sports economics(superstar effects and transfer fee in premier League soccer))

Name of Author:

Name of Professor:

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Date:                  

 

Sports Economics

Abstract

“The main reasoning that is prevalent in the English premier league lineage is that teams that buy superstars end up with the best results and that a person is a superstar given the amount that most of the clubs are willing to pay for him.” This statement has been justified in this research, as stated in the findings in this paper, teams that spend a lot of money on purchasing players often end up with good results. Players who perform well in various leagues are highly valued and are considered superstars. Teams with high numbers of superstars are successful than those with a few number of superstars. This is because superstars attract a large number of fans and this increases club revenue thus making the club successful. Superstars are also good performers on the field and this improves the positions of teams on the league table.

Introduction

Club performance is determined by the quality of players and the amount of money the club is willing to spend on their players and the acquisition of new players. This motivates the players and their performance on the field improves greatly and this in turn improves the confidence and morale of fans of a particular club. Clubs with the best players in the English Premier League often have good results. These clubs spend much money on buying these players so as to improve on their performances. A player is considered a superstar when many clubs are willing to purchase him and also the amount of money the clubs are willing to offer is a great determinant. Teams with players who have acquired the superstar status often perform better than those without superstars.

Literature Review

Transfers especially of superstars change a club’s attitude as a lot of confidence is built, and there are high hopes of the club performing well. If a club spends a large amount of money on players, it improves its performance, for example, Manchester City spent huge volumes of money on acquiring world class players and this helped them win the Premier League. It also increased their fan base and more people gained a lot of confidence in the club and this meant that the club made more money than before due to higher match attendances and other revenues.

The amount paid for a player is a topic that brings a lot of controversy during the transfer period. Arguments erupt on whether a player has been overvalued or undervalued; this may determine whether fans will be optimistic about the transfer or if they will be unsupportive. A club might spend a lot of money on a player who does not perform according to expectations and the club ends up facing sharp criticism from the fans. The amount paid for the transfer of a certain player may not necessarily determine the player’s performance, but the club’s value for a player will directly affect his performance on the field Blair, (2012). A highly valued player will perform better than those who are not highly valued. Fans always appreciate highly valued players and this motivates them to give their best performances during matches.

Highly valued players are players whom clubs spend a lot of money on. However, the effects of buying a superstar may not be felt immediately, it may take some time before the club begins feeling the effect of the player. A footballer may fail to perform during his first weeks in a new club, and this may kill the morale of fans who are usually expecting a lot from him. A club may spend huge volumes of money on a footballer, but the footballer may fail to perform as per their expectations, for example, Chelsea Football Club spent a lot of money on Fernando Torres but he did not perform as expected during his first weeks at the club.

He was a highly valued player and the managers at Chelsea as well as the fans expected a lot from him, but he only managed to score 6 goals, and this was an underperformance for a striker of his class. Other strikers who had cost a lot less, like Michu of Swansea, were performing better than Torres. Michu managed to score 12 goals which are double those that Torres managed to score, this was a great return on the part of Michu. Having superstar players is a key factor towards the performance of a club but there are also a lot of other factors involved for a club to be successful.

A club that performs well will have a wide fan base and as long as it keeps performing the fans will remain loyal to the club. The fans will always attend matches in large numbers to support their club as everyone feels happy when associated with the winning team. Large fan turnout means that the club will collect more revenue and thus it will be stable financially. Fans also motivate players during matches and the players will give their best during matches.

The amount of money spent during the transfer of players is recovered as the attendance of fans becomes higher while they come to watch the new signings their club has made so as to view their performance on the field. Also new players improve club performance and this increases their chances of winning trophies and this increase the club’s revenues in many ways, example the attendance will increase due to more fans attending matches.

Clubs that invest highly on players end up having good results and winning trophies. An example is Manchester City who spent millions of pounds acquiring world class players example Aguero who has had a great impact on the team. Manchester City yielded good results after their huge investment on players and they went ahead to win the premier league. Another example is Chelsea football club who also spent lots of money on improving their squad. This changed everything for them as they became a world class team and they have won various titles including the Champions League. The move by Chelsea to purchase Samuel Etoo who is a world class player has seen the team improve greatly in their recent matches as the Cameroonian striker has changed their game greatly.

