Dissertation_Truong Quang Hai_ID 1201912

7/30/2019 Dissertation_Truong Quang Hai_ID 1201912

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Table of Contents

I. INTRODUCTION 3

II. LITERATURE REVIEW 6

III. FOOTBALL TRANSFER MARKET FEATURES, MECHANISM AND FACTORS WHICH EFFECT PLAYERS

PRICES 10

1. Transfer market features 10

2. Mechanism 11

3. Factors which affect players prices: 13

IV. TESTING METHODOLOGY 14

V. TEST RESULTS, EXPLANATION AND IMPLICATION OF TEST RESULTS 21

1. Test for stationary in my data series 21

2. Test results 23

3. Explanations 28

4. Implications 36

VI. CONCLUSION 38

REFERENCES 41


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2

Dissertation of MSc International Money

and Banking

September 2012

Supervisor: Dr.James Reade

UNIVERSITY OF

BIRMINGHAM

TESTING EFFICIENCY OF PREMIER LEAGUE TRANSFER MARKET USING

CORRELATION BETWEEN TRANSFER SPENDING AND FOOTBALL SUCCESS

QUANG HAI TRUONG

Abstract:

This dissertation tests for the efficiency of English Premier League transfer markets

from 1995-2012 using data from 43 clubs. If market participants (football clubs) value

assets (players) accurately, the transfer expenditures of club will reflect their strength

and, as a result, their success in the Premier League. Some of the most interesting

results are the following. Firstly, the transfer market in English Premier League during

post-Bosman era can be considered as efficient. Secondly, there are no evidences

about the premium for British players, but there are evidences about premium for

attackers and players who played in a recent major championship. Finally, the

traditional Big Four Man Utd, Arsenal, Chelsea and Liverpool are the most efficient

clubs, in other words they can get success with relatively less spending.

Keywords: Dummy variables, Market efficiency hypothesis, Premier League,

Transfer spending.


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TESTING EFFICIENCY OF PREMIER LEAGUE TRANSFER MARKET USINGCORRELATION BETWEEN TRANSFER SPENDING AND FOOTBALL SUCCESS

MSc International Money and Banking

I. INTRODUCTION

There is a field where victory and success are based on rigorous statistical analysis,

ruthless drive of efficiency that capitalism demands, and democratization of

information causing a flattening of hierarchies. We are talking about the financial field,

with topics such as stock investment, M&A activity or foreign exchange high-frequency

trading? No, actually all of the terms above have been used on the famous book

Moneyball of Michael Lewis whose Liars Poker is considered as one of the most

classical books about Wall Street. Granted, baseball playing scenario is more static

than footballs counterpart, but a considerable proportion of footballs game plan (dead-

ball situations: free kicks, corners, throw-ins, penalties... and so on) is able to be

modelled1 . Presumably, we are living in an era when sports action and data analysis

are integrated deepest than ever: recently, the Premier League reigning champion

Manchester City has published a data analysis project named MCFCAnalytics, which

allow users to access to the full data set for all players in the whole 2011/12 Premier

League season. Kuper and Szymanski (2012) noted that data analysis has already

played a big role in player recruitment at four to five clubs, and this number probably

will increase in the near future. Hence football is not only one of the worlds most

popular and entertained sports, but also a potential area for testing economic theories.

1Sam Allardyce may be the one of most successful managers who used statistics and data to analyze games. His

Bolton team used to score 45-50% of their goals from set pieces, while the league average was about 33%.


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Nowadays, as employees (football players) are quite free in choosing employers

(football clubs), especially in the European Union2

- where every EU nationals enjoy

similar working rights in a common market and the performances of employees are

quite easy to be measured, European football transfer market offers a very good field

for testing the market efficiency and the valuation of human capital. Indeed, other type

of employees might be reluctant to work abroad because of the language barrier, but

this type of barrier is easier to pass in football football language is surely simpler than

business language. Apart from barriers created by government legislation, normal

employers may also be reluctant to hire a foreign employee because of the lack of

transparency on measuring performance: who knows whether a foreign junior banking

staff will perform better than a domestic one? Luckily, in football it is easy enough to

realize that Eden Hazard is a better player than Daniel Sturridge and clubs would not be

hesitated to recruit Hazard. Thus, the barrier to employment in European football

perhaps is one of the softest barriers in the worlds labour market. As Kahn (2000)

pointed out, there is probably no other field other than sports where we know the name,

face and performance history of almost every worker in the industry.

It would be optimal to make research throughout the whole European football world, but

the workload would be too huge and it is beyond the scope of an MSc dissertation.

Furthermore, not every football leagues in Europe have transparent data and reliable

2Before 1995, there were limits on foreign players quotas, but after Bosman case EU footballers are able to play in

every Member States in the European Union without any restrictions. Currently there are still some barriers on the

movement of footballers under 18 years old.


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statistics about players transfers. As a result, this dissertation will focus on ana lysing

the efficiency of the transfer market in English football, in particular the English Premier

League during 17 seasons from 1995-96 to 2011-12. Although there were some papers

looking on similar topics, most of them simply study the correlation between traditional

player statistics and their prices (Berg, E. Van den, 2011) and, as they admitted,

traditional statistics cannot fully reflect the performance of players (as I will explain

clearer in Chapter IV). This paper will use another proxy of measuring transfer market

efficiency: instead of estimating the correlation between individual factors and transfer

prices, I will study the relationship between clubs transfer expenditures and their

football success which will be measured by points they gain in the league. This

assumption is anything but reasonable: according to Tomkinstimes.com (2010), the

average amount of home-grown players on the Premier League from 1995 2010 is

roughly 1.5 players per match and, presumably, transfer should play an important role in

teams success if there is 9-10 players per side are not home-grown. Generally

speaking, transfer market efficiency means that all publicly information on the market

reflects on the players price and their prices are valued accurately, hence expensive

players and teams tend to perform better than cheap players and teams. In other words,

efficiency in the football transfer market means that other things being equal the

more a team spends on purchasing players, the higher its chance of getting success.

