An analysis of the determinants of success in the Premier League: Does money guarantee success?

8/13/2019 An analysis of the determinants of success in the Premier League: Does money guarantee success?

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EC331: Research in Applied Economics

An analysis of the determinants of success inthe Premier League: Does money guarantee

success?

Terry Tsz Chun Wong

Student ID: 1001519

Word Count: 5000 (includes footnotes and tables)

Abstract:

This paper aims to identify the extent to which money brings success to aPremier League club. This is achieved by evaluating factors thatdetermine a clubs overall financial position, in comparison with factorsrelating to team stability at a club. The analysis incorporates ideas drawnfrom Szymanski and Kuper (2012) and Van Vugt, Hart and Leader(2008), and is carried out on data across four seasons between 2007 and2011. The data was collected from Deloitte & Touches Annual Reviewof Football Finance, EUFO Football Squads and World Football

Statistics.


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An analysis of the determinants of success in the Premier League: Terry Tsz Chun WongDoes money guarantee success? 1001519

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Table of Contents

Page

Section 1: Introduction

1.1Introduction 31.2Literature Review and Theory 4

Section 2: Data Summary and Methodology

2.1 Data Summary 72.2 Methodology 9

Section 3: Empirical Analysis

3.1 Empirical Analysis: Financial Position 133.2 Empirical Analysis: Team Stability 14

Section 4: Final Conclusions

4.1 Limitations and Extensions 174.2 Conclusion 17

Section 5: Appendix

5.1 Variable Descriptions 195.2 Mathematical Appendix 20

Section 6: Bibliography

6. Bibliography 22


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Section 1: Introduction

1.1 INTRODUCTION

To build a great team is not all about money. First you have to create spirit andtogetherness in the squad and that is not easy.- Arsne Wenger, 2003.

Since the induction of the Premier League in 1992, clubs in the top league of Englishfootball have seen their revenues increase dramatically, caused by the increasinglyexpensive broadcasting rights sold to network companies worldwide by the leagueassociation. In fact, between the period 2007 and 2011 alone, the total revenue receivedsolely from broadcasting by Premier League clubs stood at over 3.3 billion1.Furthermore, a handful of Premier League clubs, most notably Manchester City andChelsea, have experienced an influx of investment by wealthy owners. Seeking healthy

returns from their investments, these owners have injected substantial levels of moneyinto the club, in an attempt to build teams that can achieve success in the Premier League.

Hence, it is no surprise that money is now perceived to play an extremely important rolein achieving success in the Premier League by fans and the media. Yet, despite this, thefinancial status of a club does not consistently correspond with the overall success of theclub within the league. This begs the question: does money really guarantee success in thePremier League?

Van Vugt, Hart and Leader (2008) argue that success can instead be achieved bymaintaining team stability. He finds that a higher level of team stability improves the

coordination between players on the pitch, whilst at the same time creating a sense ofmotivation and team spirit within the team.

This paper incorporates their ideas to compare the true significance of a clubs financialposition, in relation to factors that capture the overall stability of a club. The analysis iscarried out using data collected on Premier League teams across four seasons between2007 and 2011.

I first begin by outlining the variety of previous research on the use of economics in sportthat has provided me with inspiration during the process of constructing my final model. Ithen carry out a preliminary analysis on my chosen variables, along with a detailed

explanation of my final model. Finally, the data is fully analysed, providing results thatare interpreted statistically and economically, and subsequently allowing me to make myfinal conclusions.

1Figure calculated using data from the Premier League Annual Reviews 2007-2011


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1.2 LITERATURE REVIEW AND THEORY

Previous literature analysing, not only professional football leagues, but also renownedleagues that exist in other sports, has assisted me in developing the framework for mymodel.

The typical structure that exists in league competitions, in which teams typicallyparticipate once a year, allows a teams overall performance to be ultimately representedby the league position obtained at the end of each season.

