Transfer market activities and sportive performance in European first football leagues: A dynamic network approach

• Published in: PloS one 2018-12, Vol.13 (12), p.e0209362-e0209362
• Main Authors: Matesanz, David, Holzmayer, Florian, Torgler, Benno, Schmidt, Sascha L, Ortega, Guillermo J
• Format: Article
• Language: English
• Subjects: Athletes; Athletic Performance - economics; Biology and Life Sciences; Clubs; College football; Computer and Information Sciences; Datasets as Topic; Earth Sciences; Europa; Europe; Football; Football players; Humans; Learning algorithms; Machine Learning; Male; Markets; Models, Economic; People and Places; Physical Sciences; Principal Component Analysis - methods; Principal components analysis; Professional soccer; Research and Analysis Methods; Self organizing maps; Soccer; Soccer - economics; Social Sciences
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0209362

Professional football is a globalized game in which players are the most valuable assets for clubs. In this study, we explore the evolution of the football players' transfer network among 21 European first leagues between the seasons 1996/1997 and 2015/2016. From a topological point of view, we show that this network achieved an upper limit expansion around season 2007/2008, thereafter becoming more connected and dense. Using a machine learning approach based on Self-Organizing Maps and Principal Component Analysis we confirm that European competitions, such as the UEFA Champions League or UEFA Europa League, are indeed a "money game" where the clubs with the highest transfer spending achieve better sportive performance. Some clubs' transfer market activities also affect domestic performance. We conclude from our findings that the relationship between transfer spending and domestic or international sportive performance might lead to substantial inequality between clubs and leagues, while potentially creating a virtuous (vicious) circle in which these variables reinforce (weaken) each other.