This paper is structured as follows: Chapter II discusses and summarise the ideas some

main working papers on football transfer market area. Chapter III describes the


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background of football transfer market, its regulation and mechanism and explain some

factors which affect players prices. Chapter IV describes the testing methodology, the

procedure of collecting and converting data and the test result. Chapter V draws some

implications of test result and explanations about the football transfer market. Ultimately,

chapter VI is conclusion one.

II. LITERATURE REVIEW

Can money buy success? is always a debated question in English football, especially

in the current period of globalisation when a lot of Premier League clubs are owned by

super-rich billionaires. Traditionally, people believe that a teams success must be built

gradually based on long-term investment such as youth academy, training facilities, and

building a team with balance, with certain characteristics that blend with each other

Sir Alex Ferguson. To an extent, Sir Alex was right: although he has spent the third

biggest amount of money during the Premier League, my results in following chapters

will reveal that his Manchester United team can be considered the most efficient during

last 17 years. But there is also a handful amount of examples about teams who

arguably bought their Premier League title by money, a lot of money Chelsea and

Manchester City to be named. My paper which is inspired by the work of Kuper and

Szymanski (2012) and Tomkins et al (2010) will try to provide another answer to this

debate.


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On Soccernomics (2012), Kuper and Szymanski have created a simple but convincing

way to study the efficiency of transfer market: they collected data about clubs wage and

transfer expenditures and studied the correlation between these two variables with

teams success on Premier League and Championship. After examining 58 Premier

League and Championship clubs, Kuper and Szymanski reached a conclusion that

teams wage bill can explain a remarkable percentage of their success (88.72%) during

1998-2007. They also test the correlation between transfer spending of 40 English clubs

between 1978 and 1997, but net transfer spending could only explain 16% of club

success. On the other hand, Tomkins et al used an extremely useful tool - the Transfer

Price Index to convert historical transfer prices into current price, allowing for inflation.

Using this index, they calculated how much each clubs squad cost each season and

how much does it cost on average to win the Premier League title, to qualify for

Champions League or to finish on the mid-table. This book Pay as You Play: The True

Price of Success in Premier focuses on individual clubs transfer effectiveness and does

not consider the impact of other teams spending, moreover Tomkins et al used gross

spending statistics instead of net spending.

There are, of course, other methods of testing transfer market efficiency. Using data

from all transfers to and from the English Premier League in 2008/09 and 2009/10

seasons, Berg (2011) presents a very detailed analysis about determinants of players

transfer prices, include future performance, players function and bargaining position of


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clubs and draw a somewhat surpriseconclusion that players function do not have any

significant influence on transfer fees, while age, games played last season and

domestic transfer premium. Unfortunately, some factors in the analysis remained

ambiguous and unreliable, for example the injury-proneness of a player3 or the available

transfer budget of a club4. Furthermore, variables in players function section may not

cover all necessary skills of a player: for example Berg assumed that players height

would affect transfer prices, but how could he value jumping and heading skills ? As I

will explain below, study the correlation between players function and transfer prices is

an extremely sophisticated job as there are so many skills that a football player needs

and a considerable proportion of them for example mental and technical skills are

impossible to measure. Similar to Berg, Carmichael et al (1999) also offers another

point of view on the relationship between players function and transfer fee, however

this paper only covered a few factors such as age, appearances, goals scored, goals

conceded (with goalkeepers and defenders), position (with dummy variables), national

team appearances and how many times this player has been transferred before.

Presumably, it is very difficult to value a midfielder using Carmichaels approach as his

most important assets are assists and the accuracy of passes. Dobson & Gerrard

(1999) studied 1,350 transfers in the English Premier League between 1990 and 1996

and found that career goal scoring rate, games and goals previous season, international

caps and goal difference of buying club last season have a positive relationship with

3Berg noted that there is a fair amount of noise in his use of injury-proneness and he admit that his method

collecting data on the number of weeks that a player was sidelined during previous season may be a very

inaccurate predictor.4As historical budgets are not easily available, Berg estimate a clubs transfer budget by its stadium capacity and

its presence on European competition. He admitted that this is an imperfect measure


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transfer prices while number of previous clubs and league position of both buying &

selling clubs last season possess negative correlation.

My paper will try to study the efficiency of transfer market by a different method. Instead

of study the correlation between players ability, club function and transfer prices, I will

research the relationship between transfer spending and club success, with the

assumption that clubs will win games if players play well. I suppose that net spending

(transfer fee paid minus transfer fee received) is a more accurate indicator for the

aggression of clubs in transfer market because it reflects the true changes on club

squads: clubs are generally strengthened with new transfer purchases, but they are also

weakened when players leave. Granted, some big clubs do not need to sell players for

money and their squads are not necessarily weakened even if they sell some stars. For

example, in my opinion, Chelsea was arguably benefited from selling Arjen Robben as

he to an extent - lost motivation and become more prone to injuries. However, there

are only three to four rich clubs which do not need to sell players to balance their books,

the others do not have such privilege and, in general, clubs strengths are reduced

when they sell players. Hence I will use net spending instead of gross spending as the

independent variable.


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III. FOOTBALL TRANSFER MARKET FEATURES, MECHANISM AND

FACTORS WHICH EFFECT PLAYERS PRICES

1. Transfer market features:

Football is by far the worlds most popular sport. And unlike (North American sports

which do not contain an official transfer market, the transfer market in European

football are highly organized. There are generally four main participants during a

transfer: the buying club, selling club, the player himself and the regulator

(national/continental/international football association). The buying club normally

have to pay a transfer fee for the selling club in order to get the service of the player,

unless he is a free agent under Bosman ruling. In this case the buying club would

not have to pay transfer fee and the deal is addressed as free transfer.

