Match Results and the Equity Value of Publicly Traded Soccer Teams

Match Results and the Equity Value of Publicly

Traded Soccer Teams

Bryant University Honors Program

Honors Thesis

Student’s Name: Ryan Bebyn

Faculty Advisor: Dr. James Bishop

Editorial Reviewer: Dr. Kevin Maloney

April 2023

Table of Contents

Abstract ..................................................................................................................................... 1

Introduction ............................................................................................................................... 2

Literature Review ...................................................................................................................... 2

Overview of Publicly Traded Clubs ...................................................................................... 3

Overview of European Soccer Indexes ................................................................................. 3

COVID-19 and the Macroeconomic Environment ............................................................... 3

Financial Regulations ............................................................................................................ 4

Ownership Structure.............................................................................................................. 5

Investor Behavior & Relative Valuation ............................................................................... 6

Measuring the Impact of Specific Wins ................................................................................ 7

Domestic versus International Wins ..................................................................................... 7

Rival Wins ............................................................................................................................. 8

Individual Player Transactions .............................................................................................. 8

Hypotheses .............................................................................................................................. 10

Methodology ........................................................................................................................... 11

Regression ........................................................................................................................... 11

Daily Return and Net Return............................................................................................... 12

Sample: Publicly Traded Teams ......................................................................................... 12

Sample: Benchmark Indices ................................................................................................ 13

Results & Findings .................................................................................................................. 14

Manchester United .............................................................................................................. 14

Juventus ............................................................................................................................... 16

Borussia Dortmund ............................................................................................................. 17

Olympique Lyon ................................................................................................................. 19

Galatasaray SK .................................................................................................................... 20

AS Roma ............................................................................................................................. 22

SL Benfica ........................................................................................................................... 24

Celtic FC ............................................................................................................................. 26

FC Porto .............................................................................................................................. 27

Lazio .................................................................................................................................... 29

Ajax ..................................................................................................................................... 30

FC Copenhagen ................................................................................................................... 32

Sporting Club Portugal ........................................................................................................ 34

Conclusion .............................................................................................................................. 35

Further Analysis ...................................................................................................................... 36

Appendices .............................................................................................................................. 37

Appendix A – Complete Regression Results ...................................................................... 37

References ............................................................................................................................... 42

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 1 -

ABSTRACT

This study aims to examine the relationship between the match performance of publicly traded

European soccer clubs and their stock price returns. It is important for investors, owners, and

fans to understand how competitive factors both on and off the field affect the equity value of

European club soccer teams. Competitive factors such as basic match results (Win, Loss, or

Draw), rivalry matches, and international matches will be evaluated against the daily stock

prices of publicly traded soccer clubs. A sample of teams traded on public exchanges will be

used to analyze the effects of their matches on the daily stock returns for the respective clubs.

These daily returns will be compared against individual benchmark returns in order to calculate

a net return for each club. Different variations of regression analysis will be used to determine

the statistical significance of the relationship between individual competitive elements, which

will mainly surround the match results and the stock prices of publicly traded European soccer

clubs.

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 2 -

INTRODUCTION

Although it can be said that the stock prices of publicly traded soccer teams are driven by

economic factors, they should be evaluated in a different manner than a typical publicly traded

company (Prigge & Tegtmeir, 2018). The share prices of soccer teams could be influenced by

winning or losing an important match, defeating a bitter rival, or winning an international

tournament (Hagen & Cunha, 2019). Stock prices are very complex and are typically driven by

a multitude of underlying factors rather than a single financial metric such as revenue growth

(Prigge & Tegtmeir, 2018). This can be attributed to the stock prices of soccer teams as well,

as there are several internal and external competitive factors influencing their equity value.

Determination of the factors that have the most influence on the stock prices of soccer teams

could be helpful to investors looking to find a more precise valuation of these clubs.

Considering this, the present study seeks to analyze the stock prices of publicly traded European

soccer clubs from the perspective of various individual competitive factors to better understand

what goes into the total valuation of a soccer team. This paper will primarily focus on the match

results of publicly traded European soccer clubs, along with other competitive factors and how

they affect the stock prices of these teams. The results will aid all types of investors in

determining price movements and the true value of complex publicly traded entities such as

European soccer clubs.