Manchester United is also known to spend heavily on players and that is why they have been a top club for many years. They spent a lot of money acquiring Robin Van Persie from Arsenal and the Dutch joined the club and they proceeded to win the premier league during his first season in the club. He has been a highly valued player at Manchester United and that is why he is an excellent performer scoring goals in almost every game he plays.

Superstars have great effects on teams and the way they are treated matters a lot and it greatly affects their performance on the field. Cristiano Ronaldo for example is a world class player who impacts a great deal of influence on every team he joins. He was a world class player during his days at Manchester United and is still a great performer and a highly valued player at Real Madrid.

Football clubs should therefore focus more on acquiring world class players to improve their squads as this has proven to yield great results. Clubs should also value and treat their players well for them to give their best while in the field.

Model

Transfer of players can have a great impact on the economy of a country. An example is the case where Real Madrid that lured the player Welsh from an English team known as Tottenham Hotspur for a very high transfer fees that shocked the world. This amounted to $132million, an amount that kept the soccer industry on edge for a number of weeks. This transaction was highly criticized and condemned by others such as the new FC coach for Barcelona who argued that the deal did not portray a good image to the world.

This is because, most of the people especially in Spain are suffering from unemployment and most of the soccer clubs, including Real Madrid itself is suffering from heavy debts. As much as the player would create a good image for the club and attract more fans to support the club, this decision might lead to destabilization of the economy of Spain. More of the benefit in this case hence goes to the player as he is able to attain the status of a superstar. He will also be able to make huge volumes of money from the clubs that are buying him. On the contrary, the clubs themselves remain astounded as they are not so sure whether the player will earn as much money for them as they used in buying him.

The soccer industry can thus be said to be turning into what is referred to as a “winner-take –all market”, in which case, the few individuals who are at the top of their professions have the chance of winning very high wages while other individuals in this sector struggle to earn a living. The soccer market is hence currently regarded as getting out of control. This is due to the existence of a small number of very wealthy buyers who have an interest in a certain player in a particular team. As the club owners and presidents compete to outdo each other in signing ‘the biggest name’, they create bidding wars which are very hard to control making them their own enemies.

Some bets are, however, beneficial to the soccer clubs because one individual can have a lot of impact on the success of the club. An example is Bale, who was a consistent performer in the past season of Tottenham, in the English premier league and who is currently playing remarkably for the Spanish and Champions league. This player has attracted quite a large number of consumers to attend games which he is playing and hence spend money that flows to the club, meaning that he is a great income earner for the club. In this case, therefore, one high-profile star can have a lot of impacts on the bottom line of an increasingly global market whereby very few clubs compete for the attention of large numbers of soccer fans.  

 

 

                                                                                                     

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Data Analysis and Results

Regression Results for various seasons in the premier league

Data 2003/2004

Team

Turn – over, £m

Wage bill

Table Score

Purchase Gross

Position

Arsenal

114.562

69.889

90

45.54

1

Aston Villa

55.859

33.767

56

7.37

6

Birmingham

 

 

50

 

10

Blackburn Rovers

40.843

31.308

44

16.5

15

Bolton Wanderers

48.763

23.48

53

500

8

Charlton

42.606

29.913

53

1.5

7

Chelsea

143.615

115

79

51.536

2

Everton

44.672

33.171

39

8857.2

17

Fulham

42.948

30.9

52

7.462

9

Leeds city

 

 

33

 

19

Leicester City

 

 

33

 

18

Liverpool

92.349

65.635

60

14

4

Manchester City

 

 

41

102.08

16

Manchester United

171.5

76.874

75

48.222

3

Middlesbrough

43.047

28.967

48

 

11

Newcastle United

90.468

50.222

56

 

5

Portsmouth

 

 

45

 

13

Southampton

 

 

47

 

12

Tottenham Hotspur

66.324

33.142

45

26.18

14

Wolverampton

 