In his analysis on the impact of discrimination on league position in English football,Szymanski (2000) uses a negative log-odds function, in whichpitdenotes all possibleleague positions, from 1 to 92, across all four divisions in English football:

!!" ! !" !!"!!"! !!"! This continuous variable allows for an effective linear regression, and more importantly,gives a higher weight to progress further up the table. This advantage is particularlyvital in my analysis of the Premier League, as exponentially large financial incentives liein positions towards the top end of the table. Such incentives comprise of a greater shareof the distributed revenue from the sale of broadcasting rights2, as well as opportunities to

participate in European competitions. I have subsequently emulated Szymanskisapproach and modified the formula to simply account for the 20 possible positions a teamcan finish within the Premier League:

!!" ! !!" !!"!!"! !!"! The significance of a clubs financial strength in generating success is thoroughlydocumented within the literature. There is a particular focus on the explosion of mediathat has revolutionised the landscape across all popular team sports. Vlassopoulos (2009)thoroughly documents the significant role played by television network deals ingenerating revenue for franchises in the U.S. National Football League (NFL). Heidentifies that in 2006, the deals struck with popular media network, such as ESPN,

provided NFL franchises with almost $117 million each, which for many teams accounted

for half of their total revenue. Similarly, Premier League clubs have seen their incomesprogressively increase, as the price of broadcasting rights sold to TV companies continuesto rise.

This substantial boost in revenue thus allows teams to invest in building teams with thebest personnel, with the intention to create further success. Previous literature, mostnotably Szymanski and Kuper (2012), found that the labour market within football ishighly efficient. Hence, players with more ability are rightly paid higher wages. It istherefore no surprise that Premier League clubs with higher annual incomes havetypically larger wage bills, as they are able to afford the more talented players. This is

225% of broadcasting rights revenue is awarded on a merit basis, depending on where a club finishes in

the final League table (Barclays Premier League)


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subsequently translated into better team performances, as Szymanski and Kuper identify apositive relationship between a clubs wage bill and final league position.

On the contrary, the transfer market, in which clubs buy and sell players, has proven to berelatively inefficient. Szymanski and Kuper (2012) identified that the net transfer

spending3explained only 16% of the total variation in league position, signifying aweak relationship between the variable and a teams performance in the league. Theyexplained their findings by arguing that clubs were overestimating the true values of theirinvestments and effectively buying the wrong players that have not improved theteams overall performances.

The detailed work of Van Vugt et al. (2008) into the significance of team stability in theItalian first division (Serie A) provides the foundations for my analysis. He measuresteam stability by calculating the percentage of players, who, at the start of the league

season, were also playing for the same team at the beginning of the previous season.Having discovered a positive relationship between the variable and league position, hedraws a conclusion that stability increases the teams coordination on the pitch, as playersdevelop an understanding and a shared motivation to succeed.

Similarly, Tarlow (2012) argues that in the U.S. National Basketball Association (NBA),teams with greater team chemistryhave higher efficiency on the pitch, because a playersability to make good decisions during play is directly dependent upon his knowledge ofeach of his teammates abilities, limitations, and tendencies. This, consequently,translates into better team performances and greater success in the league.

The team chemistryvariable used in my analysis compares the top eleven players in eachteam that have played the most minutes in a certain season with the top eleven in the

preceding season. Subsequently, a point is allocated for every player that feature in bothgroups. This variable works under the assumption that the players with the most minuteson the pitch are likely to have played with each other more frequently, and thusdeveloping a shared knowledge on factors such as their teammates abilities, limitationsand tendencies (Tarlow, 2012). Moreover, by making comparisons with the previousseason, I attempt to capture the level of team chemistrythat is conserved each season.

Van Vugt et al.further analyses the potential impacts of the age of players on teamstability and subsequently league position, identifying a negative relationship between the

average age4of a playing squad and league position. He argues that because a playersability typically declines with age, older teams are expected to finish lower in the league.In extension, my model includes a variable, ratio of players aged above 29, whichcomputes the percentage of players within the squad that are 30 years old or above. Thisis the perceived age within the football industry when a players ability starts to diminish.Hence, I intend to find a similar relationship as average age.