LITERATURE REVIEW

Soccer is far and away the most popular sport in Europe and is one of the key economic drivers

throughout the continent (Drewes et al., 2020). It can be said that fans of soccer clubs in Europe

live through the support of their favorite teams. However, it is not often thought about how

these soccer clubs are run like businesses as opposed to sports franchises driven by the passion

of the fans. Many soccer teams throughout Europe are publicly traded on stock exchanges as if

they were a corporation (Bernile & Lyandres, 2011). Rhode and Baur (2016) have demonstrated

that there has been an influx of private investors moving into top clubs in Europe as well.

Additionally, existing literature has shown that privately owned clubs have been more

successful in European tournaments than publicly owned clubs (Gerretsen & Jakar, 2016). As

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 3 -

the share prices of soccer clubs are driven by other types of financial metrics, it is important to

understand the underlying factors that drive the individual share prices of these teams.

Overview of Publicly Traded Clubs

There are a growing number of soccer clubs throughout Europe that are making the move to

become publicly traded entities. For reference, the five major leagues are the English Premier

League, Serie A (Italy), Ligue 1 (France), La Liga (Spain), and the Bundesliga (Germany).

There are also publicly traded teams that are not in one of the five major leagues stated above.

Some examples of teams that are publicly traded in Europe include Manchester United, AS

Roma, Juventus, Borussia Dortmund, Ajax, and Lazio (Demir & Rigoni, 2017; Frick &

Semmelroth, 2021). Clubs looking to go public will undergo the process of an initial public

offering while following the financial guidelines of the Union of European Football

Associations.

Overview of European Soccer Indexes

There are several exchange traded funds publicly traded on European stock exchanges which

track the performance of publicly traded soccer clubs. Exchange traded funds (ETFs) are a

collection of stocks that trade as a single stock (Antoniewicz et al., 2014). Some examples of

these indexes include the Europe Football Index and the BIST Sport Index (Demir & Rigoni,

2017; Sevil et al., 2014). These exchanged traded funds can be used to track the stock price

performance of publicly traded European soccer clubs (Demir & Rigoni, 2017). If there is any

significant correlation between the stock price and match performance of publicly traded

European soccer clubs, then these indexes may be used to measure the collective match

performance of these clubs.

COVID-19 and the Macroeconomic Environment

Overarching external macroeconomic factors can also influence markets altogether, which can

lead to a general shift (positive or negative) in the stock prices of soccer teams (Baker et al.,

2020). An example of this would be when the COVID-19 pandemic first hit, there was a sharp

downward shift in all stock markets across the world (Drewes et al., 2020). The negative effects

of COVID-19 on so many different industries, especially soccer in Europe, led to negative price

returns on nearly all market sectors (Mazur et al., 2020). According to Drewes et al. (2020), the

major soccer leagues across Europe really began to feel the financial impact of the pandemic

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 4 -

one year after it first hit, as year over year revenues substantially decreased, and expenses

greatly increased. This general downturn in macroeconomic environments around the world

played an underlying role in the decrease of stock prices for publicly traded soccer teams

(Drewes et al., 2020).

Financial Regulations

UEFA has recently implemented financial regulations surrounding the different types of

ownership structures for soccer clubs in Europe. Research conducted by Gerretsen and Jakar

(2020) has indicated that the different types of ownership structures for soccer clubs has an

impact on how teams perform in tournaments. These researchers found that privately owned

teams are more likely to reach further rounds of international competition than publicly

owned/traded teams. Furthermore, the existing literature provides statistics from the 2005-06

Champions League and the 2017-18 Champions League, which illustrates the effect of

ownership structure on match performance (Gerretsen & Jakar, 2020). The study demonstrated

a positive relationship between private ownership and reaching later rounds of the tournament,

as well as a positive relationship between public ownership and appearing in earlier stages of

the tournament. Furthermore, existing literature has also found that positive effects in match

performance because of going public were seen in lower tiers of English soccer (Baur &

McKeating, 2011). While taking this research into consideration, this paper will extend existing

work by utilizing match performance as the independent variable and stock price as the

dependent variable.