 

33

 

20

 

 

 

 

 

 

 

 

 

 

insheet using C:\Users\Jakiru\Documents\prem034.csv

(5 vars, 20 obs)

 

. regress turnoverm tablescore purchasegross wagebill

 

     Source |       SS       df       MS             Number of obs =     11

————-+——————————           F( 3,     7) =   10.98

       Model | 17354.3065     3 5784.76882           Prob > F     = 0.0049

   Residual |   3686.7594     7 526.679914           R-squared     = 0.8248

————-+——————————           Adj R-squared = 0.7497

       Total | 21041.0659   10 2104.10659           Root MSE     =   22.95

 

——————————————————————————

   turnoverm |     Coef.   Std. Err.     t   P>|t|     [95% Conf. Interval]

————-+—————————————————————-

tablescore |   .8945543   .8339449     1.07   0.319   -1.077412   2.866521

purchasegr~s |   .0000692     .00309     0.02   0.983   -.0072374   .0073759

   wagebill |   1.022883   .4331002     2.36   0.050   -.0012359   2.047002

       _cons |   -24.5464   36.25697   -0.68   0.520   -110.2805   61.18772

——————————————————————————

A coefficient is a multiplier of a variable in a mathematical expression.

In the regression chart above the turnover of teams is seen to affect other variables like the table score, purchase gross and the wage bill. The turnover has an effect on the table score of teams as it gives a positive coefficient of 0.8. It also has an effect on the purchase gross as it results to a positive coefficient. The regression chart also shows that the turnover of teams has a great effect on the wage bill as it gives a positive coefficient of 1.02. The wage bill will thus have a multiplier of 1.2 which means that the figure of the wage bill will be multiplied by 1.2 if the turnover increases. The table score will have a positive multiplier of 0.8.

The graph below shows the relationship between turnover and wage bill of teams in the 2003/2004 premier league season. Graph findings show that teams with a higher turnover had a high wage bill as compared to those with a lower turnover.

 

 

 

  

 

 

 

 

 

  

 

Team

Turn – over, £m

Wage bill

Table Score

Purchase Gross

Position

Arsenal

132.112

83

67

25.55

4

Aston Villa

49.982

38.3

42

12

16

Birmingham

40.117

 

34

 

18

Blackburn Rovers

43.396

33.4

63

6.2

6

Bolton Wanderers

86.518

28.5

56

5.25

8

Charlton

41.925

34.2

47

6

13

Chelsea

130.41

114

91

58.2

1

Everton

58.123

37

50

17.75

11

Fulham

37.111

30.1

48

4.55

12

Liverpool

119.499

69

82

29.05

3

Manchester City

61.802

34

43

 

15

Manchester United

105.925

85.4

83

20

2

Middlesbrough

51.988

28.8

45

10.3

14

Newcastle United

82.882

52.2

58

37.3

7

Portsmouth

36.068

 

38

 

17

Sunderland

39.258

 

15

 

20

Tottenham Hotspur

68.885

41

65

19.9

5

West Bromwich

35.54

 

30

 

19

West Ham United

52.007

 

55

 

9

Wigan Athletic

34.852

 

51

 

10

 

 

 

 

 

 

 

 

 

 

 

 

insheet using C:\Users\Jakiru\Documents\prem0506.csv

(5 vars, 20 obs)

 

. regress turnoverm wagebill tablescore purchasegross

 

     Source |       SS       df       MS             Number of obs =     13

————-+——————————           F( 3,     9) =   10.80

       Model | 11256.0745     3 3752.02484           Prob > F     = 0.0024

   Residual | 3125.65389     9 347.294876           R-squared     = 0.7827

————-+——————————           Adj R-squared = 0.7102

       Total | 14381.7284   12 1198.47737           Root MSE     = 18.636

 

——————————————————————————

   turnoverm |     Coef.   Std. Err.     t   P>|t|     [95% Conf. Interval]

