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In this paper we aim at investigating the ability of various composite indicators to define a measurement structure for the global soccer performance. We rely on data provided by the EA Sports experts, who are the ultimate authority on soccer performance measurement: they periodically produce a set of players\u2019 attributes that make up the broader, theoretical performance dimensions. Considering the potential of clustering techniques to confirm or disconfirm the experts\u2019 assumptions in terms of aggregations between indicators, 29 players\u2019 performance attributes or variables (from the FIFA19 version of the videogame, that is, <jats:italic>sofifa<\/jats:italic>) have been considered and processed with three different techniques: the <jats:italic>Cluster of variables around latent variables<\/jats:italic> (CLV), the <jats:italic>Principal covariates regression<\/jats:italic> (PCovR) and <jats:italic>Bayesian model-based clustering<\/jats:italic> (B-MBC). The three procedures yielded clusters that differed from experts\u2019 classification. In order to identify the most appropriate measurement structure, the resulting clusters have been embedded into <jats:italic>Structural equation models with partial least squares<\/jats:italic> (PLS-SEMs) with a Higher-Order Component (that is, the overall soccer performance). The statistically derived composite indicators have been compared with those of experts\u2019 classification. Results support the concurrent validity of composite indicators derived through the statistical methods: overall, they show that, in the lack of expert judgement, composite indicators, as well as the resulting PLS-SEM models, are a viable alternative given their greater correlation to players\u2019 economic value and salary. \n<\/jats:p>","DOI":"10.1007\/s10479-023-05185-w","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T11:02:36Z","timestamp":1675681356000},"page":"37-56","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Clustering of variables methods and measurement models for soccer players\u2019 performances"],"prefix":"10.1007","volume":"325","author":[{"given":"Maurizio","family":"Carpita","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0283-5389","authenticated-orcid":false,"given":"Paola","family":"Pasca","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Serena","family":"Arima","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enrico","family":"Ciavolino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,6]]},"reference":[{"issue":"3","key":"5185_CR1","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.indmarman.2011.06.020","volume":"41","author":"C Barroso","year":"2012","unstructured":"Barroso, C., & Pic\u00f3n, A. 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The authors have no financial or proprietary interests in any material discussed in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}