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dc.contributorCoad, Alexander Jean – Luc
dc.creatorSerna Rodríguez, Maribel
dc.date.accessioned2019-01-28T21:17:51Z
dc.date.available2019-01-28T21:17:51Z
dc.date.created2019-01-28T21:17:51Z
dc.date.issued2019-01-28
dc.identifier.urihttp://hdl.handle.net/20.500.12404/13347
dc.description.abstractThe research question of this study was identify the variables that determine the market value of a high-performance soccer player, in the five major leagues around the world (Premier League, Bundesliga, LaLiga, Serie A and Ligue 1). The research question allowed to establish a hierarchy-of-hypotheses (see Table 1). Consequently, there are nine major hypothesis, 45 sub-hypothesis, and 20 sub-sub-hypothesis. The sample was conformed by 335 soccer players for the main five soccer leagues, distributed according to the proportional affixation for random stratified sampling. . The information for 45 control variables and the endogenous variable for 335 ssocer players was collected from the transfermarkt.de. website that is an online community (crowd wisdom). The 45 control variables that arised from the literature systematic review had having to be grouped in these major major hypotheses (smaller clusters or groups). For this purpose, these variables was categorized in talent indicators and external variables. Through this categorization, the 45 control variables were grouped together in five clusters according to the similarity between them. Finally, nine smaller clusters or groups were formed using divisive hierarchical clusters according to the dissimilarity between the variables. More variables had to be incorporated for some groups because the operationalization process for six variables generated other variables. In that order was structured a hierarchy-of-hypotheses (HoH), 45 variables take place to nine major hypothesis (groups), 45 sub-hypothesis (variables that belong for each group), and 20 subsub-hypothesis (levels of each variable). To answer the research question taking into account model uncertainty (variable selection) in a framework with 35 billion potential models, it was used a hedonic regression framework, and implement Bayesian model averaging (BMA) through Markov vii chain Monte Carlo model composition (����3 ). To deal with endogeneity issues, instrumental variable Bayesian model averaging (IVBMA) was implemented as well. In the latter case, the potential endogeneity between Market value and Performance was addressed using Performance as an instrumental variable and Coach and Change of team as instruments. Using the BMA mechanism, there is evidence to support the sub-hypothesis H1m (PIP=1.000), H3a (PIP=1.000), H5c (PIP=1.000), H6b (PIP=1.000), H1b (PIP=0.993), and H3c (PIP=0.992). Therefore, four variables (Performance, National team, Age, Goals, and Recognition under-21) are a value-drivers that generate a positive effect on the market value of the soccer player; and one variable (Age squared) is a negative value-driver that generates a negative effect on the market value of the soccer player. Using the IVBMA mechanism, there is evidence to support the sub-hypothesis H1m (PIP=1.000), H3a (PIP=1.000), H5c (PIP=1.000), H6b (PIP=1.000), H3e (PIP=0.997), and the sub-sub-hypothesis H7b1 (PIP=1.000). Therefore, four variables (Performance, National team, Age, First division, and Recognition under-21) are a value-drivers that generate a positive effect on the market value of the soccer player; and one variable (Age squared) is a negative value-driver that generates a negative effect on the market value of the soccer player.
dc.languageeng
dc.publisherPontificia Universidad Católica del Perú
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourcePontificia Universidad Católica del Perú
dc.sourceRepositorio de Tesis - PUCP
dc.subjectFútbol--Aspectos económicos
dc.subjectMercadeo
dc.subjectInvestigación cuantitativa
dc.titleVariables that determine the market value of a high-performance soccer player
dc.typeinfo:eu-repo/semantics/doctoralThesis
dc.type.otherTesis doctoral


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