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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

## 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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