Managerial stability is finally highlighted by Van Vugt et al. as a significant factor inachieving success, particularly in the long-run. By including a dummy variable torepresent a change in manager at a club in his regression, he concludes that teams that had

3Appendix 1.1: Variable descriptions

4Appendix 1.1: Variable descriptions


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experienced a change in manager during the season were generally ranked lower at theend of the season.

I extend my analysis to try and identify differences between amanagerial changethatoccursbefore the start of a season, and one that occurs during a season. I believe that achange during a seasonis more likely to be a result of a struggling club replacing itsunder-achieving manager, in a bid to stimulate success. Hence, I hypothesise that theimpact of such a change to be positive. By contrast, a managerial change that occursbefore the start of a seasonmay be a result of a manager deciding to take a new job. Thismay have a negative impact on the team, possibly because of the players emotionalattachment to their previous manager, or because of a lack of understanding with the newmanager.

Finally, it is likely that a teams success in a previous season can impact on their

performance in the following season. Tarlow (2012) found that in the NBA, a teamsstrong performance during one season would have a positive effect. Hence, in order tocapture this aspect, I incorporate a lagged dependent variable into the model.


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Section 2: Data & Methodology

In order to carry out my analysis, I have put together my own panel dataset that consistsof data collected between the 2007/2008 and the 2010/2011 seasons. The structure andregulations of the Premier League, however, had raised a preliminary issue. At the end ofevery season, the bottom three teams are relegated into the Championship (the seconddivision in English football), while the top three teams from the Championship are

promoted for the following season. As the Premier League provides a substantial step indifficultly from the Championship, many promoted teams featured in the Premier Leaguefor only one season, before being relegated again. Therefore, to avoid using asignificantly unbalanced dataset, I only analyse teams that had participated in at leastthree of the four seasons. Although this reduces the number of teams that I observe to 14,I have sufficiently reduced the proportion of unknowns within the dataset.

2.1 DATA SUMMARY

The variables I have analysed can be roughly separated into two categories.

The first category of variables, as listed below in the correlation matrix, provides acomprehensive measure of the overall financial position of a club. These variables have

been constructed using data from in the Deloitte & Touche Annual Review of FootballFinance reports.

Table 1. Correlation matrix: Financial position variables

The strong positive correlation between league position and wage spendingand averageattendance corresponds with the inferences made by previous literature. By contrast, nettransfer spendingappears to be negatively correlated with league position, which alreadysuggests that highly inefficient transfer business has been carried out by Premier Leagueclubs, as identified by Szymanski & Kuper (2012).

The matrix also finds positive correlations between league position and total revenue.However, as discussed later in this section, this does not necessarily imply that a direct

relationship exists between these two variables.


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The second category of variables evaluates the overall stability of the club. The data that Ihave collated to form these variables are from two online football databases, EUFOFootball Squads and World Football Statistics. The table below lists these variables andtheir suggested relationships with league position:

Table 2. Correlation matrix: Team stability variables

Firstly, the correlation matrix finds negative correlations between both the age-related andleague position, thus supporting the findings cited by Van Vugt et al.(2008). I have alsoincluded the variable,players used5, as a proxy for high injury rates (Pedace, 2008). I

hypothesise that the league position of a club is likely to be adversely impacted by theloss of key players, due to injuries. The negative correlation between the variable andleague position certainly supports this theory.

As expected, team chemistryportays a relatively strong positive correlation with thedependent variable. This supports the inferences previously made by both Tarlow (2012)and Van Vugt et al.(2008). However, a brief study of the scatter graph and histogram

below suggests that a quadratic relationship may exist between the variables.

