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The framework is designed to extract latent offensive profiles and predict high-efficiency scoring profiles across domestic and international competitions. The approach begins by constructing three composite indicators \u2013 Index of Offensive Efficiency, Competitive Resilience Index, and Versatility Score \u2013 designed to capture multidimensional aspects of a player\u2019s offensive productivity, adaptability across competitions, and contribution breadth. These engineered metrics inform a fuzzy clustering algorithm that reveals two core performance profiles: \u201cSeasoned Finishing Specialists\u201d and \u201cEmerging Versatile Contributors\u201d. Building on this segmentation, a supervised learning model based on XGBoost is employed to predict the likelihood of surpassing a goals-per-shot efficiency threshold. Model interpretability is ensured via SHAP plot, which highlight the pivotal role of salary, finishing metrics, and competition-specific resilience. Partial dependence plots further expose nonlinear and interactive effects between key predictors. A network-based analysis complements the model by mapping performance similarities and identifying both archetypal and transitional performers via centrality measures. Robustness checks, including alternative winsorization, fuzziness levels, and subgroup-specific clustering, confirm the stability of the results. Overall, the proposed framework bridges segmentation and prediction with transparency and domain-relevance, offering a comprehensive toolkit for decision-makers in sports analytics, recruiters, and talent management.<\/jats:p>","DOI":"10.1186\/s40537-025-01297-1","type":"journal-article","created":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T11:05:46Z","timestamp":1764327946000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Fuzzy clustering with robust learning models for soccer player profiling and resilience analysis"],"prefix":"10.1186","volume":"12","author":[{"given":"Antonio","family":"Pacifico","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"1297_CR1","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.1007\/s10618-017-0513-2","volume":"31","author":"G Andrienko","year":"2017","unstructured":"Andrienko G, Andrienko N, Budziak G, Dykes J, Fuchs G, Landesberger T, et al. 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