Although not an official participant himself, transfer agents are becoming very popular in

the modern era of football transfer market, as they practically act as a bridge between

clubs and players. Even the best football scouts do not fully know the whole market of

players, and players normally have a limited knowledge about their potential employers.

Furthermore, when contract terms and conditions become so complicated, clearly

players are not willing to negotiate all the complex law terms by themselves and it is


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better to let the negotiation task to their agents. An agent can give both players and

clubs wider and better choices.

2. Mechanism:

The mechanism in transfer market was completely changed after the Bosman rule.

Hence it will be useful if we divide the 20-years history of the Premier League into two

periods: before and after Bosman rule.

Bosman rule is named after Jean Marc Bosman, a Belgian player who tookhis club

(RFC Liege) to European Court of Justice because he was not allowed to freely move to

another club (US Dunkerque) even his contract has expired. As Dunkerque did not meet

Lieges transfer fee demand, Liege refused to let Bosman leave the club. According to

regulation in football transfer market at that time, in several countries buying clubs still

had to pay transfer fees to selling clubs in order to buy out-of-contract players. The

situation in English football was partially different: Simmons (1997) noted that from

1978, if the club does not offer a new contract with the same terms, the player has the

right to reject this contract and can move to a new club with no transfer fee attached.

Only in the case that the player whose contract has expired - rejected a new contract

with equal or better terms, the buying club would have to pay the compensation fee in

order to execute the transfer. It was clear that players in English league enjoyed far

more freedom than their colleagues in some continental European leagues, however

the European Court of Justice still amended the English transfer system in order to

apply the Bosman ruling.


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Before the Bosman rule a footballer was practically an asset of the club. Even if his

contract has been expired, another club would have to pay a transfer fee to sign him,

otherwise he would have to wait two years which is too long in comparison to a

footballers career - to become a free player. However the Bosman rule changed

everything. From 1995 a footballer became a free agent right after his contract has been

expired and he was free to negotiate with other clubs 6 months before his contract

ended. Hence the power shifted from club to players hands and the transfer fee has, to

an extent, decreased because buying clubs no longer had to pay transfer fees for

players whose contract has expired or has been expiring (although other factors such

as inflation, higher club revenue from TV rights, marketing helped to bring players

transfer fee to a new record).

If the players contract still has more than 6 months left, the buying club will have to

approach the selling club first to make an inquiry whether they want to sell this player or

not. If the selling club also wants to sell this player, these two clubs can negotiate a

transfer fee. If they can reach an agreement in transfer fee, the buying club will have the

right to approach the player and negotiate personal terms such as wage, bonus, image

rights... and so on. If the buying club approach their transfer target without permission

from selling club, this type of action will be considered illegal. Furthermore, clubs can

use as much European Union (EU) players as they want and there are no restrictions

on player mobility within the EU.


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3. Factors which affect players prices:

As there are three parties (selling and buying clubs, player himself) included in a

transfer, a players transfer price is generally determined by internal and external

factors.

Internals are factors which belong to this player only: age, league and national team

appearances, position-specific performance, remaining years in contract, buy-out clause

(if available) in contractand so on. Clearly, age plays an important role in determine a

players price. His price will be highest during the peak period (normally 25-28) and

gradually lower before and after this period. Generally speaking, younger players are

not experienced enough to perform on the biggest stages, while older players are

physically weaker and has little value to be re-sold. About position-specific performance,

typically a striker will be valued by the amount of goals which he scores (although it is

not always true, there are some types of striker whose task is not only score goals but

also holding the ball, create space for teammates or assist), a goalkeeper will be valued

by his number of clean sheet, an attacking midfielder will be valued by his number of

assists and so on.About contract time remaining, after studying 239 transfers on the

German Bundesliga, Feess and Muehlheusser (2003) suggested that remaining

contract years have a positive correlation with transfer prices.


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External factors are buying and selling clubs financial ability, alternative choices and

regulation. About regulation, obviously the Financial Fair Play regulation5

has a strong

influence on European clubs willing to spend on transfer market, for example rich clubs

like Man City has to offload fringe players before they can purchase new signings,

otherwise they might be banned from European competition. Alternative choices are

also very important: Robin Van Persie is, undoubtedly, a very good player but Man City

is reluctant to pay a huge fee for him because they already have many other attacking

options Last but not least, financial abilities of both buying and selling club are very

important: in-debt clubs such as Valencia years ago had to sell their best players (Villa,

Silva, Mata) at a relatively cheap price to pay for their debt, while rich clubs such as

PSG can afford huge price for a player whos not a superstar just because their owners

are so rich.

IV. TESTING METHODOLOGY

In an optimal scenario, all data related to internal and external factors needs to be

collected and measured to check whether a players transfer price reflects all the market

information or not. However, collecting such data is a very difficult job as some of them

are ambiguous and, at best, unreliable. For example: the remaining time on a contract

surely plays a very important role on determining his transfer price. According to Vialli

and Marcotti (2006), 1.5 years remaining become a point of balanced power between

5This is a effort of UEFA to prevent European clubs from spending more money than they earn. During the period

from 2011/12 to 2014/14 seasons, clubs must not loss more than 39.4 million if they want to participate in

European club competitions.


