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You should also preferably use a division or conference where the teams haven’t changed over the decade of results you will need.

I. For the division you’ve chosen, calculate the standard deviation of winning percentages for each year over a ten year period (preferably the last decade but you can fudge on that a bit). Calculate as well the “ideal” standard deviation and present your ten years of results as ratios of the actual to the ideal. Comment on any trend you
might observe and on how the division you’ve chosen compares to the results across the five pro leagues we looked at in class.

II. For three of the teams in your division (first, last, middle from last year) calculate the between season variation in winning percentage over the decade of results you have.

III. Provide a listing of the division winners over the decade. Depending on how the league works you can provide the regular season winner or if the playoff structure leads to a division champion, the champion list. Calculate the Herfindahl-Hirschman index for this and comment on the result (Table 5.4 in the text gives some comparative numbers for the major pro leagues).

IV. Draw a Lorenz curve for the distribution of points earned by your division in the last season you’re using.

V. Drawing on what we’ve learned about factors (rules, financing etc.) that influence competitiveness, describe efforts or actions taken by the League you’ve chosen over
the past decade that might have influenced the degree of competitiveness in your league. Is there any sense in your data that such actions (or lack of them) might have
had a result?

Standard Deviation of Winning Percentages

I). The standard deviation of the six team seems to be decrease and then increase at the same time compared to the trend in class. The ideal standard deviation trend for the six teams also decreases and then increases at the same time as compare d to the one in class.

II). The Herfindahl-Hischman index for the six teams during the pro-league increases in the beginning and then decrease to zero which is the minimum value. Afterwards, the Herfindahl –Hischman start increasing from 0.04 to 0.09 and 0.26 in the end.

IV). Efforts taken by premier league to ensure competiveness of the teams. 

This denotes the capacity to visualize space vividly across the whole football ground.

The footballers have been made conscious of the instant space around them. Moreover, the logical footballers are conscious. There are two major reasons why footballer’s require to have an understanding where the members of the team are to anticipate the position of the teammates and tell them where they should be .

The structure for the team affects the methods how the team will utilize to succeed the division. Throughout the years soccer formations have adjusted radically 

Expenses on salaries are believed to hike the quality of the team, and increased spending, than competitor will rise a team’s probability of winning a game (Allan, 2004). The football teams are supposed to hike the spending not completely, but in relation to the teams that are competing (Paul et al, 2010). This scrutiny, integrated with the debates that the football teams objective role is an impartial function that is linear of winning and profit. This insinuates that football teams will pursue to maximize spending above their means. 

Table 1.0 Revenue, wage expenditure, profit and funds, Premier League (2010 - 2011)

Revenue

Wage Expenditure

Profit before tax

Net Debt

Arsenal

226,825,000  

124,401,000  

14,776,000  

-97,827,000  

Aston Villa

92,028,000  

94,795,000  

-54,013,000  

-153,169,000  

Chelsea

228,574,000  

191,214,000  

-78,262,000  

-816,038,000  

Everton

82,021,000  

58,026,000  

-5,413,000  

-44,914,000  

Fulham

77,109,000  

57,672,000  

4,792,000  

-192,947,000  

Liverpool

183,690,000  

134,768,000  

-49,317,000  

-61,274,000  

Man City

153,186,000  

173,977,000  

-197,491,000  

-42,900,000  

Man Utd

331,441,000  

152,915,000  

12,004,000  

-308,258,000  

Newcastle

88,464,000  

53,585,000  

32,619,000  

-130,485,000  

Norwich

23,391,000  

18,445,000  

-7,065,000  

-16,778,000  

QPR

16,229,000  

29,739,000  

-25,383,000  

-53,963,000  

Reading

23,138,000  

20,511,000  

-5,371,000  

-34,842,000  

Southampton

13,370,000  

66,809,000  

79,447,000  

13,460,000  

47,093,000  

60,882,000  

-11,740,000  

-5,558,000  

-7,838,000  

-26,450,000  

-339,000  -76,841,000  

Stoke

Sunderland

Swansea

11,669,000  

17,392,000  

-11,173,000  

-754,000  

Tottenham

163,486,000  

91,255,000  

402,000  

-56,080,000  

West Brom

65,086,000  

43,903,000  

18,934,000  

-1,948,000  

West Ham

80,939,000  

55,704,000  

-18,565,000  

-41,614,000  

Wigan

50,507,000  

39,948,000  

-7,155,000  

-72,696,000  

Sum

2,057,409,000  

1,479,685,000  

-400,817,000  

-2,230,117,000  

Average

102,870,450  

73,984,250  

-20,040,850  

-111,505,850  

The division is selected for evaluation as UEFA declared tactics to control spending in the course of this period. Nearly one and a half billion bounds were utilized on paying the workers of the division in the course of this period (Barnett & Hilditch, 2013). A mean of 79.9 million euros per football team was spent in each year (Rascher, 2019. The division accumulated revenue, which is two billion euros offsets the division accumulate amount utilized on salaries (Pawlowski et al, 2010). Nevertheless, the Premier League salary spending went up at a high rate than proceeds, therefore causing a rise in the accumulated loss of pre-tax (Peel & Thomas, 2015). Within this time, premier league teams have minimized the gross debt. This is greatly witnessed in richer football teams such as Manchester United who reimburse an income in every division (Peel & Thomas, 2012).

