Over the years, football has emerged as one of the most popular sports globally, which the rise in its popularity has accompanied. Football clubs are no longer recognised simply as sports organisations but also as huge commercial companies. Buying and selling players has become an important activity in this sector. The current study sought to investigate the relationship between a club’s expenditure in the transfer window and the economic performance of a football team in the English Premier League and finds significant positive correlations between the economic performance of a club and the squad size, expenditure on the transfer market, and the market value of a club. The analysis also finds that the squad’s average age has a significant and negative correlation with the economic performance of that club. However, using regression to investigate causation reveals that none of the variables significantly predict a football club’s economic performance in the English Premier League. In other words, even though the expenditure on the transfer of players might correlate with economic performance, it does not cause it. The case is the same for squad size, the club’s market value, and the average age of the players.







Table of Contents

Abstract 2

1     Chapter 1: Introduction. 4

1.1      Background and Rationale. 4

1.2      Aim and Objectives. 5

2     Chapter 2: Literature Review.. 6

2.1      Overview.. 6

2.2      Teams and Their Economic Performance. 8

2.3      Factors That Determine the Transfer Price of a Player 10

2.4      The Football Labor Market 13

2.4.1       The Bosman Ruling. 14

2.5      Valuation Factors. 15

2.5.1       Match Day Revenue. 15

2.5.2       Broadcast Revenue. 16

2.5.3       Commercial Revenue. 17

2.6      Relationship Between A Player Transfer Fee and Economic Performance of Teams  17

2.7      Theoretical Framework. 18

3     Chapter 3: Methodology. 20

3.1      Data and Data Issues. 20

3.2      Variables and Model 21

3.3      Data Analysis Plan. 21

4     Chapter 4: Data Analysis. 22

4.1      Correlation Analysis. 23

4.2      Diagnostic Tests. 24

4.2.1       Normality. 24

4.2.2       Homoscedasticity. 25

4.2.3       Multicollinearity. 25

4.3      Regression Analysis. 26

5     Chapter 5: Discussion. 28

6     Conclusion. 34

7     Bibliography. 35

8     Appendix. 41

8.1      Data Source. 41





1         Chapter 1: Introduction

1.1        Background and Rationale

The English Premier League has become one of the most popular leagues globally, and this has largely been due to the economic value of football as a sport, not just in Europe but also in all other parts of the world. Football has become a lucrative sector and a sector that has grown rapidly in the past decades (FIFA, 2018), which has necessitated studying the economics of football. Studies such as Downward and Dawson (2000) argued that the activities around football, such as the building of stadia, the holding of matches, buying and selling of players, and advertising, among many other activities, involve expenditure and generating of revenues. In part, this is why the study of the economics of football is interesting to the researchers.

English Premier League was formed in 1992 after separation from English professional football due to financial considerations (Premier League, 2011). English professional football was established in 1888 and had four divisions, a total of 92 participants, which was stable till the separation. Subsequently, the English Premier League has grown in stature to a remarkable force that by 2008 and 2009, the total income was at €2.326 billion and a €93 million profit, respectively (Premier League, 2011). Over the years, there has been a spontaneous increase in revenue. The 2018/ 2019 annual revenue was at €28.9 billion, indicating a 2% growth from 2017 and 2018 (Planet Football, 2020). In relation to the latest Deloitte report, England is still leading as the richest in revenue, with € 5,851 million (Deloitte, 2019). English clubs are exceptionally active on the international player transfer market, with premier league clubs spending over £714 million, a striking £298 million of which remained in the UK (Planet Football, 2020). Manchester United is the leading spender with £192.6 million and a significant one on Harry Maguire with £80m from Leicester City (FIFA, 2018). However, Manchester has excellently performed in the league, and the expenditure on players is worth it.

The so-called transfer market involves the buying and selling of players, which, as Fürész (2018) explains, aims to make profits on the sale of players and strengthen the squads by buying new players. The players have different market values, mainly based on their talents/skills or on-field performances, which is why some players cost higher than others. The question, however, is whether or not these transfer prices have any impact on the economic performance of a given football club. This study’s findings would be critical in understanding the significance of spending large amounts of money by clubs in the Premier League during the transfer windows.

1.2        Aim and Objectives

This study aimed to investigate the relationship between a club’s expenditure in the transfer window and the football team’s economic performance in the English Premier League. The study also sought to achieve the following objectives:

  1. To examine the relationship between the profitability of a club and their expenditure in the transfer window
  2. To investigate the relationship between a club’s market value and its economic performance
  3. To investigate the significance of squad size, age of the players, and the player transfer prices in a football club’s financial performance.





