Sport Results and Footballer’s Performance Rights’ Valuation

Abstract

One of the most important group of assets in the balance sheet of an enterprise is the group of intangible assets. The asset situation of a sport enterprise is particularly complicated as such a company has an unusual type of intangible assets-players' performance rights. There are many problems with the valuation of such assets. This article presents the econometric approach to solving a valuation problem. Much research takes into account the possibilities of using econometric tools for valuation of the economic value of a professional football player's performance rights. The most important methods were presented in the paper of Trequattrini, Lombardi and Nappo (Trequattrini et al., 2012). The main goal of this article is to build econometric spatial models to explain the market value of footballer's performance rights according to players' sports results divided into homogeneous groups: goalkeepers, defenders, midfielders and forwards. The hypothesis of this research is that sports results influence the market value of footballers' performance rights. Hence, there are four additional hypotheses. For example, one of them concerning forwards will be as follows-the market value of a footballer's performance rights is dependent on the number of goals, the number of assists and the market value of the team. The following independent variables will be used: goals, assists, performance, yellow cards, red cards, club position in the league and the market value of the club in the whole research. Linear and non-linear models are used to test these hypotheses. The data set includes information about all players' game activity, taken from the Bundesliga (professional association football league in Germany) from the 2013/2014 season. The result of the analysis will identify variables closely tied to the market value of the football player's performance rights (footballers) and measure the goodness of fit of a statistical model. The study refers to the research conducted in 1999 and indicates new possibilities for the use of common tools, such as spatial econometric models. It is also a contribution to further research on the properties of the models.

Determinants of football players’ valuation: A systematic review

“…Out of our 111 models, the most frequent method was Ordinary Least Squares with 94 specifications, many of which used panel regression with fixed or random effects. Some other methods were used to correct biases or non‐endogeneity issues: ‐Generalized Method of Moments (seven specifications) was used to handle endogeneity between the valuation and appearances of foreign players (Dimitropoulos et al., 2018), ‐95% quantile OLS regression (3), was used to study the higher segment of the transfer market and the superstar effect (Franck & Nüesch, 2012; Herm et al., 2014), ‐Feasible Generalized Least Squares or Generalized Least Squares (3), were used to handle heteroscedasticity (Majewski, 2016, 2021), ‐Tobit models (3), were used to handle selection bias in the transfer fees, that is, only transferred players fees are available while we do not know the dependent variable for players not transferred for a fee (Carmichael et al., 1999; Ruijg & van Ophem, 2015), ‐Two‐stage least squares (1), were used to handle endogeneity issues (Serna Rodríguez et al., 2019). …”

“…(2019) only used an age variable, but it was stratified thus incomparable. Finally, Majewski (2021) used a parsimonious equation with three variables: assist + goals , team valuation and a binary variable for brand . The last preliminary remark on the analysis of the significance and sign of the 10 variables is that the significance levels detailed by every article can be quite different.…”

“…Indeed, the impact may depend on the nationality of the player because playing for one of the best international teams such as France, Germany, or Brazil is associated with high competition. Some studies have thus attempted to take these discrepancies into account by using variables such as FIFA rank (Balliauw et al., 2022; Majewski, 2016, 2021; Wicker et al., 2013).…”

Intellectual capital and market valuation of footballers

“…The equation 1 describes the panel data model with the footballers' fixed effects, the club's fixed effects and the effects of player-year and club-year interactions, containing the variables of intellectual capital described in the literature as determinants for a player's market value: The variables were chosen based, mostly, on the articles of Butler (2018), Carmichael et al (2000, Kiefer (2012), Lucifora and Simons (2003), Majewski (2015Majewski ( , 2016 and Metelski (2021). As dependent variable, the logarithm for the player's market value per year was used -according to the inflation index IGP-M (FGV).…”

Using the product life cycle theory in the valuation of footballers’ performance rights of chosen Polish players

“…Nieco inna sytuacja zawodowa dotyczy bramkarzy -zawodników, w przypadku których wartość rynkowa w dużej mierze zależy od ich doświadczenia (na które wpływają wiek i liczba występów w meczach ligowych i pucharowych [Majewski, 2015]). W związku z tym za interesujący przypadek można uznać kształtowanie się wartości karty zawodniczej Wojciecha Szczęsnego, który został zobrazowany przy pomocy funkcji potęgowej w tab.…”