As definite spending has a minimum effect on the positions of the league, guidelines embraced to counter spending are observed regularly. These tactics mostly take the structure of an increased salary spending allowed. There were no such tactics in the division of Premier League that were intended to supervise salary spending until the opening of the UEFA Regulations on Fair Play (Reep & Benjamin, B. 2018). 

Ideal Standard Deviation

 UEFA FFP indicators for Premier League teams (2010 - 2011)

 Table 1.1 UEFA FFP indicators for Premier League clubs (2010 - 2011)

NED

CBE <-£4m

CBE <-£36m

Debt/rev

Wage/rev

Red Flags

Arsenal

70,744,000

70,744,000

-0.43

55%

0

Aston Villa

Yes

-91,584,000 

*

-91,584,000 

*

-1.66

*

103%

*

5

Chelsea

Yes

-138,258,000 

*

-138,258,000 

*

-3.57

*

84%

*

5

Everton

Yes

-6,016,000 

*

-6,016,000 

-0.55

71%

*

3

Fulham

-8,886,000 

*

-8,886,000 

-2.50

*

75%

*

3

Liverpool

-13,477,000 

*

-13,477,000 

-0.33

73%

*

2

Man City

-308,471,000 

*

-308,471,000 

*

-0.28

114%

*

3

Man Utd

-16,819,000 

*

-16,819,000 

-0.93

46%

1

Newcastle

102,828,000

102,828,000

-1.48

*

61%

2

Norwich

-9,602,000 

*

-9,602,000 

-0.72

79%

*

2

QPR

Yes

-37,671,000 

*

-37,671,000 

*

-3.33

*

183%

*

5

Reading

1,123,000

1,123,000

-1.51

*

89%

*

2

Southampton

Yes

-19,050,000 

-9,355,000 

-33,407,000 

*

*

*

-19,050,000 

-9,355,000 

-33,407,000 

-1.98

-0.01

-0.97

*

101%

70%

77%

*

*

*

4

2

2

Stoke

Sunderland

Swansea

Yes

-9,835,000 

*

-9,835,000 

-0.06

149%

*

3

Tottenham

1,913,000

1,913,000

-0.34

56%

0

West

Bromwich

13,674,000

13,674,000

-0.03

67%

1

West Ham

Yes

-32,832,000 

*

-32,832,000 

-0.51

69%

3

Wigan

Yes

-10,810,000 

*

-10,810,000 

-1.44

*

79%

*

4

Sum

-555,791,000 

-555,791,000 

Average

-27,789,550 

-27,789,550 

-1.13 

85%

3

In the year 2010, the organization commended a number of regulations on Financial Fai Play with the aim of initiating more specialty within the team’s resources and advocating for more investment, which is responsible (Welki, & Zlatoper, 2014). Notwithstanding a reduction of rising commercial and public interest in the football cub of European at this period, many teams all though Europe are experiencing a financial health that is poor. Equivalent to several the division of Premier League teams, other teams are observed to be wriggling to cope with obligations on finances and have recorded constant losses on finances. Table 3.5 Premier League revenue and payments (2003 - 2013)

Total Revenue (TR)

Premier

League

Payment (LP)