————-+—————————————————————-

   wagebill |   .7646639   .5071851     1.51   0.166   -.3826686   1.911996

tablescore |   .5886002   .6784696     0.87   0.408   -.9462048   2.123405

purchasegr~s |   .0980685   .6389877     0.15   0.881   -1.347422   1.543559

       _cons | -.0883221   26.5709   -0.00   0.997   -60.19587   60.01923

————————————————-

                                      

In the regression chart above the turnover of teams has an effect on other variables. The turnover has an effect on the wage bill as it shows a positive coefficient of 0.76. It also has an effect on the table score as it shows a positive coefficient of 0.59. The turnover also has an effect on the purchase gross of teams as it shows a positive coefficient of 0.09. The regression chart shows that if teams have a high turnover, then they will have a high wage bill, a high table score and a high purchase gross. This shows that the purchase gross will have a multiplier of 0.09 and the value of the purchase gross will be multiplied by this figure to attain the new amount of the purchase gross. The coefficient is a multiplier of the variable.

 

The graph above shows the correlation between the turnover and the wage bill of teams. The graph shows that teams with a high turnover also have a high wage bill. The turnover of teams often determines their wage bills as shown in the graph.

 

  

  

 

 

 

 

 

 

 

Team

Turn – over, £m

Wage bill

Table Score

Purchase Gross

Position

Arsenal

139.003

89.7

68

3.4

4

Aston Villa

52.674

43.2

50

 

11

Blackburn Rovers

43.303

36.7

52

 

10

Bolton Wanderers

43.087

30.7

56

8

7

Charlton Athletic

35.929

34.3

34

10.7

19

Chelsea

165.341

132.8

83

21

2

Everton

51.412

38.4

58

8.6

6

Fulham

39.228

35.2

39

2

16

Liverpool

133.91

77.6

68

2

3

Manchester City

56.952

 

42

2.6

14

Manchester United

143.823

92.3

89

18.6

1

Middlesbrough

47.838

38.3

46

7.75

12

Newcastle United

87.083

62.5

43

15

13

Portsmouth

40.245

 

54

1.7

9

Reading

49.909

 

55

1.35

8

Sheffield United

38.93

 

38

3.15

18

Tottenham Hotspur

103.91

43.8

60

10.9

5

Watford

28.824

 

28

3.1

20

West Ham United

49.427

 

41

4.1

15

Wigan Athletic

26.889

 

38

8.5

17

 

 

 

 

 

 

 

 

 

insheet using C:\Users\Jakiru\Documents\prem0607.csv

(5 vars, 20 obs)

 

. regress turnoverm tablescore wagebill purchasegross

 

     Source |       SS       df       MS             Number of obs =     11

————-+——————————           F( 3,     7) =   23.44

       Model | 21894.2498     3 7298.08325           Prob > F     = 0.0005

   Residual | 2179.24959     7   311.32137           R-squared     = 0.9095

————-+——————————           Adj R-squared = 0.8707

       Total | 24073.4993   10 2407.34993           Root MSE     = 17.644

 

——————————————————————————

   turnoverm |     Coef.   Std. Err.     t   P>|t|     [95% Conf. Interval]

————-+—————————————————————-

tablescore |   .9439209   .5265692     1.79   0.116  -.3012173   2.189059

   wagebill |   1.068659   .2933147     3.64   0.008     .3750795   1.762238

purchasegr~s | -.9164046   1.021205   -0.90   0.399     -3.33117   1.498361

       _cons | -21.85281   20.82751   -1.05   0.329   -71.10204   27.39642

 

The above is a multiple regression chart result for the turnover, table score, wage bill and purchase gross of teams for the 2006/2007 season. The results show that the turnover had an effect on two of the variables as they both show positive coefficients of 0.9 for the table score and 1.07 for the wage bill. The purchase gross however shows a negative coefficient of -0.9. In this regression the purchase will have a negative multiplier and this shows that the turnover will not have a positive effect on it. The wage bill and table score will have positive multipliers and this shows that the turnover will have a positive effect on them which means they will increase in value.