2         Chapter 2: Literature Review

2.1        Overview

The introduction of European top football players such as David Villa and Thierry Henry popularised football and brought more money to football teams. Despite some of its members consistently reporting annual losses and large debts, revenue streams in professional sports leagues have continuously reported increases (Deloitte, 2013). As a result, UEFA implemented a regulation (Financial Fair Play) to improve the financial and economic capability of failing football clubs under the remit. According to Forbes, the EPL is considered the wealthiest league with an estimated revenue of £5.15 billion (2018/2019). To safeguard its key revenue stream, the twenty football clubs indiscriminately act in the sale of broadcast rights where the quantity of broadcasted games is restricted to 380 matches each season (Deloitte, 2011). Contrary to other broadcast rights in other European leagues, the clubs are prevented from selling the right to their games directly to the broadcaster.

As football’s interest continues to grow, the amount of money invested in transferring players is increasing. For instance, Adidas signed a 10-year sponsorship deal with Manchester United in 2010 worth €1 billion. Barclays renewed a three-year partnership that will see the multinational bank continue as the league’s official banking partner (Premier League, 2019). Other factors raising the transferring fees include sponsorship money received by top teams, the rising ticket revenue and broadcasting rights, and players’ salaries. Real Madrid promoted Gareth Bale to among the most expensive footballers after signing the player at an estimated value of €91 million and €100 million. Manchester United exceeded the record by buying Paul Pogba from Juventus at €105 million. In the history of their purchases, the club had never recorded such high transfer prices; the price continues to increase every season. Van der Berg (2011) believes that the competitive balance and the league’s collective objectives trigger the team’s behaviour concerning the transfer of players.

According to the researcher, football clubs are dependent on one another to call spectators and compete, taking into consideration each team’s quality. The changing dynamics in the industry have been caused by the close link between financial and sporting success. Trauttman & Traxler (2010) add a different aspect to the mechanical of transfers. The researchers establish a reverse pricing concept in relation to the online auctions of virtual football players, comparing two effects; the psychological reference-dependent effect and the mechanical effect of the auction. When put to practice, the aspect presents different candidates as reference points, thus clouding investors and sellers’ judgment, leading to an increase in transfer prices. Robinson and Simmons (2014) investigated revenue sharing impacts on talent distribution using data from the English Premier League from 1969 to 1995. The study showed that a large number of quality players agreed to transfer because of the success of the football club between 1983 and 1995. With a revenue-sharing policy in place until 1983, a positive impact on the economic performance and the competitive balance between the teams was recorded.

According to Cox (2016), participants of other European competitions such as the Europa League access revenue that has not been shared among the remaining teams, normally termed as “weak” in the Premier League. Additional competitions, however, results in a greater strain on the financial resources and the playing squad. In this case, additional revenue is expected to compensate for the requirements of meeting fixtures of an extra match. The researcher assumes that by easily meeting competing requirements in more matches, the Premier League’s policies will promote competitive balance. Galloppo & Boido (2020) suggest that the investor’s mood has an impact on the player transfer price, the willingness to invest, and the market value of a club. Average returns were mostly negative on days after a loss than a win, and on days where the club won, the market was hardly affected. From a financial management perspective, March (2014) argues that players are important assets for their teams and are simply capitalised to fit into financial reports. March believes the player transfer prices are irrational as rich football clubs inflate the market while clubs with low market value shares sell players to settle their “bills”.

Numerous researchers have investigated the impacts of match outcomes on the leagues’ football clubs’ stock prices. In most cases, the out-turns were similar to the short-term impacts on the stock price, which were positive after winning a match and negative after losing a match (Stadtmann, 2006). The above results were mostly recorded in the knock-out matches and ones that were played towards each season’s end. In relation to the market value of a club and its economic performance, Footaki et al. (2007) investigated the impacts of transfer prices on the market value. Findings from the study indicated that sales, in the short run, had a positive impact on the selling price of the selling football club as positive impacts were found in both acquisition and sales at the negative interval. Investors did not believe that the acquired players could earn back the transfer fees. However, they believed it could be acquired through more prize money from European competition and higher ticket revenues. From the study, a club recorded higher stock prices on the day of the transfer deal and the day before.

2.2        Teams and Their Economic Performance

Over the years, football has emerged as one of the most popular sports in the world. With the rise in popularity, football clubs are no longer recognised as sports organisations. Many of them have become huge commercial companies. This is evidenced by their ability to raise millions of euros in revenues. According to Delloitte (2016), an analysis of the top thirty European football clubs shows that they generate approximately 6.6 billion euros. Despite their growth into economic entities, Pyatunina et al. (2016) observe the absence of efficient football clubs and teams’ efficient analysis approaches. As a result, the authors recommend the use of financial efficiency and sports efficiency. The recommendation stems from the fact that financial analysis allows examining the teams/club’s ability to make profits. On the other hand, sports efficiency allows the analysis of the teams’ success or clubs.

Conejo et al. (2007) seek to examine the effects of football on a given region’s economy. While examining football’s economic impacts, the authors identify the introduction of a business model in football clubs’ management. In Spain, the emergence of limited sports companies traces back to the late 1980s and early 1990s. The era was marked significant changes in Spain’s social development and the conception clubs such as Real Oviedo and Sporting. While the study focuses on Spanish football clubs, it provides an insight into the transformation of football clubs into sports companies. The emergence was marketed by football clubs signing contracts with pay-per-view television companies. These contracts were able to improve the popularity of football clubs at a global level. In the case of Asturian clubs in Spain, their economic future was not secured after the transformation. The failure was seen through a continued decline in finances and poor performance in sports. Nevertheless, Conejo et al. (2007) provide a significant overview of the transformations that occurred to create a perception of football clubs as financial and or economic entities.