Figures 1 (left) & 2 (right): Scatter graph plotting team chemistry on league position across all

seasons & histogram portraying the distribution of team chemistry

5Appendix 5.1: Variable Description


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This would make sense economically, as teams that consistently change their personneldramatically each year would fail to sustain a reasonable level of team chemistry. At thesame time, teams that have evidently not attempted to improve their team and are morelikely to fall behind their rivals.

Surprisingly, the matrix finds no difference in correlation between a change in managerduring the seasonand one before the start of a season. This therefore appears tocontradict my hypothesis that a managerial change during the season effects league

position positively.

2.2 METHODOLOGY

The results are generated using a fixed-effects estimation, allowing me to account for

unobserved heterogeneity, and thus reduce the level of bias in my estimations. Possiblefactors that cause bias include the ability of both the players and managers, which arelikely to be positively correlated with league position.

Table 3: Fixed-Effects Estimation of all independent variables on Log-odds leagueposition

Note:

Standard errors in parentheses

** Coefficient statistically significant at the 15% significance level

*** Coefficient statistically significant at the 10% significance level


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A preliminary regression of league position on all variables provides some interestingmotives for the construction of the final model. Most notably, the coefficient on totalrevenueis surprisingly statistically insignificant and suggests a negative relationship withfinal league position.

This suggests that total revenuemay be better captured as an indirect effect on leagueposition. In theory, a clubs revenue ultimately affects a teams success through investingthe money towards improving the team by purchasing better players with naturally higherwage demands. Hence, modelling wage spendingas an endogenous variable and totalrevenue as an instrument within a two-stage least squares IV regression effectivelycaptures the sophisticated triangulate relationship between league position, revenue andwages:

First stage:

!"!!"#$!!" ! !!!" !"#"$%" !" ! !!!"#!" ! !!!"#$%&!"

Second stage:

!!" ! !! !!"!! ! ! !" !"#$ !" ! !!!" ! !!" ! !!"Furthermore, I believe that this model allows me to deal with reverse causality that islikely to exist between total revenueand final league position. Clubs that consistentlyfinish in higher positions are able to improve their teams exponentially, due to theadditional revenue they receive, and thus stimulating further success in the league.

The first stage regression includes two dummy variables that significantly influence aclubs overall wage spending. UCLitandEuropaitrepresent a clubs participation in theUEFA Champions League and the Europa League respectively. As well as generatingextra revenue through the further sale of broadcasting rights and match tickets, the

participation in European competitions is particularly important in attracting the bestplayers to a football club. In order to improve their own abilities, talented players seek tochallenge themselves by playing in the most competitive of environments. They willtherefore aspire to join the clubs that regularly finish amongst the top five positions in thePremier League, and subsequently participate in these competitions.

Table 4. Correlation matrix: Endogenous variable and Instrumental Variables


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Table 4 reveals positive correlations between wage spending and both dummies, albeit aclubs participation in the Europa League conveys a significantly weaker relationship.

Nevertheless, this justifies the use of these variables as suitable instruments for wagespending.

In order to evaluate the true influence of a clubs financial position on success, I run oneregression solely on a clubs financial factors, followed by a second that incorporates thestability variables, with the latter representing my final model. This will therefore allowme to compare the estimations of the financial variables between the two models.Moreover, I will be able to identify the factors concerning stability, which contribute mostsignificantly to achieving success.


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Section 3: Empirical Analysis

The table below presents the results of the two fixed-effects IV regressions on thedependent variable, the Log-odds League Position.

