Financial player valuation from the perspective of the club: the case of football

ABSTRACT

1. Rationale/purpose

Recent transfer periods clarified that the transfer fee paid for football players is a subject of social, economic and financial matter. This paper seeks to provide a comprehensible valuation method for athletes.

2. Design/methodology/approach

The aim of this paper is to provide an approach to determine the financial value of a player based on future payment streams. A theoretical model following the investment theory is presented and the results are illustrated within a fictitious case study.

3. Findings

The subjective value of an athlete is determined by club-specific expected future payment streams which require especially financial determinants. The value can be influenced by several factors: share of sporting success, merchandising potential or salary. Historic data can be used at best as an indicator.

4. Practical implications

The approach can serve as economic benchmark to calculate a club’s individual price limits for player valuation and provides managers an initial direction in transfer negotiations.

5. Research contribution

Recent models are missing factors such as future-orientation and subjectivity of a player’s value. This paper tries to add these elements to the existing literature. Moreover, it seeks to cushion the problem of uncertain valuation at the decision situation. Nevertheless, this study focuses on financial variables which must be supplemented by clubs with sporting factors.

KEYWORDS:

• Sports economics
• football
• finance
• soccer
• valuation
• income approach
• Monte-Carlo simulation

Acknowledgement

The authors would like to thank Lukas Richau as well as two anonymous reviewers for valuable comments on previous versions of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 This paper and the given remarks are limited on the case of men’s football and its particularities. However, with the consideration of different parameters, the model described in the following can also be applied to other sports and to women’s football.

2 If an employee can use its skills at least within another company, it is called general human capital. Specific human capital refers to the individual abilities that can only be used in a single company, but not apart from it.

3 In general, Transfermarkt.de values can be used as indicators to calculate a wider range of possible values. Nevertheless, it is important to point out, that those values are no subjective player values from a club’s point of view (which has usually more data available or subjective goals that cannot be judged externally), see, Ackermann and Follert (2018). Moreover, crowd-based values are often driven by public opinions or trends.

4 The regulations governing the “status and transfer of players” published by FIFA and UEFA in 2001 from the international framework establish the national transfer system in each country. As a result, the contract period of a player must be at least one year and a maximum of six years (Elter, 2012b).

5 Uncertainty exists regarding health, adaptabilities in the new environment, potential and the development, etc.

6 Mediators in professional football occur within the function of argumentation, similar to a neutral third party, in transfer negotiations, to facilitate between both clubs and to find an advantageous solution. In many countries, there is an additional third party taken part into the negotiations between the clubs (Wackerbeck, 2015).

7 Possible premiums: performance-related, per winning a point or personal trophies.

8 For further elaboration on mental health, e.g. in connection with external impacts, see Souter et al. (2021).

9 For a more detailed execution, see chapter 4.

10 It needs to be point out that the application of the plus-minus player rating to football might be rather of theoretical nature due to the team size of football.

11 Economic goals are club-specific determinants. While some clubs pursue the goal of liquidity, others act under the secondary objective of asset maximization by aiming for the highest possible transfer sums through the potential future sale of players. Typically, FC Porto is designated as “educational” club with the focus on training young, developable players and selling them at a profit – at least for the value of the projected future benefit. Financial restrictions can be determined by the participation of an (external) investor. Richau et al. (2021) elaborated, that foreign investors in particular in the Premier League are willing to pay a premium transfer fee for similar players compared to domestic investors.

12 SPV_S = Subjective player value of player S. PF_{S t} = (Future) Payment flows of player S (includes positive and negative payments).

13 Generally, to identify a period the whole calendar year has to be considered. Due to sector-specific characteristics, for the valuation of a professional football player it is rather recommended to determine a complete season (from July to June) as an entire period t.

14 The retailer’s profit is about 18 MU per jersey. The club management of D elucidates, that about 70% of the jersey sales take place by third party distribution and the remaining 30% through own sales.

15 Before the transfer of S, D received an annual amount of 3,300,000 MU from an equipment supplier N. After a transfer of S, A offers D a contract with an annual salary of 3,600,000 – starting from t = 2 – due to the promising development of S. The difference is imputed to the payment flow prognosis of S.

16 Before the transfer of S, D received 4,000,000 MU per season from the farmer sponsor partner. After the signing of S, the amount increases up to 4,400,000 MU. The difference is imputed to S.

