software engineering/Preprocessing" ], [ "plos", "/Physical sciences/Mathematics/Statistics/Statistical methods/Forecasting" ], [ "plos", "/Physical sciences/Mathematics/Statistics/Statistical methods/Multivariate analysis/Principal component analysis" ], [ "plos", "/Physical sciences/Mathematics/Statistics/Statistical methods/Regression analysis/Linear regression analysis" ], [ "plos", "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions" ], [ "plos", "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Forecasting" ], [ "plos", "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Multivariate analysis/Principal component analysis" ], [ "plos", "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Regression analysis/Linear regression analysis" ], [ "plos", "/Social sciences/Psychology/Behavior/Recreation/Sports" ], [ "plos", "/Social sciences/Psychology/Cognitive psychology/Decision making" ] ], "openalex": "W7211992087", "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0353239&type=printable", "pmcid": null, "pmid": null, "published": "2026-09-09", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/0sgk25rntgqr2.ris", "sources": [ { "first_seen": "2026-10-30T12:17:56Z", "native_id": "10.1371/journal.pone.0353239", "source": "plos", "url": "https://doi.org/10.1371/journal.pone.0353239" } ], "subjects": [ { "field": "mathematics", "origin": "plos:statistical methods", "subfield": "statistics" } ], "title": "Optimizing football analytics: Dimensionality reduction meets machine learning for offensive and defensive player profiling", "title_text": "Optimizing football analytics: Dimensionality reduction meets machine learning for offensive and defensive player profiling", "type": "peer-reviewed", "updated_at": "2026-10-30T12:17:56Z", "url": "https://research.draconitic.com/paper/0sgk25rntgqr2", "venue": "PLOS One", "versions": [] }