 

 

The graph above shows the correlation between the turnover and the wage bill of teams. The graph shows that teams with a high turnover also have a high wage bill. The turnover of teams often determines their wage bills as shown in the graph.

 

 

 

 

 

 

 

 

 

 

 

Team

Turn – over, £m

Gate and Matc – hday income, £m

TV and Broad – casting, £m

Comm – ercial, £m

Wage bill

Table score

Position

Arsenal

222.5

95

68

31

101.3

83

3

Aston Villa

75.6

18.5

46

11

50.4

60

6

Birmingham

27.2

7

14

4

21.8

35

19

Blackburn Rovers

56.4

6.2

41.2

9

39.7

58

7

Bolton Wanderers

59.1

6.8

34.2

 

39

37

16

Chelsea

213.6

 

 

5.3

149

85

2

Derby County

11.2

 

 

 

37.1

11

20

Everton

76

20.5

46.6

8.9

44.5

65

5

Fulham

53.7

9.6

34

4.9

39.5

36

17

Liverpool

159

 

 

 

 

76

4

Manchester City

823

13.6

43.3

25.4

54.2

55

9

Manchester United

256.2

101.5

90.7

64

121.1

87

1

Middlesbrough

48

11.1

27

2.4

34.8

42

13

Newcastle United

100.8

32.3

41.1

27.4

74.6

43

12

Portsmouth

70.5

12

51.2

 

54.7

57

8

Reading

9

 

 

 

6.9

36

18

Sunderland

63.6

13.6

35.6

6.1

52.9

39

15

Tottenham Hotspur

114.7

28.6

40.3

8.3

21.8

46

11

West Ham United

57

17

24

9

44.2

49

10

Wigan Athletic

43

 

 

 

38.4

40

14

 

 

 

 

 

 

           insheet using C:\Users\Jakiru\Documents\prem0708.csv

(7 vars, 20 obs)

 

. regress turnoverm tablescore wagebill position

 

     Source |       SS       df       MS             Number of obs =     19

————-+——————————           F( 3,   15) =   0.90

       Model | 91384.1228     3 30461.3743           Prob > F     = 0.4661

   Residual | 510041.613   15 34002.7742           R-squared     = 0.1519

————-+——————————           Adj R-squared = -0.0177

       Total | 601425.736   18 33412.5409           Root MSE     =   184.4

 

——————————————————————————

   turnoverm |     Coef.   Std. Err.     t   P>|t|     [95% Conf. Interval]

————-+—————————————————————-

tablescore | 1.107604   8.315402     0.13   0.896   -16.61626   18.83146

   wagebill |   .6193771   1.984609     0.31   0.759   -3.610716     4.84947

   position | -5.501691   25.40185   -0.22   0.831   -59.64445   48.64107

       _cons |   95.33155   662.0892     0.14   0.887   -1315.878   1506.541

——————————————————————————

The above is a multiple regression result for the turnover, the wage bill and the position of teams in the 2007/2008 season. The regression chart results show that the turnover has a positive effect on the table score as there is a positive coefficient of 1.1. The turnover also has an effect on the wage bill as it shows a positive coefficient of 0.62. The position however has a negative coefficient of -5.5. Teams with a high turnover had a high wage bill as well as a high table score. The regression shows that the table score and the wage bill will have positive multipliers which means that they both will increase with an increase in the turnover.

 

.

The graph above shows the correlation between the turnover and the wage bill of teams. The graph shows that teams with a high turnover also have a high wage bill. The turnover of teams often determines their wage bills as shown in the graph.