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clubs and players, with 2 or more years the power shifted into clubs hands and the club

tends to ask for higher price than his fair value, with 1 year or less the player will hold

the power and the club may be forced to sell him at a lower transfer price. Undoubtedly,

studying this variable will be extremely useful but there is no clear and reliable source

for all transfers. Frick (2007) noted that information on contract duration is difficult to

obtain and further empirical study on this area is required. The same story happens with

buy-out clause: presumably, higher fee on buy-out clause (if it exists) on a players

contract tends to drift transfer price higher, but it is extremely hard to collect that

information for all transfers.

Furthermore, these criteria such as number of goals/assists/clean sheets. seem to be

too simple to measure players value, for example Xavi Hernandez (or Michael Carrick,

to an extent) is a very good player but his contribution is mainly ball retention and tempo

control, hence how can we measure his value by number of goals and assist ? With

defenders and defensive midfielders the situations are far more complicated: of course

Rio Ferdinand or Javier Mascherano do not score nor assist frequently, and we need to

value them through their amount of successful tackles, for instance. However there is a

famous story about Sir Alex Ferguson mis-sold Jaap Stam partially because he believed

that the Dutch defender is declining as his number of tackles decreased: in fact Stam

and other great defenders (Maldini, Baresi) choose their positions so well that they

did not have to make so much tackles (Kuper and Szymanski, 2012). Hence, we need

even more detailed analysis perhaps all Optas statistics - about a players

performance to measure his value and this task is almost impossible in the scope of this


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MSc dissertation. Berg (2011) also states that his approach using direct measures of

player performance is too individual-focused: it cannot demonstrate the actual

contribution of a player on the probability of winning or losing a game of his team.

The external factors are also very interesting but very hard to measure as well.

Obviously, the Financial Fair Play (FFP) regulation of UEFA will probably limit the

transfer spending of clubs, but because the information about FFP was leaked to the

public five years before this regulation come to effective, it is difficult to assess whether

this regulation has effect on a particular year and particular transfer. For example, clubs

have three years (up to 2014-15 season) to balance their football-related expenditures

(money spending on transfers and wages) and Martin Samuel (Daily Mail, 2011)

suggested that rich club such as Man City might not afraid of spending huge amount of

money on 2010-11 season to qualify for Champions League next season, knowing that

the revenue from Champions League would help them to balance books. Financial

ability of both buying and selling clubs is important as well, but many of Premier League

transfers include one foreign party (Italian, Spanish, German, Frenchclubs) and

financial data of these clubs are not always available.

To avoid such noises and obstacles, I will choose another proxy to study the efficiency

of the transfer market. Instead of measuring all internal and external factors as above, I

will study the correlation between the transfer expenditures and football success of

Premier League clubs. If the coefficients for transfer spending are significant and

different from 0, and independent variables can explain a considerable proportion in the

variation of dependent variables, the transfer market in English Premier League can be


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considered as efficient. Indeed, the basic concept of efficient market is that market

information will determine asset prices. With the semi-strong form, all publicly

information would be reflected on asset prices. It is relatively hard to define the term

publicly information on financial markets such as stock or currency, but luckily

enough it is not that hard to assess a players skill based on his performance on the

football pitch, especially on this highly-commercial era of football when top-tier clubs

might play more than 60 matches per season. In football, publicly information is simply a

footballers performance on the pitch. If the football transfer market is efficient, players

performance will reflect onto his price: the better one player is, the higher his transfer

price is. In other words, expensive players will tend to perform better than cheap

players. Hence, it is possible to test the efficiency on the transfer market using the

correlation between transfer spending and sport success of football clubs, using

assumption about a simple chain: club buys players, players play well, team wins

games, team is successful.

Granted, there are several factors which can determine the result of a particular match

and an expensive team may lose even if their players perform well because of bad luck.

A player can be playing his best and doing a lot of good things, but the results may just

not be happening for the team due to the uncertainties associated with football.

However, true luck should even itself our over years and if the dataset is big enough,

the effect of luck can be believed to diminish.


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To study the correlation between transfer spending and football success of Premier

League clubs, I collect all data about transfer for 20 Premier League clubs each season,

from 1995/96 to 2011/12 season. Transfer data is collected mainly from clubs financial

accounts and reputable websites such as transfermarkt.co.uk and transferleague.co.uk.

Data was cross-checked on trusted media sources such as English newspaper:

guardian.co.uk, telegraph.co.uk, dailymail.co.uk and several sport websites worldwide:

espn.go.com, sportsillustrated.cnn.com, football.uk.reuters.com and the Deloitte Annual

Review of Football Finance when possible. Clubs successes are measured by the

amount of points they gain during a particular season. Personally, I suggest that league

point is a more accurate tool to measure success than league position because of two

reasons. Firstly, points can demonstrate the difference between teams performance

better, for example: the difference between Man Utd and Arsenal in 2011/12 season is

19 points (89 in comparison with 70) but only 1 position (2 compared with 3). Secondly,

points can reflect the difference between the same teams performances in different

seasons. For instance, Man Utd attained 91 points in season 1999/00 and just 79 points

in season 1998/99 a 12 points difference but they won the Premier League title in

both seasons, so their positions are the same. .

To test whether transfer spending has a positive with clubs football success or not, I run

a regression between Premier League clubs transfer spending (with lagged spending)

and their respective football success in every season of Premier League. The concept

of lagged spending is quite clear: when clubs buy a new player, generally they do not

require him to play extraordinarily well in the very first season as this player needs time


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to settle down (normally it is easier to settle if he is from the British Isles, more difficult if

he is from overseas), adapt the playing style of new club, get familiar with new

teammates and coaches... and so on. Normally, a clubs squad can contain players that

they bought from two or three years ago and these players still have contribution to the

teams success (Fernando Torres is a good example: he played terribly during his first

season at Chelsea but is gradually recovering his form in the current season). Hence

the success of a spectacular team on a particular season does not depend only on its

spending in this season but also on previous seasons. However, the number of lagged

spending on previous seasons which we include in regression model should not excess

3, because the average time that a player spends in one club is around 3-4 years. For

newly promoted clubs who did not participate in the Premier League in the previous

season, I will use their transfer figures at Championship because it is all spending in

recent years which might help explain success in any given year.