Table 5: Fixed-Effects IV Regression models

Model 1 Final Model

First lag of league position -0.2676**(.1847)

Wage spending 1.953***(.8174)

0.9485(1.148)

Average attendance 6.460***

(3.790)

16.89***

(5.591)Net transfer spending 0.000000009920

(0.000007030)0.00000001510***

(0.000006510)

Average age 0.2144(.1877)

Ratio of players aged above29

-6.051***(2.633)

Players used -0.09446***(.04926)

Team chemistry 0.8353**

(.5366)Team chemistry2 -0.05150

(.03696)

Managerial change before thestart of the season

-0.4593***(.2750)

Managerial change before thestart of previous season

0.5716**(.3496)

Manangerial change duringthe season

0.3923(.3331)

Constant -88.83(40.38)

-191.5(57.28)

Observations 63 45

Within R 0.1170 0.7401

F-statistic 2.68 4.43

Probability > F 0.0051 0.0021

Notes:

Standard Errors in parentheses

** Coefficient statistically significant at the 15% significance level

*** Coefficient statistically significant at the 10% significance level


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3.1 EMPIRICAL ANALYSIS: FINANCIAL POSITION

Wage Spending

The coefficients on a clubs annual wage spendingprove to be particularly interesting.The first model finds that a 10% increase in a clubs wage spendingcorrelates to anincrease of approximately 20% in league position. This suggests that the labour marketwithin the Premier League has continued to be relatively efficient, as players with betterability are being paid higher wages.

In the second model, however, the effect of wage spendingbecomes diminished andstatistically insignificant. This suggests that money cannot exclusively buy a club successin the Premier League. Chelsea Football Club provides a clear example of this. Under theownership of Russian oligarch Roman Abramovich, the club spent on average 25% more

(Deloitte, 2007-2011) on their wage bill between 2007 and 2011, compared to theirclosest rival, Manchester United. Yet despite this, Chelsea only managed to win one offour seasons, while Manchester United won the remaining three. The model thereforeinfers that while a strong financial position can help significantly in building a team withstrong technical ability, team stability is an equally important factor for success.

Net Transfer Spending

In stark contrast to wage spending, the coefficient on net transfer spendingimplies that anincrease in 1 million in net transfer spending would increase a clubs league position by1.51%. This infers that clubs are better off making profit from their transfer activities with

very little impact in their overall league position, which goes against economic logic. Theresult thus suggests that an inefficient transfer market continues to exist within thePremier League, as theorised by Syzmanski and Kuper (2012).

Average Attendance

Both regressions find average attendanceto be a considerably important factor inproducing Premier League success, to the extent that a 1% increase in attendance wouldbe followed by a 16.9% increase in a clubs league position. This emphasises thecontribution that attending audiences can make in encouraging their team to perform onthe pitch during a match. Larger crowds are generally able to make more noise, providing

a psychological boost for the team they are supporting, and at the same time injecting alevel of fear into the opposition. This effect, however, is most prominently witnessedduring matches played at a clubs home stadium, in which the majority of the crowd arelocal supporters. Since all clubs play 19 games each at their own stadium, the impact ofcrowds on performance should be balanced out fairly across the season. This, therefore,raises the issue regarding the contribution of attendance towards a clubs total revenue.Clubs with larger stadiums tend to receive higher revenue through the sale of match-daytickets. Subsequently, through the wage-spending transmission mechanism, a clubsleague position can be improved. It is therefore likely that the positive correlation

between attendance and revenue may raise concerns related to bias within the results.


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3.2 EMPIRICAL ANALYSIS: TEAM STABILITY

Team Stability

Team stability is categorised by the following variables: the first lag of league position,the total number of players used, team chemistry and theage characteristics of the playingsquad.

Perhaps the strongest proxy for measuring team stability is team chemistry. The modelidentifies that a higher level of team chemistry will induce better team performances andthus a higher league position. An increase in one point in team chemistryleads toapproximately an 84% increase in log-odds league position.

To put this into perspective, this effect is evaluated in terms of the changes in actual

league position using the following derivative

6

:

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Hence, at the mean league position, 10thplace, an increase in one point translates into amaximum increase of approximately 4 places7.