Many scholars have analysed the emergence of economics in English football clubs. For instance, Lee (1998) seeks to examine English football’s political economy during the 1990s decade. The author compares English football in years before the 1990 decades. During the 1990s, English football displayed a wide range of inequalities and short-termism. Issues such as greed of society were also observed. Such characteristics were attributed to the growth of London’s financial markets. According to the supporters, the new paradigm meant that being a supporter would be rather expensive. As a result, passiveness and individualism were the main dimensions displayed by the supporters. The passiveness and individualism led to a struggle between supporters and the clubs. Perhaps the best example of such struggles was illustrated by Manchester United PLC. The football club was extremely instructive about their potential for supporter’s democracy in the club’s activities. The struggle was seen in cases where some individuals embraced the financial disciplines that resulted from the floatation of the financial markets. The author concludes that the 1990s era was an era in which English football served as an avenue to legitimise inequalities between football clubs. These inequalities have been expressed by other scholars such as Moore et al. (2012). Today, such inequalities still exist where higher financial authority clubs can dominate the sport and increase their financial performance.

While the inequalities continue to exist, many studies have called for the need to examine sports management. Hence, Moore et al. seek to examine the business parameters that can be found in sports. Therefore, the study finds that many of the parameters applied in football management relate to or can be found in SMEs. These parameters include entity size, mentality and turnover rates. At the same time, these parameters are not easily observable. The difficulty in observation of parameters limited the consideration of football club as economic entities. Therefore, it is recommended that more analysis of football clubs’ management be conducted through SME models’ prism. The use of SME models will allow identifying business parameters that create a different perception of football teams or clubs.

2.3        Factors That Determine the Transfer Price of a Player

Football clubs are in constant competition to establish dormice in the field. At the same time, these teams are continuously trying to improve their economic performance. While their competition is necessary, it is critical to consider player transfer value in the clubs’ economic performance. According to Dobson et al. (2001), several scholars have applied different models to analyse player value determinants. In the Indian Premier League, hedonic pricing models have been applied to assess different players’ value. While the study provides an insight into the transfer value issues, it is critical to recognise its focus on cricket. As a result, the applicability of this study to football may be significantly limited.

Over the last decades, the football transfer window has experienced numerous changes. According to many supporters, the actions in the recent transfer windows can any be termed as crazy. The aspect is evidenced by the shattering of the world record transfer fee more than once. From a global perspective, teams in North America are constantly watching their turnover and revenue streams. Such actions have allowed them to sacrifice the signing of the so-called high-quality players to save more funds. At the same time, trends in the football transfer market are spiralling out of control. Such market characteristics can be best seen through the value put on English payers. The trend is likely to continue, especially due to the UEFA’s policy on the number of home-grown talents in a champion’s league side must field. As a result, the price of English players has risen to another level. For example, Liverpool had to pay over 17.5 million pounds for Glen Johnson in 2009. Likewise, players such as Kyle Walker and Kyle Naughton were valued lower than 10 million pounds. The value is presented even though these players have championship experience. Additionally, the transfer of Cristiano Ronaldo and kaka is often examined. With these two great talents, their value had huge significant differences. It raises a question of how managers value their players. Fundamentally, how does the manager’s value affect the transfer price of a player? (Pyatunina et al., 2016; The Arsenal Review, 2009)

Based on the football transfer market analysis, several factors influence player value in the transfer market. These factors include the squad status, premium position, age, league factor, adaptability and iconic status. Depreciation has also been recognised as one of the main factors influencing the transfer price of a player. Depreciations has been identified as an area where a few managers pay attention. There have been cases where managers have paid higher value for players older than 30 years. An instance of such a case was the value placed on Shevchenko at 30 years. The player was valued at 32 million pounds which were considered an over price. Hence, depreciation is considered a deal that amounts to a rental factor. For instance, Alex Ferguson acquired Carlos Tevez’s services for two years for 10 million pounds. The depreciation concept explains why some teams tend to sell players while they are at the peak of their career (The Arsenal Review, 2009).

Squad status refers to the role of the player in the team. Some players are considered indispensable, while others are considered back up to the first team. Valued squad members often have a high transfer value since the team will need to find their replacement. However, the value of each player may vary due to age. Players at the age of 21-25 are often considered indispensable. As a result, the value is likely to be higher than those within the age bracket of 16-20 and those within a higher age bracket. While it is necessary to value players based on their contributions, The Arsenal Review (2009) states that talent, premium position, and iconic status significantly impact players’ value.