Besides the dependent and independent variables, I also add dummy variables for each

team which has participated in the Premier League during the research period. From

1995 to 2012, there were 43 teams have played in the Premier League, hence I have 43

dummies for each team, from D1 to D43.

By using dummies, I will allow each club to have a different intercept, hence I can

compare the slope which is the relationship between spending and success of one


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particular team with other teams. In other words, I can examine whether one club is

more efficient than another.

The regression formula is as below:

(1)

With is club success on season t

is clubs transfer spending on season t

is clubs transfer spending on season t-1



D1 = 1 if it is Arsenal, D1 = 0 if it is other teams

D2 = 1 if it is Aston Villa, D2 = 0 if it is other teams

... and so on for all 43 teams.

I will run one big regression for all 17 Premier League seasons that I examine.

The benchmark is 50%: if independent variable (net spending) can explain more than

50% of dependent variable (success), there will be a positive correlation between clubs


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transfer spending and success. If beta value of at least one explanatory variable is

significant and different from 0, hence the transfer market can be considered as

efficient.

V. TEST RESULTS, EXPLANATION AND IMPLICATION OF TEST

RESULTS

1. Test for stationary in my data series

Before run the regression between transfer spending (include lagged variables) and

club success, I need to test for stationary of my data set first and make sure that my

data is stationary.

Non-stationary data can not be viewed as good input because of the potential

spuriousness problem that it can create. The ultimate target of regression analysis

technique is to estimate the real correlation between explanatory variables and

response variable correctly and precisely. However, if we simply consider the raw

number data set without the incorporation of economic theory, some fake relationship

between variables can happen though they may not have any relationship on the real

world. For example, both the rainfall in Vietnam and the unemployment rate in Southern

Europe has risen sharply in recent years and perhaps their rates of increase have some

relationship on purely number analysis, but obviously these two outcomes do not have


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any direct effect to each other in the real life. This fake relationship can be illustrated

by two drunken people walk randomly in the same direction. In fact they do not have

any incentive to follow each others route, but because of their random walks

observers may feel that they have fairly strong relationship, rather than such a

coincidence. By mathematical language, non-stationary data can be described as:

= + + with = 1, therefore = + + or= + .

That means this variable may not have the trend to move towards its equilibrium level

(since no such level exist) and only responds to new, random information (

Here below is the result for stationary test, using Statistics by PcGive/Unit-root test on

OxMetrics

Yt: ADF tests (T=337, Constant; 5%=-2.87 1%=-3.45)

D-lag t-adf beta Y_1 sigma AIC

0 -20.97** -0.13307 15.49 5.487

\Xt: ADF tests (T=337, Constant; 5%=-2.87 1%=-3.45)


0 -18.99** -0.034892 22.49 6.232

Xt-1: ADF tests (T=337, Constant; 5%=-2.87 1%=-3.45)



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0 -19.56** -0.066740 21.79 6.169



0 -18.91** -0.029781 20.7 6.066



0 -17.69** -0.030734 19.29 5.925

With all variables are stationary, I can carry out the regression without further data

modifying.

2. Test results

As I choose a model with lagged spending until season t-3 and the Premier League only

began on season 1992/93, the earliest season that I can start study is 1995/96 (if I start

with earlier season, will not be in the Premier League)


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Using OLS regression on OxMetrics6 to study the correlation between net spending and

success of Premier League clubs during 17 seasons from 1995/96 to 2011/12, I get the

results for quantitative variables as below:

Coefficient Std.Error t-value t-prob

Constant 31.9171 6.376 4.89 0.0000

Xt 0.0725144 0.02555 2.84 0.0049

Xt-1 0.12707 0.02786 4.56 0.0000

Xt-2 0.0272205 0.02932 0.928 0.354

Xt-3 0.0557588 0.02843 1.96 0.0508

R^2 0.71

With t-prob ofandlarger than 0.05, we do not reject the null hypothesis that

these two variables are insignificant. In contrast, andare significant as their t-

prob are smaller than 0.05.

Test for general restrictions:

&1=0;

&2=0;


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GenRes Chi^2(2) = 38.156 [0.0000]**

Hence we can reject the hypothesis that and= 0.

The value of R^2 is 0.71, which means the independent variables (net transfer spending

on the current and the previous season) can explain 71% of variation in dependent

variable (teams success measured by points). Thus, to an extent, the transfer market in

English Premier League can arguably be considered as efficient. Furthermore, one of

the direct conclusions from the result above is that, generally speaking, transfer

spending of the seasons 2 and 3 years ago do not have a significant effect on clubs

success.

Besides that, the result for qualitative variables (dummies) is as below:

Dummies Coefficient T - prob

D1 - Arsenal 42.1399 0.0000

D2 - Aston Villa 19.9842 0.0032

D3 - Blackburn 16.9707 0.0127


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D4 - Bolton 12.403 0.0704

D5 - Chelsea 31.7174 0.0000

D6 - Everton 19.965 0.0032

D7 - Fulham 11.8553 0.0873

D8 - Liverpool 31.6271 0.0000

D9 - Man City 11.4638 0.1002

D10 - Man Utd 47.1726 0.0000

D11 - Newcastle 20.7216 0.0023

D12 - Norwich 7.70734 0.3913

D13 - QPR 3.18549 0.7203

D14 - Stoke 10.5406 0.1765

D15 - Sunderland 5.3888 0.4362

D16 - Swansea 14.7369 0.181

D17 - Tottenham 20.449 0.0025

D18 - West Brom 3.65095 0.6192

D19 - Wigan 10.4137 0.1494

D20 - Wolves 0.716584 0.9268

D21 - Blackpool 7.43537 0.4993


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D22 - Birmingham 10.9313 0.1378