17 This conforms a boost of 2% compared to the scenario without S. In the Bundesliga, the capacity amounts to 34,000 seats. In cup games this amount is reduced by 4,000 seats due to the prohibition of standing areas. The club accrues in Bundesliga 30 MU, in DFB-cup 35 MU, Europe league 40 MU and Champions league 50 MU per game. Included are proceeds such as gastronomy, renting of loges, as well as costs for security issues and personal.

18 For every absolved game, S earns in Bundesliga 5,000 MU, German cup 7,000 MU, EL 10,000 MU, CL 15,000 MU. If the goals of the club management are achieved within a season, they pay a premium of 2,000 MU for every point at the final table of the Bundesliga.

19 Accompanying transaction costs due to a transfer must be aggregated to the marginal price, as for example for drawing up the contract (provision for the player agent, etc.).

20 The amount of the so-called market value insurance underlies another independent complex valuation by the insurance companies, which is determined by factors such as age, history of injuries, etc.

21 Due to costs for hotels, flights and train, negative payments of approximately 800 MU per player for every away match are assumed.

22 For a triangle (a,b,m)-distributed random variable X, which includes the distribution function:

$\mathrm{F}\left(\mathrm{x}\right)=\{\begin{array}{c}\frac{{\left(\mathrm{x}-\mathrm{a}\right)}^2}{\left(\mathrm{b}-\mathrm{a}\right)\left(\mathrm{m}-\mathrm{a}\right)}ifa<x\le m\\ 1-\frac{{\left(\mathrm{b}-\mathrm{x}\right)}^2}{\left(\mathrm{b}-\mathrm{a}\right)\left(\mathrm{b}-\mathrm{m}\right)}ifm<x\le b\end{array}$ And the realization x with the given random number z ∈ (0,1) results in a value (Waldmann & Helm, 2016).

$\mathrm{x}=\{\begin{array}{c}a+\sqrt{\mathrm{z}\left(\mathrm{b}-\mathrm{a}\right)\left(\mathrm{m}-\mathrm{a}\right)},forz\le \left(m-a\right)/\left(b-a\right)\\ b-\sqrt{\left(1-\mathrm{z}\right)\left(\mathrm{b}-\mathrm{a}\right)\left(\mathrm{b}-\mathrm{m}\right)},forz>\left(m-a\right)/\left(b-a\right)\end{array}$

23 It is distinguished between objective and subjective probabilities. Objective ones exist by dicing. In real decision situations like the player’s valuation, exclusively subjective ones are qualified. This probabilities base on an individual assumed degree of occurrence (Riessen, 2010).

24 Monte-Carlo simulation, see, e.g. in the context of valuation theory, Coenenberg (1970).

25 Estimated value and standard deviation for normal distribution and most frequent, lower and upper marginal value for the triangle distribution (Götze, 1993).

26 Experts of the club are estimating the probability distribution of the uncertain variables. Based on the deficiency of information, we assume general processes such as normal, triangle and equal distribution (Hering, 2017).

27 Simulated values (quantile of the normal distribution) can be determined in the program Excel with functions such as NORMINV (random variable, mean, standard deviation).

28 For single components of the payments flow, we use the betaPERT-distribution, because this distribution is well-suited for unknown variables with few empirical values (Davis, 2008). The density function is $\mathrm{D}\left(\mathrm{x}\right)={\displaystyle \frac{{\left(\mathrm{x}-\mathrm{a}\right)}^{\alpha -1}{\left(\mathrm{c}-\mathrm{x}\right)}^{\text{β}-1}}{\mathrm{B}\left(\alpha ,\text{β}\right){\left(\mathrm{c}-\mathrm{a}\right)}^{\alpha +\text{β}-1}}}$ with$\alpha ={\displaystyle \frac{4\mathrm{b}+\mathrm{c}-5\mathrm{a}}{\mathrm{c}-\mathrm{a}}}$ The probability function is I_Z($\alpha ,\text{β})$ with $\mathrm{z}=\left(\mathrm{x}-\mathrm{a}\right)/\left(\mathrm{c}-\mathrm{a}\right)$.

29 The simulated values (quantile of the betaPERT-distribution) can be determined with the program Excel and the function BETA.INV (random variable;$\alpha ;\text{β}$; Max; Min).

30 The results are depending on the individual estimated distributions and parameters.