While the coefficient on the squared term of team chemistry is statistically insignificant, itis, however, only just rejected at the 15% significance level. It thus suggests that theexistence of a quadratic function is not completely wrong. Interpreting the coefficients

implies that team chemistrymaximises a clubs league position at approximately 8points8. In theory, this advises managers to introduce at most several new players tofeature regularly each season. In reality, however, managers will regard player ability assubstantially more significant than team chemistry. Therefore, they are not likely tohesitate to introduce as many new, highly able players to the team as possible, in expenseof sustaining team chemistry.

The total number of players usedby a team during a season provides a sufficient proxyfor the injury rates in a team. The analysis finds that an increase in one player usedreduces league position slightly by roughly 9.5%. This relationship corresponds with thatfound in Pedaces (2008) analysis and implies that a higher number reduces overall teamstability, and thus have an adverse impact on league position. That is because a team thatuses more players is likely to have experienced periods, in which key players were absentthrough injury, and as a result, negatively affecting the overall performances of the team.

An analysis of the two variables, which measure the age characteristics of a clubsplaying squad, produces contrasting results. While, the impact of a squads average ageon league position is positive and statistically insignificant, the ratio of playersagedabove 29affects league position negatively. The analysis suggests that a 0.1 increase inthe ratio reduces league position by approximately 61%.

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Mathematical Appendix 5.3.17Mathematical Appendix 5.3.2

8Mathematical Appendix 5.3.3


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Intuitively, having older players can be highly beneficial to a team, as their experienceand knowledge may prove valuable. However, previous literaturehas shown that the skillsnecessary for success in professional athletic sports may decline with age (Berman, Down, &

Hill, 2002). Hence,the results suggest that in the increasingly competitive and physicalenvironment of the Premier League, the athleticism of younger players supersedes theexperience brought by older players. In reality, it makes most sense for managers to find a

balance between youth and experience that would benefit the team most.

Finally, thefirst lag of league position, rather surprisingly, suggests that teams finishingin higher league positions in a preceding season are likely to finish lower than what thehad achieved in the following season. This contradicts Tarlows findings, although thatmay be explained by the sizeable differences, in terms of the rules and structure, betweenthe NBA and the Premier League.

Managerial Stability

In contradiction to the relationships previously proposed by the correlation matrix, amanagerial change that occursbefore the start of a seasonhas a contrasting impact to onethat occurs during a season.

The model identifies that a change in managerbefore the start of a season reduces ateams final league position by 45.9%. Once again, evaluating the coefficient at the mean,the change at most reduces league positionby 2 places9. Intriguingly, by including afirstlagof this variable, the model finds that the same managerial change increases league

position by 57.2% in the following season. It thus suggests that a team requires one full

season before a managerial change can have its desired effect on team performance. Thismay be because players need time to understand and adapt to their new managersmanaging style and tactics. Moreover, a new manager may decide to bring in new playersinto the team, thus interfering with team stability. The negative correlation identified

previously in Table 2 between a change in manager and team chemistry supports thisidea.

On the other hand, amanagerial changeduring the seasoncauses an immediate increasein final league position by 39.2%. Although the coefficient is statistically insignificant,one can still make logical inferences from the result. Koning (2003) suggests that achange in manager creates a positive impact through a shock effect, where a new coach

is able to motivate the players better and can give the team a psychological boost. This isthen translated onto the pitch through better results and a higher league position. Achange in manager during the seasonis therefore more likely to occur at a club strugglingto achieve wins and is threatened by the prospects of relegation. The table overleafsuggests the total change in actual positions10that a managerial change has on teams inthe bottom three positions of the league, under the assumption that teams around them donot change their manager too.

9Appendix 5.3.4

10Appendix 5.3.5


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Position !!!"!!!"

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18 1

19 1

20 0

The table suggests that out of the three teams in the relegation zone, only the club residingin 18thposition is likely to avoid relegation after a change in manager. In reality, due tothe substantial financial benefits that are at stake from retaining a clubs Premier Leaguestatus, clubs in and close to the relegation zone are likely to take a gamble and replace anunder-achieving manager, in a bid to stimulate better performances.