% of TR

Fixed

Payments

% of LP

%of TR

Variable Payments

% of LP

% of

TR

2003 2004

1,327,770,000  

436,995,370  

33%

205,644,980  

47%

15%

231,350,390  

53%

17%

2004 2005

1,333,575,000  

467,682,048  

35%

262,953,160  

56%

20%

204,728,888  

44%

15%

2006 2007

1,530,430,000  

463,640,898  

30%

259,284,480  

56%

17%

204,356,418  

44%

13%

2007 2008

1,927,358,000  

766,793,964  

40%

462,268,340  

60%

24%

304,525,624  

40%

16%

2009 2010

2,030,000,000  

830,958,732  

41%

494,780,860  

60%

24%

336,177,872  

40%

17%

2010 2011

2,271,000,000  

952,749,977  

42%

634,912,513  

67%

28%

317,837,464  

33%

14%

2011 2012

2,360,000,000  

968,180,900  

41%

651,054,740  

67%

28%

317,126,160  

33%

13%

2012 2013

2,525,000,000  

972,165,620  

39%

654,695,280  

67%

26%

317,470,340  

33%

13%

Based on the regulation of the FFP, the Premier league division revealed controls on expenses to begin from the 2013 to 2014 division (Baimbridge et al, 2016). The guidelines affirm that every team, beyond the coming 3 divisions, cannot make an accumulated deficit of more than one hundred and five million euros (Beckman et al, 2011). In a similar time, teams whose accumulated salary bill is above fifty-two euros will only be permitted to raise their wages by a total of four million euros per division (Peel, & Thomas, 2017). Moreover, any team generating a deficit great than five million euros yearly must warrant those deficits against the asset of the owner (Verbeek, 2008). The harshest penalty for violating the guidelines is the subtraction of division points. The effect of these guidelines will probably be seen past the 2015 -2016 division (Rascher, , & Solmes,. 2007).

Table 1.2 Club revenues from the Premier League and UEFA competitions (2007-2008)

Club total revenue

Total Payment from FAPL (as % of TR)

Total Payment from UEFA (as % of TR)

Media Payment (PL + EUFA)  (as % of TR)

Manchester United

257,116,000

49,851,273

19%

33,788,652

13%

83,639,925

33%

Chelsea

213,648,000

46,058,490

22%

28,663,500

13%

74,721,990

35%

Arsenal

209,294,000

47,524,659

23%

18,285,540

9%

65,810,199

31%

Liverpool

164,222,000

45,923,106

28%

21,130,220

13%

67,053,326

41%

Tottenham

114,788,000

36,465,219

32%

365,494

0%

36,830,713

32%

Newcastle

100,866,000

39,684,372

39%

39,684,372

39%

Manchester City

82,295,000

40,106,571

49%

40,106,571

49%

West Ham United

81,726,000

23,655,817

29%

23,655,817

29%

Everton

75,650,000

42,568,569

56%

412,774

1%

42,981,343

57%

Aston Villa

75,639,000

42,720,000

56%

42,720,000

56%

Portsmouth

71,556,000

40,831,632

57%

40,831,632

57%

Sunderland

63,597,000

34,003,221

53%

34,003,221

53%

Bolton

59,072,000

32,401,668

55%

365,494

1%

32,767,162

55%

Reading

58,023,000

30,951,546

53%

30,951,546

53%

Blackburn

56,395,000

40,680,201

72%

78,800

0%

40,759,001

72%

Fulham

53,670,000

31,676,607

59%

31,676,607

59%

Birmingham

49,836,000

30,226,485

61%

30,226,485

61%

Derby

48,558,000

29,501,424

61%

29,501,424

61%

Middlesbrough

47,952,000

34,576,851

72%

34,576,851

72%

Wigan

43,455,000

33,851,790

78%

33,851,790

78%

The team’s quality is affected by the price of the specific players. The wage expenditure is likely to be a strong predictor of the strength of the team, given a competitive market for players. Chelsea sent the largest amount on salaries, which is one hundred and fifty four million euros every year, on average over the data period, while Manchester United have the finest results of match win with a salary bill of one hundred and eighteen million per year Wooldridge, . 2010). There is a great association between the number of matches won (correlation index 0.77) and the spending on wages, indicating that a rise in expenditure on salaries hikes the quality of results for the team (Vrooman, 2011). This great association is significant indication backing up the allegation that the market of labor for players of soccer is competitive. Figure 1.1 depicts the association for the year 2011 to 2012 division (Vrooman, 2015). The football teams that sit north have more expenditure on salaries to obtain similar points such as those ones below the line. For instance, NewCastle and Chelsea managed to reach 65 and 64 points progressively but the salary bill was 64 and 174 million, progressively

Conclusion

There is sense that regulation of debt and spending on players have a an effect on the results released by the team.

Table 1.3 Results from modelling match results by ordered Probit

All Clubs

Top 5

Bottom 5

All Clubs with fixed effects

Coeff.

SE

Coeff.

SE

Coeff.

SE

Coeff.