D23 - West Ham 14.8753 0.0293

D24 - Barnsley 3.37153 0.7592

D25 - Bradford -0.450164 0.9601

D26 - Burnley -1.94262 0.8598

D27 - Charlton 12.4793 0.08

D28 Coventry 10.2329 0.1642

D29 - Crystal Palace 1.80152 0.8411

D30 Derby 7.87135 0.2758

D31 Hull 2.31749 0.8331

D32 Ipswich 19.2679 0.0327

D33 Leeds 22.3836 0.0016

D34 Leicester 13.0829 0.0707

D35 Middlesbrough 10.6637 0.1191

D36 - Nottingham Forest 9.9728 0.2259

D37 - Sheffield United 5.11177 0.6421

D38 - Portsmouth 10.9893 0.1289

D39 Reading 13.1147 0.145


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D40 - Sheffield Wednesday 8.81745 0.242

D41 Southampton 12.2199 0.0803

D42 Watford -5.19343 0.5636

D43 Wimbledon 11.7918 0.1179

The figures above suggest that Man Utd (coefficient value is 47.1726), Arsenal (beta

value is 42.1399), Chelsea (31.7174), Liverpool (31.6271) are actually the most efficient

clubs. In other words, given the same level of net transfer spending, these four clubs

can achieve far more success than the rests of Premier League. Arsenals efficiency is

hardly a surprise: Arsene Wengers smart transfer policy is broadly recognized, and

Man Utds efficiency can also be explained by the appearance of Golden Generation

with Ryan Giggs, David Beckham, Paul Scholes, Gary Neville, Nicky Butt, Phil Neville.

All these players are home-grown, hence Man Utd did not have to pay any transfer fee

for them but they have helped the club to enormous success. However, Chelsea and

Liverpools efficiency are more difficult to explain: Chelsea was presumably the leagues

biggest spenders before the rise of Man City, while Liverpool did not win any Premier

League title and only had twice runners-up during the research period.

3. Explanations


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There results about R^2 and the significance of independent variables above indicate

that the English Premier League transfer market is efficient, but certainly it is not a

perfect market as R^2 is 71%. Apart from the randomness nature of football and,

admittedly, the uncertainty of my causal link, there may be some other reasons why the

transfer market is not perfectly efficient.

Why the transfer market efficiency is not perfect

Firstly, some nationalities are overvalued and some other nationalities may be

undervalued both of these two issues lead to an inaccurate valuation of players.

Established managers such as Arsene Wenger, Rafa Benitez, Sven-Goran Eriksson

and Martin Jol (notably, all of them are foreigners) have argued that English footballers

are often overpriced in comparison with their foreign counterparts, with disappointed

transfers such as David Bentley (17m, Tottenham), Francis Jeffers (8m, Arsenal),

James Beattie (16m, Everton) to name just a few. However, their opinions are not

necessarily true: as a players ability includes visible (passing, shooting, tackling, height,

weight...and so on) and hidden ability (tactical, work rate, influence, aggressive... and so

on), English (and British players in general) footballers hidden value may make up for

their relatively limited ability on the field. For example, they probably will settle down

quickly in the new club and will not have such relocation risk6, they are trained to play

in 4-4-2 formation that lots of Premier League managers prefer, they can help create a

6The risk that a new, foreign player fail to adjust to the new culture, new language, new society and underperform


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good atmosphere in the dressing room and after some years playing at the club they

can become not only managers messenger on the pitch but also mentor to the younger

players.

Nonetheless, English is not the only nationality which is believed to be overvalued on

the transfer market. Kuper and Szymanski (2012) suggest that Dutch and Brazilian are

overvalued nationalities as well, with the latter becomes the most favourable nationality

on transfer markets. Arguably, clubs are often willing to pay some premium for Brazilian

and fashionable nationalities - footballers and this behaviour makes the transfer

market less efficient. But the favour upon some particular nationalities are not only

because of clubs biases, it is also because of football regulation. Currently English

football regulators require a non-EU player to meet strict criteria: either played in at least

75% international games of their national team, and their national team must rank at

least 70th

on FIFA average ranking over a two years period prior to the work permit

application, or has significant contribution to the development of football at the highest

level. Hence clubs tend to pay a premium on EU nationals (compared with non-EU

nationals) who do not need to apply for a work permit, as Berg (2011) points out. It may

seems illogical that unrestricted supply of a good (EU players) lead to increase in price,

but clubs may want to avoid the complex procedure to registering a non-EU player,

especially if he does not meet the criteria above and has to be considered under the

category significant contribution to the development of football. In fact, Szymanski

(2000) pointed out that there is statistically significant evidence of racial discrimination,

which means clubs tend to pay less money for black players (although not every black


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players are non-EU nationals). In summary, non-EU nationals are generally

undervalued as clubs have to overcome lots of obstacles in order to obtain their

services and unfashionable nationalities may also be under-priced: Frick (2007)

suggested that Eastern European players actually suffered from discrimination in

Germany (though the situation in English football may be slightly different).

Another possible reason for the inefficiencies in football transfer market is that, certain

positions are overvalued. Normally attackers (strikers, attacking wingers/midfielders) are

over-priced because they are considered as the difference-makers in the team and they

can create flamboyant, magic moments. On the other hand, defenders and goalkeepers

are usually undervalued because they do not appear much on highlights, they are

judged as doing dirty works, they cannot make the team win themselves and

defenders can only help their teams not-lose.Arguably, attackers have to do a tougher

job than defenders as they have to beat the defender and put the ball accurately into the

net, while defenders only need to clear the ball away. Furthermore, in modern football

attackers are normally outnumbered by defenders because most teams use a 4-men

defence (4-4-2, 4-3-3, 4-5-1, 4-2-3-1... and so on). Consequently, clubs tend to pay

more money for an attacker than a defender.