 

 

 

 

 

 

 

 

 

Team

Turn – over, £m

Gate and Matc – hday income, £m

TV and Broad – casting, £m

Comm – ercial, £m

Wage bill

Table score

Position

Arsenal

222.5

95

68

31

101.3

83

3

Aston Villa

75.6

18.5

46

11

50.4

60

6

Birmingham

27.2

7

14

4

21.8

35

19

Blackburn Rovers

56.4

6.2

41.2

9

39.7

58

7

Bolton Wanderers

59.1

6.8

34.2

 

39

37

16

Chelsea

213.6

 

 

5.3

149

85

2

Derby County

11.2

 

 

 

37.1

11

20

Everton

76

20.5

46.6

8.9

44.5

65

5

Fulham

53.7

9.6

34

4.9

39.5

36

17

Liverpool

159

 

 

 

 

76

4

Manchester City

823

13.6

43.3

25.4

54.2

55

9

Manchester United

256.2

101.5

90.7

64

121.1

87

1

Middlesbrough

48

11.1

27

2.4

34.8

42

13

Newcastle United

100.8

32.3

41.1

27.4

74.6

43

12

Portsmouth

70.5

12

51.2

 

54.7

57

8

Reading

9

 

 

 

6.9

36

18

Sunderland

63.6

13.6

35.6

6.1

52.9

39

15

Tottenham Hotspur

114.7

28.6

40.3

8.3

21.8

46

11

West Ham United

57

17

24

9

44.2

49

10

Wigan Athletic

43

 

 

 

38.4

40

14

 

 

 

           insheet using C:\Users\Jakiru\Documents\prem0809.csv

(7 vars, 20 obs)

 

. regress turnoverm tablescore wagebill position

 

     Source |       SS       df       MS             Number of obs =     19

————-+——————————           F( 3,   15) =   0.90

       Model | 91384.1228     3 30461.3743          Prob > F     = 0.4661

   Residual | 510041.613   15 34002.7742           R-squared     = 0.1519

————-+——————————           Adj R-squared = -0.0177

       Total | 601425.736   18 33412.5409           Root MSE     =   184.4

 

——————————————————————————

   turnoverm |     Coef.   Std. Err.     t   P>|t|     [95% Conf. Interval]

————-+—————————————————————-

tablescore |   1.107604   8.315402     0.13   0.896   -16.61626   18.83146

   wagebill |   .6193771   1.984609     0.31   0.759   -3.610716     4.84947

   position | -5.501691   25.40185   -0.22   0.831   -59.64445   48.64107

       _cons |   95.33155 662.0892     0.14   0.887   -1315.878   1506.541

——————————————————————————

 

 

The above is a multiple regression chart result for the 2008/2009 season. The chart shows that the turnover has an effect on the table score and the wage bill. The two variables show positive coefficients of 1.1 and 0.62 respectively. Teams with a high turnover had a high table score and a high wage bill during this season. An increase in the turnover meant an increase in the wage bill and also an increase in the table score as well. The table score and the wage bill will have positive multipliers and this means that they will both increase with an increase in the turnover.

 

The graph above shows the correlation between the turnover and the wage bill of teams. The graph shows that teams with a high turnover also have a high wage bill. The turnover of teams often determines their wage bills as shown in the graph.

 

 

 

 

 

 

 

          

 

 

Team

Turn – over, £m

Gate and Matc – hday income, £m

TV and Broad – casting, £m

Comm – ercial, £m

Wages as Prop – ortion of Turn – over (%)

Wage Bill

Table Score

Position

Arsenal

382

94

85

31

29

111

75

3

Aston Villa

91

24

52

14

88

80

64

6

Birmingham City

56

7.4

42

7

68

38

50

9

Blackburn Rovers

58

6

43

9

81

47

50

10

Bolton Wanderers

62

9

38

4

74

46

39

14

Burnley

9

 

 

 

144

13

30

18

Chelsea

213

 

 

5

82

175

86

1

Everton

79

19

50

10

69

54

61

8

Fulham

77

11

43

9

63

48

46

12

Hull city

52

21

16

15

90

46

30

19

Liverpool

185

43

80

62

65

120

63

7

Manchester City

125

18

54

53

106

132

67

5

Manchester United

286

100

104

81

46

131

85

2

Portsmouth

61

10

39

6

49

30

19

20

Stoke City

59

 

 

 

76

45

47

11

Sunderland

65

13

39

5

83

54

44

13

Tottenham Hotspur

119

27

52

8

56

67

70

4

West Ham United

72

17

38

13

75

54

35

17

Wigan Athletic

43

 

 

 