SE

Spending on wages

Home Wages

0.47

0.08

**

0.43

0.12

**

0.70

0.26

**

0.58

0.18

**

Away Wages

-0.48

0.06

**

-0.64

0.14

**

-0.46

0.11

**

-0.45

0.06

**

Additional Competitions

Home FA Cup

0.01

0.08

-0.24

0.18

0.29

0.16

*

-0.03

0.11

Away FA Cup

-0.09

0.08

0.06

0.17

-0.28

0.16

*

-0.08

0.10

Home Champions League

0.12

0.15

-0.02

0.19

6.39

6.22

0.10

0.15

Away Champions League

-0.26

0.15

*

-0.14

0.31

-0.25

0.26

-0.25

0.15

*

Home Europa League

0.02

0.15

-0.51

0.36

0.03

0.24

-0.18

0.16

Away Europa League

-0.02

0.14

0.14

0.35

0.01

0.23

-0.06

0.15

Legacy performance

Home Form

0.03

0.03

0.05

0.08

-0.01

0.05

-0.06

0.03

*

Away Form

-0.05

0.03

*

0.04

0.06

-0.05

0.05

-0.07

0.03

**

Attendance

6.7E-06

3.1E-06

**

1.5E-05

4.8E-06

**

-8.8E-06

7.3E-06

-1.6E-05

1.3E-05

Distance

3.1E-04

3.4E-04

-1.8E-04

7.1E-04

1.2E-03

6.1E-04

*

4.3E-04

3.7E-04

Fixed effects significance:

Team

  -

 -

  -

Yes

Day

-

-

-

No

Month

-

-

-

No

Season

Yes

No

Yes

Yes

Cut 1

-0.43

**

-0.26

-0.41

-1.87

Cut 2

0.36

**

0.62

0.33

-1.06

Log likelihood

-1766

-344

-612

-1726

Observations

1824

434

598

1824

 Table 1.4 Premier League Competitive Balance between(2003 - 2013)

(1) HHI

(2) Champions

(3) SD

(5) DN

2003 2004

0.054

Manchester Unitedd

0.197

2004 2005

0.055

Manchester United

0.215

0.345

2005 2006

0.055

Manchester City

0.192

0.330

2006 2007

0.054

Manchester City0.193

0.285

 

2007 2008

0.056

0.213

0.265

 

2008 2009

0.055

0.204

0.260

 

2009 2010

0.055

Manchester United

0.207

0.285

2010 2011

0.052

Manchester City

0.186

0.195

2011 2012

0.055

Chelsea

0.202

0.215

2012 2013

0.055

Chelsea

0.216

0.200

Average

0.055

0.203

0.264

(2) HHIC

0.360

(4) CBR

0.262 

References

Allan, S. (2018). Satellite television and football attendance: the not so super effect. Applied Economics Letters, 11(2), 123-125. doi: 10.1080/1350485042000200231

Baimbridge, M., Cameron, S., & Dawson, P. (2016). Satellite television and the demand for football: a whole new ball game? Scottish Journal of Political Economy, 43(3), 317.

Barnett, V., & Hilditch, S. (2013). The effect of an artificial pitch surface on home team performance in football (soccer). Journal of the Royal Statistical Society.

Beckman, E. M., Cai, W., Esrock, R. M., & Lemke, R. J. (2011). Explaining Game-to-Game Ticket Sales for Major League Baseball Games Over Time. Journal of Sports Economics, 13(5), 536-553. doi: 10.1177/1527002511410980

Paul, R. J., Wachsman, Y., & Weinbach, A. P. (2010). The Role of Uncertainty of Outcome and Scoring in the Determination of Fan Satisfaction in the NFL. Journal of Sports Economics, 12(2), 213-221. doi: 10.1177/1527002510376789

Pawlowski, T., Breuer, C., & Hovemann, A. (2010). Top clubs’ performance and the competitive situation in European domestic football competitions. Journal of Sports Economics, 11(2), 186-202.

Peel, D., & Thomas, D. (2012). The demand for football: some evidence on outcome uncertainty. Empirical Economics, 17(2), 323-331.

Peel, D., & Thomas, D. (2015). Outcome uncertainty and the demand for football: an analysis of match attendances in the English football league. Scottish Journal of Political Economy, 35(3), 242-249.

Peel, D., & Thomas, D. (2017). Handicaps, outcome uncertainty and attendance demand. Applied Economics Letters, 4(9), 567-570. doi: 10.1080/135048597355041 Premier League Judgement

Rascher, D. A., & Solmes, J. (2007). Do fans want close contests? A test of the uncertainty of outcome hypothesis in the National Basketball Association. A Test of the Uncertainty of Outcome Hypothesis in the National Basketball Association (June 15, 2007).

Rascher, D. A. (2019). A test of the optimal positive production network externality in Major League Football. Sports, Economics: Current Research.

Reep, C., & Benjamin, B. (2018). Skill and chance in association football. Journal of the Royal Statistical Society. Series A (General), 581-585.

Vrooman, J. (2011). Two to Tango: Optimum Competitive Balance in Professional Sports Leagues.

.Vrooman, J. (2015). A general theory of professional sports leagues. Southern Economic Journal, 971-990.

Welki, A. M., & Zlatoper, T. J. (2014). US professional football: The demand for game-day attendance in 1991. Managerial and Decision Economics, 15(5), 489-495.

Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data: MIT press.

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