Premium positions entail entails the positioning of the player in the field. In particular,  players positioned in the final third of the field are often referred to as the premium. As a result, the transfer price of these players is higher than players in other positions. Nevertheless, there have been some anomalies within the transfer market; Dani Alves has been considered one of the most expensive full-backs. Recent Liverpool on a goalkeeper Alisson Becker and defenders has suggested the presence of anomalies in the relationship. Another significant anomaly is the price paid by Manchester United to acquire Harry Maguire and Bruno Fernandez’s services.  However, the player’s adaptability to the new environment and the level of completion in a new league holds a significant impact on the overall value of the player (The Arsenal Review, 2009).

2.4        The Football Labor Market

Here, the labour market is explained under the transfer market, how transfer fees are determined, and player salaries set. In studying the economy of sports, there is an ironic ground for the application of tightly engrossed microeconomics and economics equally to the team and individual results (Szymanski, 2013, p. 53). Recently, the English Premier League clubs have augmented their transfer expenses since it is commencement. Mostly, transfer expenses are being funded by filthy rich owners who started acquiring majority stakes in clubs. Transfer fees in the world of football are reserves in elusive fixed assets involving the expense incurred in obtaining the player’s registration, which sticks the player to the club for a lifetime of their contract (FIFA, 2018). Football contracts define a schedule for the wage payment and show the connection amidst expenditures and the team’s performance.

Moreover, transfers occur when players under contract move between clubs. They are done when there is a transfer window and upon set rules by the governing body. Mostly some compensation is done as part of the rights of the player. When transfers occur, the old contract is terminated, and a new contract is negotiated with the new club. Though, American, Canadian and Australian sports trade player’s contracts.  According to FIFA (2018) research, there were 15,049 transfers of male players with a total fee of US$7.1 billion and 577 international transfers of female players for US$493,235.

Furthermore, the normal labour market allows employees to resign from their jobs whenever they feel like they are fixed to a club with a contract, unlike in the football labour market. Nonetheless, the past 60 years have remarkably seen the labour change, with more players gaining more power. According to Sloane (1960, P.181), till 1961, the transfer market practised retain and transfer system where there was a fixed maximum salary, and lest the club consents to trade a player, they were destined to the club provided they wanted the player portraying characteristics of a slave market. Subsequent years realised a change in the labour market, and the maximum wage was stopped in 1961. In 1963, the Retain and Transfer system changed, but a transfer fee was still placed according to the club. As a result, Sloane (1961, P.182) elude that the retain and transfer system became the cornerstone of the football labour market, and despite many years of stress by the union, its simple principle that a club has the right to ask for payment for the loss of service of a player is still the same. The Retain and Transfer system ensure minimal variance between clubs in terms of star players. Besides, the Retain and Transfer system allow small clubs to receive a transfer fee when big clubs buy their players. However, this contended and enticements were given to train and augment players’ talents since the clubs were sure to be rewarded for their investments in the players’ growth.

2.4.1       The Bosman Ruling

In December 1995, the European Court of Justice arrived at a verdict named the Bosman Ruling, which stopped the prevalent Retain and Transfer System (CJEC, 1995, P. 4921). The ruling portrayed football as an economic activity and, therefore, subject to the provisions of the treaty of Rome concerning liberty of movement. This allowed football players to seek employment whenever their contracts expired without the club being recompensed. The Bosman verdict implied that clubs in the European Union (EU) could hire as many EU citizens and citizens from countries that have signed up as EU as they wanted. Before this ruling, a maximum of three foreigners could be allowed. Citizens outside the Bosman ruling are still being affected by national regulations, which vary from one football association to another. For example, in the UK, according to Osselaer (2008), a player must have 75% participation in international matches in the past two years for which their country has played, and the country must be in the FIFA top 70 to gain the country’s work permit.

2.5        Valuation Factors

The main goal for any football club is to succeed in its sports and to satisfy its supporters. To succeed in this, the football club must grow and gain talented players. Inferring to Carmichael and Thomas (1993, P.76), sporting success comes with a cost. Teams seeking to acquire new players focus on team reinforcement where the aim is to attain success. According to the UEFA Financial fair play, clubs need to avoid getting losses. Amir and Livne (2005, P.3) investigated football players’ contribution and investment on the club’s revenue and profit and realised a positive impact that did not last past two years. Szymanski (2013, P.55) suggests that when club performance increases, revenue growth increases, which increases attendance, higher prices on tickets, and an increase in sponsorship and TV income. League positions formed 82% of the difference in revenue among clubs in 1996 and 1997. Similarly, looking at clubs’ performance over the years, there is an alike association: great profits stream from advanced league positions. According to Szymanski (2013, P. 56), increased expenses on salaries and purchase of players results in increased positions in the league since exemplary players seek higher wages and better players have the greatest chance to win more matches. To articulate this better, three main revenues for a football club are discussed below, with their impact on the economy.

