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Soccer is among the sports with the most followers globally. Recent studies have shown that over four billion people follow this team sport. Because of the sport’s popularity, the market for professional footballers has grown in recent decades, with players now earning more than a hundred million euros. Compared to the average inflation rate, these statistics are significantly higher than previous trade numbers. For example, Paris Saint-Germain smashed the world transfer record by signing Neymar from Barcelona during the summer transfer window of 2017. The French club paid a whopping transfer fee of 489,228,117 euros, or 222 million euros, to Football Club Barcelona to sign Neymar, who was touted as the most talented. The transfer fee included part of his contract release clause and agent fees (Marca, 2021).
Aside from Maradona’s and Neymar’s transfers, the world of football has experienced rapidly increasing transfer fees. The rapid increase in transfer fees has been associated with player wages or salaries, which gradually increased over the past decades. In sports economics, determining a football player’s wage may be challenging since a footballer’s wage bill may be a function of many variables. It was traditionally conducted through qualitative analysis before the information era, partly due to a lack of uniformity in the collection of football player statistics and data, making it even more challenging to compare the performance and skills of footballers scientifically. However, with the introduction of football statistics gathering and dissemination in the late 1990s, the amount of information generated has constantly increased (Yaldo & Shamir, 2017). A variety of performance metrics and athlete compensation are currently available in public football statistics.
Superstar football players make a lot more money than regular players, not to add the money they get from ticket sales, memorabilia, and broadcast deals. Due to the limited supply of superstar footballers, this effect is amplified, resulting in higher salaries through monopsony rents. Monopoly pricing rents are expected in a labor market when multiple football clubs compete for a limited number of personnel. In the monopsony football economy, football teams fight for the services of scarce elite athletes, leading teams to raise remuneration to stay competitive with other clubs and hire great players.
When a footballer’s net wages rise, their performance rises as well. On the other hand, salary disparity has been shown to negatively impact a team’s performance, with a football player’s output reducing as the income gap between him and other players in the same team expands. Furthermore, the salary of a football player directly impacts coaching decisions, as seen by the fact that trainers employ players with higher salaries in ways that are negatively associated with their on-field performance, as opposed to players with lower salaries (Yaldo & Shamir, 2017). Other forms of discrimination, such as bias based on nationality, are significantly less widespread than wage discrimination.
While the player’s amount of labor has little relevance to salaries, it has been established that a football player’s earnings are influenced by the past season’s performance, international caps, and goals scored. While these factors impact a football player’s wages, it’s plausible to suppose that other factors such as passing ability, free-kick accuracy, pace, and tackle ability also impact. Because these many skill traits can be combined to form a whole image of a player’s talents, comparing just a handful of them may not adequately reflect the player’s true worth. Many fans are unaware of the sport’s economic repercussions. However, with rising criticism of footballers’ excessive salaries, knowing this hot topic is critical. The current study examines the factors that contribute to such high remuneration.
- What are the determinants of the professional footballer’s wages?
- How is age associated with footballers’ wages?
Correlational research was carried out to determine which factors were significantly associated with professional footballers’ wages globally. The study involved collecting and compiling secondary data from various websites like transfermarket.com and sofifa.com, which provide reliable and up-to-date data about professional footballers. Multiple linear regression analysis between the dependent variable and its predictors to determine if there was a significant association.
For this study, data on football players were collected from sofifa.com and transfermarket.com and combined into a single Excel file. Each player’s performance and skills, as well as their salary, are included in the statistics. This study considers the football club’s gross earnings as agreed between the club and the player’s agent. It does not view the player’s other sources of income, such as advertising fees and sponsorship deals. FIFA, UEFA, and other international football governing bodies have imposed strict rules requiring football clubs to report accurate wage data.
As a result, the clubs will be able to meet financial fair play requirements. If these criteria are strictly followed, this data can be trusted. The data from sofifa.com was obtained through a web crawler that gathered information on each athlete during the 2016 season. The dataset was compiled by football experts who examined each player’s abilities to simulate the players in video games1, thus scouting. Many researchers interested in studying various aspects of the game used the data. Prasetio (2016) and Shin and Gasparyan (2014), for example, used it successfully to predict the outcome of football matches. Some of the variables used in this study include age, performance, wage, dribbling, agility, value, acceleration, and aggression.
Data Reliability and Validity
Validity and reliability are significant concepts to consider to determine the research quality. Reliability measures the consistency of the data when collected by different people or different instruments. At the same time, validity is how the results measure what is supposed to be measured. The data was not collected from a single source. It was collected from different sources with all the information scientists and researchers seek. Sofifa.com and tranfermarket.com have used several new and old data sources, including surveys, cohort studies, etc. Some of the data collected by these sites come from states, while others originate from field research. That way, the data may be valid and reliable.
Before running the multivariable linear regression, the descriptive analysis revealed the target variables’ patterns, trends, and distribution. Exploratory data analysis was also done to visualize the data. The target variable (wage) is continuous. Similarly, the predictor variables are endless. SPSS Version 26 was used in the analysis.