However, all of the arguments above are purely theory. To test whether nationality and

position have a significant influence on players transfer fees or not, I carry out a simple

regression with two dummy variables for nationality and position. I hand-collect data for

all transfers during three seasons 1998/99, 2005/06 and 2011/12, with the final sample

includes 434 transfers. The regression equation is as below:


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(2)

With D1 = 0 if this is a non-British Isles player, 1 if yes.

D2 = 0 if this is a non-attacker, 1 if yes.

Here above I categorize English, Scottish, Welsh, Northern Irish and Irish players in to

British Isles group. While Scotland, Wales and Northern Ireland are all parts of the UK,

Carmichael et al (2000) noted that Irish players were not treated as foreigners even

before the Bosman ruling, and they also share the common language (English) and

culture. Hence, Irish should be categorized as non-British Isles (domestic) players

rather than foreign.

As a result, the base group on model (2) is non-British Isles and non-attackers. All

comparisons will be made in relation to this group.

The regression result is as below:


Constant 3.89695 0.4426 8.8 0.0000

D1 -1.38949 0.5681 -2.45 0.0150

D2 1.86279 0.5957 3.13 0.0019

Coefficients for both dummies are significant. As coefficient for D1 is less than zero,

non-British Isles players actually have higher transfer prices than British Isles

colleagues, which is quite a surprise and against the traditional stereotype. This result


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could be due to the fact that a considerable proportion of domestic player transfers in

Premier League include junior, relative cheap players, however there is no evidence on

the over-valuation of British Isles players. Berg (2011) also found the same conclusion

that UK nationality, in fact, has negative relationship with transfer prices. In contrast,

Pedace (2008) found that South American players in English professional soccer

(including Premier, First and Second Division) are overpriced in comparison with their

productivity contributions.

On the other hand, attackers are valued relatively higher than non-attackers, which

confirm the theory that clubs tend to pay a premium for attacking players

Moreover, players who perform well at major championship are often overvalued as

their prices are justified on just a good month with maximum 7 matches. The midfielder

Kleberson played extremely well for Brazil in the 2002 FIFA World Cup and even

contributed an assist in the final against Germany, after that Sir Alex Ferguson stated

that Kleberson won the World Cup for Brazil last summer. Man Utd paid 6.5 million to

sign him, however Kleberson would only make 20 appearances during his two seasons

at Old Trafford and became a big disappointment. During the same World Cup,

Senegalese striker El-Hadji Diouf also made a strong impression and helped his team

reach the quarter-final. Nevertheless he was a big flop at Anfield after scoring only three

goals in two seasons. Jamie Carragher even referred to Diouf as a forward with one of

the worst strike rates in Liverpool history, and the only No.9 failed to score even a single

goal during one whole season. Even a World Cup winner, Stephane Guivarch, failed to

make any impact at Newcastle after cost the club 3.5m. Guivarch played only four


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matches for Newcastle, scored one goal before leaving for Rangers.Roman

Pavlyuchenko is another example: after scoring 3 goals in 5 matches of Euro 2008 final

and be named on the Team of the Tournament, he was bought by Tottenham Hotspur

for a fee around 14 million. However, Pavlyuchenko never fulfilled the expectation and

had to accept a substitute role. Generally speaking, this problem is alike to the

Representativeness theory in behavioural finance, when market agents tend to make

decision based on a small sample sizes.

To test whether a World Cup premium really exists, I collect all transfers data from

three post-World Cup seasons: 1998/99, 2002/03 and 2010/11, with the final sample

includes 295 transfers and run a regression with one dummy variable:

(3)

With D1 =1 if this player has already played in the recent World Cup, D1 = 0 if not. The

base group is players who did not participate in the World Cup.

The result is as below:


Constant 3.70877 0.3934 9.43 0.0000

D1 4.45690 0.8722 5.11 0.0000

With coefficient of D1 bigger than zero and is significant, there is an indication that

recent World Cup (and perhaps other major international tournaments) performer will be


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valued higher than players who did not play in recent tournaments. Nonetheless, there

is another argument that these World Cup stars are probably better players than non-

World Cup players, hence their transfer prices should be higher as a result and it is still

unclear whether World Cup players are priced at their fair value.

Kuper and Szymanski (2012) also suggest another explanation that a new manager

usually wastes money. As he wants to add his own mark on the team, a new manager

will try to buy some new players just for the sake of spending and does not really care

whether it is a good long-term investment or not. In most cases, he does not have the

incentive to save money for the club as he will not get more bonuses if the club makes

profit. In the context of microeconomics, this situation can be drawn into information

asymmetry, and by more details, moral hazard category where a party (the manager)

have incentives to take risks (buy new players), knowing that he probably will not have

to bear the costs that could incur(clubs financial loss).

This problem is also partially noticed by Tomkins et al (2012) as the Newcastle effect:

when a manager moves to a bigger club, he will needs more money to achieve the

same level of success as with his former club. For example, Tomkins et al noted that

Mark Hughess cost per point at Blackburn was 584,081, however after he was

appointed as the manager of Man City Hughess cost per point rose to 1,557,045

nearly three times higher. According to the same source (Tomkins et al) Martin ONeill

needed to spend 453,849 to get a point at Leicester City, but he would have to spend

1,183,892 for one point at Aston Villa later, while Steve Bruces cost per point rose

sharply to 1,176,778 at Sunderland in comparison with 613,370 at Wigan earlier.