2.5.1       Match Day Revenue

This revenue is mostly gained from membership, season tickets and receipts. Deloitte (2009) and (2010) indicate that the twenty biggest football clubs in Europe measured by their cumulative returns had recorded their matchday revenues as €1 billion backing 26 % of total incomes, and they had an average ability use of 87 % in their grounds. Nonetheless, match day revenue can be augmented by great capacity utilisation or by expanding the capacity. This can be achieved by attracting more spectators to the stadium through fan appeal and increased performance. Brandes et al. (2007) elude that 69% of the European football club fans have an affiliation to a team from the team’s specific players. Besides, Carmichael and Thomas (1993, p.77) agree that an excellent football club preserves its consumer loyalty by retaining a stable team tirelessly.

Increased performance, on the other hand, largely contributes to match day revenue. Increased performance includes one of the following three aspects of a football team: higher ranking, more wins and more entertainment. Ranking and wins are direct outcomes of increased performance, while entertainment might likely be more subjective from the fan’s side. Further increased performance can be achieved by investing in new players by purchasing players from the transfer market. The particular players might increase team performance by themselves or teach others to raise the rest of the team. Notably, these players must have a non-negative contribution to the team’s performance for them to be retained (Carmichael, Forest and Simmons, 1999, 125).

2.5.2       Broadcast Revenue

This revenue is acquired from both domestic and international competitions. Every club has access to an equal share of £31.8m from the domestic TV contract in EPL. They get from commercial revenue a £5m and an equal share of £43.2m each from overseas TV rights (Planet Football, 2020). In addition, Atkinson, Stanley and Tschihart (1988, p.27) postulate that great television viewership translates into more expensive advertising time, reinforcing the competition for the broadcasting rights and bidding the price up. However, aspects like fan appeal and increased performance automatically increase broadcast revenue.

2.5.3       Commercial Revenue

Commercial revenue involves both sponsorships and merchandising revenues. According to Deloitte (2008-2009), the twenty biggest football clubs in EPL had a total of €1.3 billion in commercial revenues, funding 32% to total revenues. Manzenreiter (2007) says that multinational corporations utilise the popularity of sports, sports stars and sporting events as the marketing vehicle.

2.6        Relationship Between A Player Transfer Fee and Economic Performance of Teams

The analysis of player transfer price has shown the influence of different factors on the player’s transfer value. While these factors may affect the player, they often affect the financial performance of a team. Some of the critical factors that are likely to present significant impacts on financial performance include image rights and iconic status.  Evidence from America suggests that a player with an iconic status can have the ability to influence the performance of the team in the field and finances. The influence in finances is best seen in a thorough rise in ticket sales and products related to a particular team and player. However, evidence supporting this notion is lacking. Today, teams tend to have iconic status instead of players. For instance, the question that is often asked is whether the transfer of Ronaldo from Manchester united led to the decline of brand name and tickets sales. Another classic example can be obtained from David Beckham’s career; he is considered one of his generation’s most iconic players (The Arsenal Review, 2009).

Regarding image rights, the question is often the players’ contribution through sales of tickets and other sports products. While it is a critical aspect of analysis, the subject is somewhat challenging; the challenge stems from the fact that teams often keep information of player contribution on merchandise confidential. Such confidentiality is due to the ability of regulators to affix value on the player transfer price. As a result, it would limit other factors’ significance in influencing players’ transfer value (The Arsenal Review, 2009; Moore & Roger, 2012).

While these factors play a critical role in establishing the economic entities in sports, they often focus on the player’s transfer value. Today, football clubs rely on factors such as the sales of merchandise and tickets. Given the popularity of some teams globally, it is easy for these clubs to make significant amounts of profit. The limits the significance of the relationship between player transfer value and the economic performance of the team. At the same time, it is critical to acknowledge that the data examining the relationship between these two variables. Hence, the need to identify different parameters that determine the economic performance of a football club. This will allow examining the economic dimension of sports within the context of football clubs and players (Klobučník et al., 2019).

2.7        Theoretical Framework

The analysis of the economic performance of football teams is often complex. For this reason, the number of theories exploring economic entities in sports is limited. The limitation is on the analysis of economics in the context of sports. However, this study uses economic theory to analyse the relationship between economic performance in football clubs in England. According to Engemann et al. (2009), the theory has been used to examine different issues that relate to sports. The theory postulates that economic performance in a given depends on a wide range of factors. These factors include market efficiencies, risk behaviours, discriminations and market powers.  In terms of risk behaviour, the theory examines the sporting industry as a competitive market. Therefore, football clubs are in constant competition both on and off the pitch. Hence, they seek to maximise their profits. The market power can be view in terms of sports league. Different leagues offer different playing styles, which influences the role of players in a given team. Thus, they tend to have a significant impact on value creation. While looking at value creation, it is critical to look at market efficiency and discriminations. Market efficiencies often refer to teams and other stakeholders’ capacity to react to key market changes appropriately. Discrimination shows how different football clubs will prefer to acquire a given player’s services in the transfer market and fail to push another’s acquisition (Engemann & Owyang, 2009).