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Section 4. Final Conclusions

4.1 LIMITATIONS AND EXTENSIONS

The most evident restriction that lies within my analysis is the limited number ofobservations that were used. This is caused by several reasons. Firstly, my analysis simplyexamines data across four consecutive seasons between 2007 and 2011. This relativelyshort time frame means that the interpretations of each variable may still not provide the

best representations of reality. Collating a comprehensive dataset that expands theexamined period, possibly even to the induction year of the Premier League in 1992,would certainly improve the overall reliability of the study.

Secondly, the number of observations has been constrained by the fact that only clubs that

had participated in three of the four seasons were analysed, in order to create a morebalanced dataset. This, therefore, meant that lower-ranked teams, who frequentlyexperienced relegation from the Premier League, were much less represented, creating atop-heavy dataset. My analysis could be developed further to incorporate these teams,

by running an ordinal probit model with a handful of categories that include one forrelegated teams. By evaluating marginal effects, I would be able to identify the levels ofeach control factor that are required by teams to move between the ordered categories.

Finally, there is reason to believe that a large degree of unobserved heterogeneity that Ihave failed to capture sufficiently within my model. Notably, due to a deficiency inavailable data, factors relating to the ability of specific players are unobserved. As such, it

is not possible to dissect, for example, the effects of particular transfers, which aregenerally overlooked by the net transfer spendingvariable. Similarly, the research could

be further extended to manager-specific analysis. One can examine, for example, thelevels of experience of managing in the Premier League each manager has obtained anelement that may strongly determine the individual ability of a manager. Hence,observing these factors would reduce the level of bias that may still exist in the model.

4.2 CONCLUSION

This paper set out to evaluate the extent, to which money plays a role in bring success to a

Premier League club, and in doing so, highlight how maintaining stability at a club cancontribute to success.

My findings conclude that whilst a strong financial position does contribute to success inthe Premier League, it certainly does not guarantee it. The first model, in which only11.7% of the variation is explained by the financial variables, justifies this argument.Furthermore, wage spendingbecomes statistically insignificant, once stability factorshave been accounted for, while the impact of a clubs net transfer spendingon league

position is identified to be minimal. The latter implies that the operations of PremierLeague clubs in the transfer market remain highly inefficient, and supports the previousfindings cited by Szymanski and Kuper (2012).


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The analysis also finds that team stability plays a significant role in determining a clubsfinal league position, as 74% of variation is explained in my model. The model suggeststhat stability can be achieved by building a squad with younger, more athletic players,who are able to perform well in a highly competitive environment. At the same time,maintaining a high level of team chemistry contributes to achieving a higher position,through developing a level of understanding on the pitch between regular players and ashared determination to succeed.

Finally, managerial stability is recognised as a significant factor affecting league position.The findings show that a change in manager before the start of seasonreduces league

position, implying that retaining a manager proves to be the best option for a club.However, the impact of a managerial change during the seasonsuggests the opposite. Achange during the season is typically associated with teams that are struggling to achieveresults and are being threatened by relegation. Hence, the club hierarchy look for a

solution to stimulate a recovery to avoid relegation. Particularly with the substantialfinancial incentives from maintaining a Premier League status at stake, disregardingmanagerial stability to improve results may prove to be the most beneficial decision.


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Section 5. Appendix

5.1 VARIABLE DESCRIPTIONS

Log-odds league position A log function of the final league position achieved by a teamin a given season in the Premier League. The function has been adapted from Szymanski(2000), giving weight in value to higher league positions.

First lag of league position The first lag of the dependent variable. This represents theposition that a club finished in at the end of a preceding season.

(Log) Wage spending The total spending on wages at a Premier League club, includingplayers and staff.

Net Transfer spending The sum of transfer fees received from selling players minus thesum of transfer fees paid for buying players.

Average squad age The mean age of the clubs playing squad. These were calculatedusing squad lists accumulated by the EUFO Football Squads database.