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Nevertheless, this reason remains debatable as not every new manager spends huge

sums on transfer, especially when his new club are not financially strong. Furthermore,

samples are not always comparable at these managers time at their clubs can be

highly different from each other, thus a simple cost per point figure may not reflect the

true story. As I can only provide arguments without quantitative testing, it would be very

useful if future analysis can examine the existence of this new manager effect.

4. Implications

The question of market efficiency has a very important place on investment valuation

theory. If there is evidence that market may be inefficient, there would be chances for

market participants to exploit by buying undervalued assets and selling overvalued

assets. In the context of football transfer, that means buying underpriced players and

selling overpriced ones. Though football has its own characteristics and having the best

assets (players) does not guarantee winning games and football success, it clearly

gives the club an big edge, especially if we note that recent studies did not find

evidence for strong managerial effects on English football clubs performances. Audas

et al (1997) indicated that clubs do not receive any short-term benefit from changing

manager after a spell of bad results, while Szymanski (2000) quote Johan Cruyff one

of the most influential players and managers during the history of football saying that

if your players are better than your opponents, 90 percent of the time you will win.

Hence it is worth study the efficiency of the football transfer market and try to figure out

the gap (if it exists) between a players true value and his market value.


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The result above implies that English Premier League transfer market is quite efficient

and clubs value players transfer prices quite exactly. This means, in general, good and

useful players will have higher transfer fees than bad players and clubs may not be able

to maintain the same level of success without keep spending money on purchasing new

players, unless they have a very good generation of home-grown players. There is not a

coincidence that Man City has emerged from virtually nothing to a dominance power

after spending 400 million on net transfer during last 4 seasons, while Arsenal cannot

compete for the Premier League title any longer after earning 19 million from players

trading during the same period.

Nevertheless, as the transfer market is far from perfect, there maybe some effects that

clubs can take advantage of. Firstly, regarding to nationality, clubs should not be afraid

of buying British talents as, in the scope of this dissertation, there is no evidences that

UK and Ireland nationality might be over valued. Besides that, they should carefully

scout South American players before make a purchase as this category of players often

be over priced. Furthermore it may be useful to look at the non-EU players market,

especially Eastern European and black players because these players are arguably

suffered from discrimination. Secondly, with some indications that players who played at

recent major championship might be valued at a higher level than their fair price, thus

clubs should be careful and selective and, if possible, simply buy him before the

tournament kicks off. For example, before World Cup 2010 Sir Alex Ferguson has


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bought Chicharito who would score 20 goals in his very first season at Man Utd - for

only 7 million to avoid the risk that his price might be inflated a lot after a successful

tournament.

Another interesting finding is that coefficient of net transfers spending on the last

season ( has the biggest value among four seasons. It can be loosely interpreted

that, in general, players who were recruited in the previous season will have biggest

contribution on clubs success in comparison with other groups. Although it does not

mean that teams should sell players who are in their 3rd

or 4st

year, this fact above

presumably indicates that clubs and fans should be patient with their new signings in

their very first year.

Last but not least, net transfer spending on season (t-2) and (t-3) do not have significant

effects on club success because their coefficients are not significant. This insignificance

may be due to the speed of purchasing new players of clubs: as they buy a handful

amount of new players every season, older players who arrive in 2 or 3 years ago would

be gradually replaced, thus transfer spending on (t-2) and (t-3) season are not

statistically significant.

VI. CONCLUSION


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This paper tries to test the efficiency on football transfer market by collecting related

data on Premier League transfers and studying the correlation between clubs net

spending and success. It also examines the existence of such a premium about British

players, attackers and recent World Cup players. Though there are, admittedly, some

weak points in my causal link (perhaps the weakest point is team win games if players

perform well) and transfer figures might not be 100 percent correct as there is no direct

confirmation from clubs, my dissertation has find some evidences about the efficiency of

the English Premier League transfer market and, to an extent, confirm the existence of

efficient market hypothesis. In other words, players are valued fairly accurate and

normally teams who consistently spend more money will get more success in the long

term. The efficiency of English Premier League transfer market might be due to the

liberalization of European labours market, the wider and deeper information that clubs

possess about potential targets or the English model of football management. In several

continental European countries Italy perhaps the best example Vialli and Marcotti

(2006) suggest that the sporting director also weighs strong influence in transfer market

and the manager is more like a first-team coach. In England, managers not only carry

out training and tactical planning but also take responsibility for players transfers. Thus,

Premier League managers arguably know better than their continental colleagues about

how to extract the best of talent from his transfer purchase.


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Besides that, it also shows that given the same level of net spending, some clubs can

get more success than others and it is interesting to note that four clubs with highest

intercept values much higher than the rests - during the research period are

traditionally Big Four: Man Utd, Arsenal, Chelsea, Liverpool (although Liverpool did not

win any Premier League title, their average league position is still higher than other

league winner such as Man City or Blackburn). Furthermore, at least within the scope of

this dissertation, it points out that the common stereotype about over-priced British

players is not necessarily true.

My research clearly has some limitations and it can be improved. First and foremost, it

would be optimal to establish an ELO-styled ranking system for all Premier League

clubs, with different weights for different competitions, instead of using plain points. This

ranking system should, generally speaking, reflect performances of Premier League

clubs better and it would provide a more exact view about the correlation between

transfer spending and success.

Then, it would be very useful and interesting to test the invariance proposition of Coase

Theorem using pre-Bosman and post-Bosman data. According to Coase Theorems

invariance proposition, the same outcome will happen regardless of the property rights

distribution. In the context of football transfer market, that means the movement of

football players during post-Bosman (free agent) era will be the same as pre-Bosman

era. I will hope for further analysis with much curiosity.


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