The theory proposes examining risk factors within the teams and the transfer market in relation to football clubs. An analysis of these risk factor often influences the decisions of coaches, managers and players. In particular, it tends to influence players’ adaptability in a given team, hence influencing their transfer value. Simultaneously, the risk factor influences the decision of the coacher to acquire different players’ services. These decisions tend to bring out the concept of discrimination and the market’s power in influencing the team’s economic performance. Hence, the theory is highly relevant in the analysis of different relationships in the world of sport.

























3         Chapter 3: Methodology

3.1        Data and Data Issues

The data used in this study was from the 2018/19 Premier league season, and the reason for choosing this season is that it is the latest season that was uninterrupted and has complete data with minimal missing data. The analysis would have used the 2019/2020 season, but this was a season that was interrupted by Covid19, and the data on profitability might be affected by other non-football related factors, and this would have limited the ability of this study to provide accurate results. The data source was and, and the links are shown in the appendix section.

3.2        Variables and Model

The dependent variable in this study was the football team’s economic performance, and the proxy for economic performance was the revenues generated. The predictors in this study included the player transfer prices (expenditure on transfers), the average age of the squad, the club’s market value, and the average size of the squad. The analysis used the linear regression model, which is generally shown as follows:

Y = β0 + β1×1 + β2×2 + ……. βnxn + ℯ

Moreover, more specifically, after plugging in the variables, the model is as follows:

Y (economic performance) = β0 + β1(expenditure on transfers) + β2(age of squad) + β3(size of squad) + β4(market value of the club) + ℯ

3.3        Data Analysis Plan

Data analysis was done using SPSS. Both descriptive and inferential statistics were used. The inferential statistics, in this case, was regression analysis. Prior to carrying out regression analysis, the analysis tested the three main regression assumptions; normality, homoscedasticity, and multicollinearity.


















4         Chapter 4: Data Analysis

The mean squad size in the premier league was 39.06 players, with a relatively small standard deviation of 5.9, which is an indication that clubs generally have almost the same squad sizes. This is the same for the squads’ average age, with a mean of 24 and a standard deviation of 1.13.  There was, however, a large standard deviation of the expenditure on the transfer market (9.15811 million), an indication that how clubs spend varies greatly. This is shown in the table below:

Descriptive Statistics
  N Minimum Maximum Mean Std. Deviation
Squad size 17 32 56 39.06 5.900
Average age 17 22.1 25.8 24.059 1.1303
Foreigners 17 12 31 22.06 4.943
Total mkt value (€bn) 17 .20558 1.27000 .5343788 .34274684
expenditure on transfers(€mil) 17 5.02000 37.46000 13.8541176 9.15811188
Performance (revenues) (Millions) 17 159.66000 764.17000 345.7882353 210.88278638
Valid N (listwise) 17        



4.1        Correlation Analysis

As shown in the correlation matrix below, squad size, expenditure on the transfer market, and the market value of a club have significant positive correlations with a football club’s economic performance. It is also shown in the correlation matrix that the average age of the squad has a significant and negative correlation with the economic performance of that club.





  Squad size Average age Total mkt value (€bn) expenditure on transfers(€mil) Performance (revenues) (Millions)
Squad size Pearson Correlation 1 -.578* .152 -.123 .242
Sig. (2-tailed)   .015 .559 .639 .350
N 17 17 17 17 17
Average age Pearson Correlation -.578* 1 -.430 -.281 -.505*
Sig. (2-tailed) .015   .085 .275 .039
N 17 17 17 17 17
Total mkt value (€bn) Pearson Correlation .152 -.430 1 .955** .910**
Sig. (2-tailed) .559 .085   .000 .000
N 17 17 17 17 17
expenditure on transfers(€mil) Pearson Correlation -.123 -.281 .955** 1 .830**
Sig. (2-tailed) .639 .275 .000   .000
N 17 17 17 17 17
Performance (revenues) (Millions) Pearson Correlation .242 -.505* .910** .830** 1
Sig. (2-tailed) .350 .039 .000 .000  
N 17 17 17 17 17
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).


4.2        Diagnostic Tests

4.2.1       Normality

The P-P plot below shows that the plots conform to the diagonal line of normality, which, according to (Osborne and Waters, 2002), means that the data is normally distributed, and as such, the first regression assumption is satisfied.



4.2.2       Homoscedasticity

In the scatterplot below, the fact that the plots do not seem to follow any particular pattern indicates that the data is homoscedastic, which also means that the second assumption has been satisfied.


4.2.3       Multicollinearity

For this assumption to be satisfied, VIF should be >1, but <10, and from the output below, all the variables had VIF that falls within this range, and as such, the assumption has been satisfied.