91

39

36

16

Wolverhampton

28

6

17

3

82

23

38

15

 

 

           insheet using C:\Users\Jakiru\Documents\prem0910.csv

(8 vars, 20 obs)

 

. regress turnoverm tablescore wagebill position

 

     Source |       SS       df       MS             Number of obs =     20

————-+——————————           F( 3,   16) =   10.46

       Model |   109120.12     3 36373.3732           Prob > F     = 0.0005

   Residual | 55659.6803   16 3478.73002           R-squared     = 0.6622

————-+——————————           Adj R-squared = 0.5989

       Total |   164779.8   19 8672.62105           Root MSE     = 58.981

 

——————————————————————————

   turnoverm |     Coef.   Std. Err.     t   P>|t|     [95% Conf. Interval]

————-+—————————————————————-

tablescore |   4.65596   5.157063     0.90   0.380   -6.276525   15.58845

   wagebill |   .8440911   .6559905     1.29   0.216   -.5465467   2.234729

   position |   8.043118   14.5102     0.55   0.587   -22.71713   38.80337

       _cons | -276.4014   395.1361   -0.70   0.494   -1114.053   561.2497

——————————————————————————

 

The above is a multiple regression chart result for the 2009/2010 season. The results show that the turnover had an effect on the other variables. There was a positive effect on the wage bill as it shows a positive coefficient of 0.84. There was also an effect on the table score as it shows a positive coefficient of 4.6 and also on the position as it shows a positive coefficient of 8. This means that teams with a high turnover had high wage bills, high table scores and better positions on the league table during this season.

 

Team

Turn – over, £m

Gate and Match – day income, £m

TV and Broad – casting, £m

Comm – ercial, £m

Wages as Prop – ortion of Turn – over (%)

Wage bill

Table Score

Position

Arsenal

256

93

85

33

48

123

68

4

Aston Villa

92

21

54

17

90

83

48

9

Birmingham City

56

7

42

7

68

38

39

18

Blackburn Rovers

58

6

42

9

86

49

43

15

Blackpool

52

 

 

 

48

25

39

19

Bolton Wanderers

68

9

45

 

82

56

46

12

Chelsea

222

 

 

0.6

86

190

71

2

Everton

82

17

53

12

71

58

54

7

Fulham

77

12

51

10

75

58

49

8

Liverpool

184

41

65

77

73

134

58

6

Manchester City

153

20

69

65

114

174

71

3

Manchester United

331

109

119

103

46

152

80

1

Newcastle United

89

24

48

16

60

53

46

13

Stoke City

67

 

 

 

70

46

46

14

Sunderland

79

12

48

10

77

60

47

10

Tottenham Hotspur

163

20

54

9

56

91

62

5

West Bromwich Albion

59

8

43

7

63

37

47

11

West Ham United

81

19

46

12

69

55

33

20

Wigan Athletic

 

51

 

 

78

0

42

16

Wolverhampton

64

10

44

5

59

37

40

17

 

             insheet using C:\Users\Jakiru\Documents\prem1011.csv

(8 vars, 20 obs)

 

. regress turnoverm tablescore wagebill position

 

     Source |       SS       df       MS             Number of obs =     19

————-+——————————           F( 3,   15) =   28.26

       Model | 96450.7976     3 32150.2659           Prob > F     = 0.0000

   Residual | 17061.9392   15 1137.46262           R-squared     = 0.8497

————-+——————————           Adj R-squared = 0.8196

       Total | 113512.737   18 6306.26316           Root MSE     = 33.726

 

——————————————————————————

   turnoverm |     Coef.   Std. Err.     t   P>|t|     [95% Conf. Interval]

————-+—————————————————————-

tablescore |   7.768428   2.262905     3.43   0.004     2.945159     12.5917

   wagebill |   .2608471   .3537677     0.74   0.472   -.4931909   1.014885

   position |   7.254508   4.037009     1.80   0.092   -1.350172   15.85919

       _cons | -380.9494   142.0021   -2.68   0.017   -683.6197   -78.27922

——————————————————————————

The above is a multiple regression chart result for the turnover, table score, position and wage bill of teams in the 2010/2011 premier league season. The results show that the turnover had an effect on the wage bill as it shows a positive coefficient of 0.26. It also has an effect on the table score as it shows a positive coefficient of 7.8. The turnover also has an effect on the position of teams as it shows a positive coefficient of 7.25. This means that teams with a high turnover also had high wage bills and performed well on the league table. The table score will have a multiplier of 7.25 and the wage bill will have a multiplier of 0.26. This shows that the wage bill and the table score are expected to increase with an increase in the turnover of teams.

