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Determinants of sport performance in European football: what can we learn from the data?
dc.creator | Zambom Ferraresi, Fabíola | es_ES |
dc.creator | Ríos Ibáñez, Vicente | es_ES |
dc.creator | Lera López, Fernando | es_ES |
dc.date.accessioned | 2019-06-25T11:34:36Z | |
dc.date.available | 2019-10-01T23:00:15Z | |
dc.date.issued | 2018 | |
dc.identifier.issn | 1873-5797 | |
dc.identifier.uri | https://hdl.handle.net/2454/33483 | |
dc.description.abstract | Nowadays game-related statistics in the sports industry are demanded by coaches, players, managers, journalists, supporters, fans, video games developers, betting markets and academics. However, the employment of game related statistics to analyse performance in football (soccer) has inherent problems given it is a multifaceted and complex phenomenon. This study analyses the importance of a large number of possible determinants of sport performance in the 'Big Five' European football leagues during the period 2012/13-2014/15. To this end, Bayesian model averaging techniques and relative importance metrics are employed. The results obtained point to the existence of a set of robust determinants in sport performance. This set of drivers comprises (i) assists, (ii) shots conceded, (iii) saves made by the goalkeeper, (iv) the number of precise passes with respect to the total number of passes, and (v) shots on target. The results of the study support the idea that offensive actions are more relevant than defensive ones. In addition, we find the existence of some performance indicators that have usually been ignored by previous analyses, such as saves made by the goalkeeper and assists. These findings could help the decision-making process of the coaching, scouting and managerial units of football clubs. Finally, the modelling techniques employed in this context can be generalized to gain knowledge in other fields of knowledge to extract factors affecting complex problems from large data sets. This could be particularly interesting when previous research has not yet obtained a well-defined and robust set of factors explaining these complex problems. | en |
dc.description.sponsorship | Fernando Lera-Lopez acknowledges the financial support from the Spanish Ministry of Education and Research (Project ECO2017-86305-C4-4-R). Fernando Lera-Lopez and Fabiola Zambom-Ferraresi acknowledge the financial support from Foundation Caja Navarra, Foundation La Caixa and UNED Pamplona (Project 2018-19). | en |
dc.format.extent | 34 p. | |
dc.format.mimetype | application/pdf | en |
dc.language.iso | eng | en |
dc.publisher | Elsevier | en |
dc.relation.ispartof | Decision Support Systems, 114 (2018) 18-28 | en |
dc.rights | © 2018 Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license | en |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Performance analysis | en |
dc.subject | Sport management | en |
dc.subject | Soccer leagues | en |
dc.subject | Sport success | en |
dc.subject | Bayesian model averaging | en |
dc.subject | Relative importance analysis | en |
dc.title | Determinants of sport performance in European football: what can we learn from the data? | en |
dc.type | info:eu-repo/semantics/article | en |
dc.type | Artículo / Artikulua | es |
dc.contributor.department | Economía | es_ES |
dc.contributor.department | Ekonomia | eu |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
dc.embargo.terms | 2019-10-01 | |
dc.identifier.doi | 10.1016/j.dss.2018.08.006 | |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/ECO2017-86305-C4-4-R/ES/ | en |
dc.relation.publisherversion | https://doi.org/10.1016/j.dss.2018.08.006 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | en |
dc.type.version | Versión aceptada / Onetsi den bertsioa | es |