Model Unstandardised Coefficients Standardised Coefficients t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) 943.972 922.315   1.023 .326    
Squad size -7.870 10.761 -.220 -.731 .479 .131 7.631
Average age -24.034 27.734 -.129 -.867 .403 .537 1.861
Total mkt value (€bn) 1098.696 579.312 1.786 1.897 .082 .013 4.643
expenditure on transfers(€mil) -21.630 21.714 -.939 -.996 .339 .013 4.874
a. Dependent Variable: Performance (revenues) (Millions)


4.3        Regression Analysis

R= 0.926, and this indicates that the correlation between the dependent variable economic performance) Moreover, predictors is a very strong one. The predictors or the independent variables can also explain or account for 85.7% of economic performance (R square = 0.857). This is shown in the model summary below:


Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .926a .857 .810 91.92853840
a. Predictors: (Constant), expenditure on transfers(€mil), Squad size, Average age, Total mkt value (€bn)


In the ANOVA table, F (4, 12) = 18.049, and sig = 0.000, which is less than the critical value (0.05), which is an indication that this model can significantly predict the outcome in the analysis.

Model Sum of Squares df Mean Square F Sig.
1 Regression 610134.519 4 152533.630 18.049 .000b
Residual 101410.274 12 8450.856    
Total 711544.793 16      
a. Dependent Variable: Performance (revenues) (Millions)
b. Predictors: (Constant), expenditure on transfers(€mil), Squad size, Average age, Total mkt value (€bn)


The table below shows that none of the variables significantly predict a football club’s economic performance in the English Premier League. The implication is that even though the expenditure on the transfer of players might correlate with economic performance, it does not cause it. The case is the same for squad size, the club’s market value, and the average age of the players.

Model Unstandardised Coefficients Standardised Coefficients t Sig.
B Std. Error Beta
1 (Constant) 943.972 922.315   1.023 .326
Squad size -7.870 10.761 -.220 -.731 .479
Average age -24.034 27.734 -.129 -.867 .403
Total mkt value (€bn) 1098.696 579.312 1.786 1.897 .082
expenditure on transfers(€mil) -21.630 21.714 -.939 -.996 .339
a. Dependent Variable: Performance (revenues) (Millions)

















5         Chapter 5: Discussion

According to Terekli et al. (2018), sports has shown tremendous social, political and economic developments. Due to these developments, several economic reflections can be observed in the sporting world. Examples of such reflections include transferring players from one team to another at a fee and football clubs’ economic value. These economic reflections can be observed in terms of millions of dollars of investments in football clubs. Therefore, there is a need to examine teams’ economic value about their financial performance (Terekli & Çobanoğlu, 2018).

Many valued clubs characterise the English premier league. These clubs are considered to be on top of the football food chain ion the world. They include Chelsea FC, Manchester United and Manchester City. Arsenal, Liverpool, and Tottenham Hotspur have been among the world’s top ten most economically valued clubs. Chelsea is currently valued at over $ 2.5 billion with a revenue of over $ 500 million. The clubs operating income is valued at approximately $ 127 million. It is considered one of the top premier league teams. Manchester united is valued at over $ 3.8 billion, with a revenue of over $790 million. The club has an operating income of over $230 million. Given these values, the economic value of the clubs is quite high.  However, this does not account for its economic performance (Forbes, 2019).

Byrom (2020) seeks to examine the profitability teams in the English premier league since 2015. Based on statistics, teams such as Chelsea FC, Manchester City, Fulham and Manchester United have been performing poorly. Chelsea experienced a loss of over 384 million pounds. Manchester United experienced a loss of about 149 million pounds while Manchester city had a total loss of approximately 254 million pounds. The records also show that football clubs with lower economic value have experienced losses too. For example, Fulham and Aston villa experienced a loss of about 259 million pounds and 184 million pounds, respectively. The presence of such observations deviates from the positive correlation that exists between the economic value of teams and their economic performance (Byrom, 2020).

The deviation from the correlation can also be seen where other teams with lower economic value have experienced higher profit margins. For instance, Burnley has made a total profit of about 79 million pounds. Teams with higher economic value with better economic performance are Liverpool and Tottenham. According to Byrom (2020), these football clubs’ profitability largely depends on the transfer market’s investments and decisions. Football clubs such as Chelsea have had investment input lower than their total revenue. Chelsea sold high valued players like Diego cost for a fee of fewer than 50 million pounds in terms of decisions. At the same time, they bought players at a higher value than that. In Manchester United’s case, their high expenditure in the transfer market has been the reason behind their poor financial performance. An example is the purchase of Harry Maguire and Paul Pogba for a value of more than 80 million pounds. The same team has sold talented young players like Adnan Januzaj at a lower transfer fee- consider the case of di Maria.

The premier league teams have been known to spend a lot during the transfer window. This expenditure varies significantly between the summer and winter transfer windows. On average, the premier league spends approximately 1.4 billion pounds during the summer transfer window. Despite this amount of spending, the number of players purchased teams often fall below the one hundred markets. With these averages, teams with higher market value are likely to spend more than teams with lower market value. However, the significance of this relationship is yet to be tested.

