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.
To export a reference to this article please select a referencing stye below:
My Assignment Help. (2020). Analysis Of Competitiveness In Premier League Division. Retrieved from https://myassignmenthelp.com/free-samples/eco371-sports-economics-for-tactical-knowledge-and-regulation-of-debt.
"Analysis Of Competitiveness In Premier League Division." My Assignment Help, 2020, https://myassignmenthelp.com/free-samples/eco371-sports-economics-for-tactical-knowledge-and-regulation-of-debt.
My Assignment Help (2020) Analysis Of Competitiveness In Premier League Division [Online]. Available from: https://myassignmenthelp.com/free-samples/eco371-sports-economics-for-tactical-knowledge-and-regulation-of-debt
[Accessed 27 July 2024].
My Assignment Help. 'Analysis Of Competitiveness In Premier League Division' (My Assignment Help, 2020) <https://myassignmenthelp.com/free-samples/eco371-sports-economics-for-tactical-knowledge-and-regulation-of-debt> accessed 27 July 2024.
My Assignment Help. Analysis Of Competitiveness In Premier League Division [Internet]. My Assignment Help. 2020 [cited 27 July 2024]. Available from: https://myassignmenthelp.com/free-samples/eco371-sports-economics-for-tactical-knowledge-and-regulation-of-debt.
WhatsApp
