Identification of Factors Determining Market Value of the Most Valuable Football Players
Bibliographic record
Abstract
Purpose: The problem of identifying the most important determinants of the market value of football players is quite well described in the literature. There are many works that try to identify these factors. Some of them are focused on variables to make a set complete and others are on models and methods. The aim of this article is to present the variables influencing the valuation and to build an econometric model valuing footballers playing on the forward position, taking into consideration the assumptions of the econometric modelling. Such an approach indicates managers as other sources for obtaining information. Methodology: Econometric models were used to verify the hypothesis formulated in this research. The database was created on the basis of variables presented on the website www.transfermarkt. de that presents the values of the most valuable football players in the world playing on the forward position. The Gretl program was used in the research. Findings: The literature review showed that there are many independent variables having an impact on the value of the player. There are also many different models used to valuate footballers’ performance rights. The results of estimation of models in the research indicated that such factors as Canadian classification points adjusted the market value of the team and dummy variables describing “goodwill” (only for the best players) had an impact on the market value of footballers’ performance rights. Limitations/implications: Information about different factors having an impact on football players’ market value could support the investment decision process of football managers. Originality/value: The results were part of a study concerning economics of sport, particularly processes of management of football clubs and valuation of intangible assets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".