 

The graph above shows the correlation between the turnover and the wage bill of teams. The graph shows that teams with a high turnover also have a high wage bill. The turnover of teams often determines their wage bills as shown in the graph.

 

 

 

 

 

 

 

 

 

 

 

2003-2012 multiple regression chart

regress wagebill purchasegross tablescore

 

     Source |       SS       df       MS             Number of obs =     122

————-+——————————           F( 2,   119) =   60.31

       Model | 88106.8594     2 44053.4297           Prob > F     = 0.0000

   Residual | 86928.9758   119 730.495595           R-squared     = 0.5034

————-+——————————           Adj R-squared = 0.4950

       Total | 175035.835   121 1446.57715           Root MSE     = 27.028

 

——————————————————————————

   wagebill |     Coef.   Std. Err.     t   P>|t|     [95% Conf. Interval]

————-+—————————————————————-

purchasegr~s | -.0000824   .0030718   -0.03   0.979     -.006165   .0060001

tablescore |   1.559736   .1423824   10.95   0.000     1.277805   1.841668

       _cons | -25.50706   8.191125   -3.11   0.002   -41.72631   -9.287817

——————————————————————————

 

The above chart shows the multiple regression results for clubs during the 2003-2012 seasons. According to the results the wage bill has an effect on the table score of teams as it shows a positive coefficient of 1.6. The purchase gross shows a negative coefficient of -0.0000824. Teams that had a high turnover had a high wage bill and a high table score during all the football seasons between 2003 and 2012.

 

 

 

 

. regress wagebill gateandmatchdayincomem purchasegross tablescore

 

     Source |       SS       df       MS             Number of obs =     100

————-+——————————           F( 3,   96) =   36.06

       Model | 59995.0555     3 19998.3518           Prob > F     = 0.0000

   Residual |   53247.339   96 554.659782           R-squared     = 0.5298

————-+——————————           Adj R-squared = 0.5151

       Total | 113242.395   99 1143.86257          Root MSE     = 23.551

 

——————————————————————————

   wagebill |     Coef.   Std. Err.     t   P>|t|     [95% Conf. Interval]

————-+—————————————————————-

gateandmat~m |   .2305124   .0960665     2.40   0.018     .0398218     .421203

purchasegr~s | -.0002894   .0026854   -0.11   0.914     -.00562   .0050412

tablescore |   1.288285   .1581069     8.15   0.000     .9744456   1.602125

       _cons | -17.27623   8.467013   -2.04   0.044   -34.08312   -.4693459

——————————————————————————

The regression chart above shows the results when the wage bill, purchase gross, table score and gate and match day earnings are regressed. It shows that the wage bill has an effect on the table score of teams as it shows a positive coefficient of 1.3. It also has an effect on the gate and match earnings as it shows a positive coefficient of 0.23. The table score will have a positive multiplier with a figure of 1.3, this shows that the table score will increase with an increase in the wage bill.

 

 

 

 

Conclusion

The data collected from various seasons for teams in the English Premier League show that teams with high wage bills performed better than those with low wage bills. Teams that paid their players well ended up attaining higher positions in the league tables as compare to those that paid their players poorly. Teams with superstar players also performed better on the league than teams with no superstars. This shows that superstars have a great effect on a team’s performance and teams should invest more on their players so as to perform better on the league table.

 

 

 

 

 

 

 

 

 

 

 

Works Cited

Blair, Roger D. Sports economics. New York: Cambridge University Press, 2012.

 

 

 

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