Based on clubs, each club often spends based on what is perceived to be their weakness. Clubs with higher market value have been known to spend more in the transfer market. Many of these spendings are often on young talent. In some cases, they have been known to spend more on experienced players. According to sky sports, Manchester United did spend more than any other club in the 2019 summer transfer window. They spent a total of 148 million pounds. More than 80% of this expenditure was on defenders, while the rest was on the attack. During the same period, higher spending was observed in Manchester city, arsenal, and Tottenham camps. Each club spent over 100 million pounds, while some broke their transfer fee record in acquiring players on permanent deals (Smith, 2020).

While this has been observed, some clubs with lower market value have spent more in the transfer market. Aston Villa best portrays this deviation in the summer of 2019. Aston villa spent a total of 144.5 million pounds in the transfer market. These transfers looked to strengthen their defence and midfield. Despite the deviation, there are vital deviations in the number of players bought by a particular club- For instance, the 148 million spent by Manchester united was on three players, while 144.5 million spent by Aston villa was on twelve players. Such notable variations bring up the question of how players are valued in the transfer market. Additionally, the question of whether clubs take advantage of other club needs remain unanswered.

Squad Value and Economic Performance of The Clubs

With the reputation of premier league clubs in the transfer market, it is critical to examine their financial performance. In this case, it is vital to determine whether clubs with more expensive players have better economic performance. The summary of squad value for each premier league club is provided in the table below:

Football club Total squad value (million pounds)
Manchester city 810.87
Manchester United 628.13
Chelsea 577.1
Arsenal 454.8
Liverpool 454.25
Everton 388.05
Tottenham 385.3
Leicester 317.45
Wolves 241.98
Aston villa 229.35
Westham 218.5
Newcastle 198.1
Southampton 170.55
Brighton 159.1
Crystal palace 149.86
Sheffield 120.7
Leads 120.3
West Bromwich 97.7
Fulham 94.35
Burnley 82.6


Based on the summary above, it is clear that football clubs with higher market value have some of the expensive squads. Within the value of these squads, some players are valued at a higher cost than others. For instance, the Liverpool squad has a significant number of expansive players. These players include Salah and Mane, each valued at over 120 million euros. In the case of Manchester City, players such as De Bruyne and Sterling cost a total of over 230 million euros. With such expensive players on their side, these teams are expected to have a high performance both on and off the pitch.  However, that is not the case for Manchester United, Manchester City and Chelsea (Lucas, 2020). These teams have experienced numerous financial losses in the last six years. Therefore, the question is, what are the main causes of this poor financial performance?

Football analytics have linked the economic performance of the football clubs to sales of club merchandise and tickets. Clubs such as Manchester United, Chelsea and Arsenal have a huge fan-based that contributes to their financial performance. The sale of players during the transfer windows is also a critical aspect of financial performance. With many variables contributing to the football clubs’ profitability, the question of why is the financial performance of Chelsea and Manchester United still low.

Many analysts have considered the concept of a fan favourite in analysing the relationship between player value and the team’s financial performance. In the past, clubs like Chelsea have had key players that were considered fan favourites. Such players were known to attract more fans into the home stadium. The presence of such players in the club was also critical to the sale of merchandise. However, on many occasions, such players were not considered to be expensive in the transfer market. As a result, the relationship between the club’s financial performance and the player value was not considered.

From another perspective, the performance of a player on the pitch often determines his transfer value. As a result, there are cases where fan favourites are players who have a high on-field preface. With their high value, they can contribute to the sales of merchandise and tickets during match days. Example of such players includes Salah and Mane. In such scenarios, highly rated players in the transfer market significantly contribute to the club’s financial performance. At the same time. Their transfer away from the current club could mean more profits for the club. However, this notion is often contested fiercely. The contention stems from the impact of such transfer on the club’s performance both economically and in sports. A reduction in sales of merchandise often accompanies the transfer of an expensive fan favourite. Stadium attendance may also drop significantly, leading to extreme losses for the football club.  For example, Manchester United has been associated with the sale of fan-favourite players since 2015. Robin van Persie, Louis Nani and Javier Hernandez’s transfer have often affected the team’s support. This was especially seen through the significant drop in sales of merchandise.





6         Conclusion

Football clubs are in constant competition to establish dormice in the field, and as established in this study, these teams are also continuously trying to improve their economic performance. Different scholars have applied different models to analyse player value determinants because the value of a player is associated with their likely contribution to the team performance both on the pitch and financially. Over time, the football transfer window, especially in England, has experienced numerous changes, with the shattering of the world record transfer fees becoming a norm.  The current study sought to investigate the relationship between a club’s expenditure in the transfer window and the economic performance of a football team in the English Premier League and finds significant positive correlations between the economic performance of a club and the squad size, expenditure on the transfer market, and the market value of a club. The analysis also finds that the squad’s average age has a significant and negative correlation with the economic performance of that club. However, using regression to investigate causation reveals that none of the variables significantly predict a football club’s economic performance in the English Premier League. In other words, even though the expenditure on the transfer of players might correlate with economic performance, it does not cause it. The case is the same for squad size, the club’s market value, and the average age of the players.







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8         Appendix

8.1        Data Source



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