Ratio of players aged above 29 The percentage of players in the playing squad that areaged 30 or above. Both the media and fans perceive the age 30 as the age, when a playersvalue starts to depreciate considerably, due to his loss in athleticism.

Total number of players used The total number of players that have made an appearancein the Premier League in a given season, calculated using data from the World FootballStatistics database. This is used as a proxy for the injury rates in a squad, as a highernumber of players used suggest that key players have been missing through injury at some

point during the season and managers must call upon the less regular players in the squad.

Team chemistry Team chemistry is calculated by comparing the group of players whohave played the most minutes in a given season (using data from the World FootballStatistics database), with the equivalent group in the previous season. A point is allocatedfor every player who features in both groups, allowing a maximum of 11 points,representing complete team chemistry. The variable assumes that players who play the

most are more likely to have played with each other more frequently, allowing them todevelop a shared understanding on the pitch, as well as a shared motivation to bringsuccess to their club.

Managerial change before the start of the season A change in the clubs managerialposition that occurs during the summer break, before the start of a new Premier Leagueseason. This type of change may be triggered by a decision made by the current manager,as he may, for example, be looking for a new challenge and subsequently resigning fromhis current role. It may also be triggered by the club hierarchy, who are looking to

progress the club further by replacing their current manager with one that may have moreability and experience. The first lag of the variable is also generated to capture amanagerial change that occurs before the start of the previous season.


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Managerial change during the season A change in the clubs managerial position thatoccurs in the middle of a season. This type of change typically occurs at a club strugglingto achieve results and may be threatened by the prospects of relegation. The clubhierarchy may therefore decide to replace the manager and bring in a new manager withdifferent ideas and tactics, in a bid to boost team morale and stimulate performances.

5.2 MATHEMATICAL APPENDIX

5.2.1 Calculation method of!!

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(the change in actual league position from a unit

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5.2.2 Calculation of the gain in actual league position from a unit increase in team

chemistryat mean position !:

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Section 6. Bibliography

6. BIBLIOGRAPHY

Books

Kuper, S. and Szymanski, S. (2012) Soccernomics.Revised ed. New York: NationBooks.

Journals

Berman, S., Down, L., Hill, J. and Charles, W. L. (2002) Tacit knowledge as a source ofcompetitive advantage in the National Basketball Association.Academy of Management

Journal, Volume 45, No. 1, p. 13-31.

Koning, R. H. (2003) An econometric evaluation of the firing of acoach on team performance.Applied Economics, Volume 35, No. 5, p. 555-564.

Pedace, R. (2008) Earnings, Performance, and Nationality Discrimination in a HighlyCompetitive Labor Market: An Analysis of the English Professional Soccer League

Journal of Sports Economics April 2008 vol. 9 no. 2,pp.115-140.

Szymanski, S. (2000) A Market Test for Discrimination in the English ProfessionalSoccer Leagues.Journal of Political Economy, Volume 108, no.3, pp.590-603.

Publications

Tarlow, J. (2012) Experience and Winning in the National Basketball Association.Paper for the MIT Sloan Sports Analytics Conference 2012, Boston, March 2-3 2012.

Van Vugt, M., Hart, C., & Leader, T. (2008) Does stability foster team performance? AEuropean Football (Soccer) Inquiry, VU University Amsterdam.

Vlassopoulous, A. (2009) Determinants of NFL Franchise Revenue Generation.Bachelor of Arts Thesis. The Colorado College.

Reports

Deloitte & Touche (2008), Deloitte Annual Review of Football Finance 2008, Issue 17.




The Football Association Premier League Limited (2008), Premier League 2007/08Season Review.


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Online Databases

EUFO: European football squads since 1999, (URL http://en.eufo.de/).

World Football Statistics, (URL http://www.worldfootball.net/wettbewerb/eng-premier-

league/).

Documents

An analysis of the determinants of success in the Premier League: Does money guarantee success?