Exploratory Data Analysis
Table 1: The descriptive statistics
|Std. Error of Skewness||.030||.030||.030||.030|
|Std. Error of Kurtosis||.059||.059||.059||.059|
The table above shows the dependent variable’s summary statistics and some of the predictor variables used in this study. Football players’ average wage per week is (M=24.9313K, SD=32.95220), while the median salary is 15.0K per week. Since the average wage is larger than the median wage, the variable wage is positively skewed or skewed to the right. The high standard deviation of the means indicates the wage disparities among professional footballers. Similarly, there are considerable disparities in age, the market value (value), and other predictors used in this study. These disparities might influence the wage structures of different teams in the major leagues across the globe. The following histogram depicts the distribution of the wage variable.
One of the significant regression analysis assumptions is a linear relationship between the predictors and the dependent variable. The following graphs show the relationship between wage and predictors like value and age.
Based on the figure above, there seems to be a positive linear relationship between wage and age. As age increases, wage also increases. The exact relationship is observed between wage and value. In other words, as the transfer market value of a player increases, their wages also increase ceteris paribus –other factors kept constant.
Table 2: The model summary
|Model||R||R Square||Adjusted R Square||Std. Error of the Estimate|
From the table above, it can be confirmed that there is a statistically significant association between wage and its predictors like age, value, agility, and aggression, among others. Since the p-value is less than the significance level, alpha=.05, the relationship between wage and predictors is statistically significant.
Table 3: Regression analysis results
Table 3: Regression analysis results
|Model||Sum of Squares||df||Mean Square||F||Sig.|
a. Dependent Variable: Wage
b. Predictors: (Constant), Crossing, Age, Value, Aggression, Acceleration, Potential, Agility, Dribbling
68% of the variation in professional footballers’ weekly wages is explained by its predictors like Crossing, Age, Value, Aggression, Acceleration, Potential, Agility, and Dribbling
The present study found that as football players get old, they tend to earn wages. With age comes experience, improvement in different skill sets, and another critical game aspect. For this reason, a football team may decide to renew the contract of players to retain them in their clubs. However, as football players become older, say (over 30 years), their physicality and work rate may reduce. For this reason, they do not earn much. The transfer market value is also positively associated with a player’s wages. As the value of a player increases, their wages also increase. Over the years, it has been shown that talented players tend to attract substantial transfer fees. Such players may be perceived to have more significant potential to succeed in competitive leagues worldwide. For that reason, clubs may offer them huge wages to convince them to sign or continue playing for them.
Similarly, players who are more agile, aggressive, and have greater potential may also be paid more. These finds are consistent with other studies’ findings. Yaldo & Shamir (2017) found that a football player’s wage is determined by various elements that aren’t directly tied to their performance or abilities. The suggested method in this study is empirical, with the athlete’s wage determined only based on their measured talents and performance. Income disparity has been found to have a detrimental impact on football player performances. The negative effect increases as the disparity between the player’s salary and the other team members develop. Income disparities have been found to negatively impact footballer performance, with the negative impact rising as the disparity between the player’s salary and that of the other team members grows. Using a quantitative method as a benchmark for football player wages may influence the team’s conduct.
Trichard (2021) also found that we could determine whether pitch performance was a determinant and, if so, how significant it was using our econometric analysis. The most critical factor turned out to be the player’s grade. The coefficient was significantly larger than the sum of all other variables, indicating that the better a player is, the more money he will earn. This is the case because this variable is a composite of all other variables. Then there are awards such as Man of the Match, goals, and assists, which significantly impact the players’ earnings. These findings support the notion that players’ performance substantially affects their pay.
Even though this study provides valuable insights into the factors associated with professional footballers’ wages, it is limiting in many ways. First, major football leagues like La Liga, Ligue One, Serie A, Premier League, Bundesliga, and others may put varying caps on the maximum salary players get. The present study did perform a separate analysis for these leagues. For this reason, these findings may be general and cannot be applied to a specific league. The study also did not consider the position of the players in the field. In football, players are categorized as goalkeepers, defenders, midfielders, and forwards. These positions can attract different salaries based on performance. Therefore, the study ought to have treated this variable as a covariate.
Wages for footballers are determined by various factors, including age, worth, and potential. It’s also possible that other factors, such as passing skill, pace, and tackle ability, impact pay. Many performance characteristics can be combined to offer a complete picture of a player’s abilities. A quick comparison of a few may not fully reflect their actual value. Many sports enthusiasts are oblivious to the financial implications of their beloved pastime. With the growing backlash against footballers’ exorbitant earnings, it’s crucial to understand this controversial topic.
Salary discrepancy, on the other hand, has been found to negatively affect a team’s performance, with a football player’s output dropping as the income gap between him and the rest of the squad widens. Besides, the salary of a football player directly impacts coaching decisions, as seen by the fact that coaches employ players with higher incomes in ways that are negatively related to their on-field performance, as opposed to players with lower salaries. For these reasons, identifying the factors associated with players’ wages is invaluable to football clubs. It helps policymakers and the management of the teams to tailor the wage bill to attract talent to the club while remaining economically viable and competitive in the game.
- Marca, (2021). Neymar’s signing cost PSG a total of 489m Euros. MARCA. Available at: https://www.marca.com/en/football/ligue-1/2021/09/04/6133c872e2704e976e8b45e6.html [Accessed April 27, 2022].
- Trichard, T. (2021). What are the main determinants of professional soccer players’ wages? An Examination of the top 5 European Leagues. Dissertation.
- Yaldo, L. & Shamir, L. (2017). Computational estimation of football player wages. International Journal of Computer Science in Sport, 16(1), 18–38.