Furthermore, UEFA implemented regulations known as “Financial Fair Play” back in 2010,

with the goal of prohibiting clubs from spending more money than they generate in revenues

(Freestone & Manoli, 2017). These rules essentially prevent clubs from spending more than

they have (Freestone & Manoli, 2017). UEFA’s Financial Fair Play regulations have influenced

the way in which soccer clubs are structured and how they are run. Both publicly traded clubs

and privately owned clubs have not been able to spend money as freely since these regulations

were put in place. Plumley et al. (2013) conducted a study in which they analyzed the

relationship between ownership structure and match performance on a domestic level by

specifically looking at data from teams in the English Premier League. The study collected

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 5 -

financial and match data of twenty Premier League teams from 2001-2010. They found that

publicly traded clubs are in a “stronger financial position” than clubs that are not publicly

traded, which can mainly be attributed to publicly owned clubs’ ability to raise capital as

opposed to privately owned clubs (Plumley et al., 2013). Financial health may not necessarily

have a direct correlation with match performance, however clubs that have more money are

able to buy better players which can lead to better performances on the field. Similarly, Cunha

and Hagen’s (2019) study demonstrated that after UEFA’s new financial regulations were put

in place, the stock prices of listed clubs were positively impacted.

UEFA’s “Financial Fair Play” regulations have impacted the way in which clubs are structured,

which in turn have influenced match results for teams across Europe (Plumley et al., 2013).

Brown (2007) examines the Glazer family takeover of Manchester United in England, which

led to some initial on the field success. When the Glazer family first took ownership of

Manchester United, they invested a large amount of money into the club by bringing in big

name players, which led to them winning Premier League titles in 2007, 2008, and 2010.

Similar to how Plumley et al. (2013) argue that “Financial Fair Play” regulations have limited

club spending and influenced match performance, Brown’s (2007) study demonstrates that

Manchester United saw much better match results before “Financial Fair Play” regulations were

implemented. To summarize, more financial investment in clubs leads to favorable match

results which can have a positive impact on a publicly traded club’s stock price (Plumley et al.,

2013).

Considering the existing literature, this paper will analyze the relationship between financial

wellbeing and match performance from the opposite angle, by utilizing regression analysis to

see if on the field results demonstrate any correlation to stock prices of publicly traded teams.

Considering the existing literature, the following hypothesis can be offered regarding match

performance and stock returns.

Ownership Structure

An additional variable that seems to have some effect on match performance and financial

returns is that of ownership structure (Baur & McKeating, 2011). As indicated by Baur and

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 6 -

McKeating (2011), being a publicly owned club does not have a major effect on match

performance in either domestic or international games. However, the literature illustrates a

positive effect of being a publicly owned club in relation to on the field performance in lower

divisions of European soccer (Baur & McKeating, 2011). These significant effects of match

performance could be due to the positive financial impact of being a publicly owned club. This

argument is supported by Plumley et al. (2013), whose study demonstrated that publicly traded

clubs are in a stronger financial position than clubs that are not of the public ownership

structure.

The current literature examining the competitive factor of ownership structure also stipulated

that there is a relationship between the stock returns for publicly listed soccer clubs and their

match performance (Baur & McKeating, 2011). These results were also found to be more

prevalent among teams in lower divisions than those in the top leagues across Europe. This is

in part due to the financial implications that come with competing in lower tiers of soccer

throughout Europe. In the lower divisions of European soccer, if a team gets promoted to a

higher division, then more money is earned for the club. On the other hand, if a club is relegated

or demoted to a lower level based upon poor match results, this will lose money for the club.

Existing literature further supports that ownership structure influences match performance,

specifically at the domestic level in England (Plumley et al., 2013). Considering the existing

literature, this paper will examine competitive variables such as club ownership structure to

determine the relationship more effectively between match performance and stock price returns

for publicly traded soccer clubs in Europe.

Investor Behavior & Relative Valuation

Another variable to consider in the relationship between match performance and stock returns

of publicly traded European soccer clubs is investor behavior. Cunha and Hagen’s (2019) study

indicates that investor sentiment, along with other factors, contributed to the European Football

(Stock) Index beating the return of the FTSE 100 by nearly 25% from the period of February

2013 to February 2019. For reference, the European Football Index is an exchange traded fund

which tracks the prices of publicly traded European soccer clubs, and the FTSE 100 is a

benchmark exchange traded fund for the top one hundred companies traded on the London

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 7 -

Stock Exchange. Additionally, Baur and McKeating (2011) argue that European soccer clubs

typically do not perform better directly after going public compared to before they were public.

The factors contributing to the European Football Index beating the return of the FTSE 100

seem to take full effect long after clubs initially go public.

When taking the existing literature into account, publicly traded soccer clubs are not valued in

the same manner as typical publicly traded entities on the market. Prigge and Tegtmeir (2019)

utilize the capital asset pricing model to argue that 13 of the 19 publicly traded soccer clubs

they sampled are overvalued relative to the majority of other securities on the market. The

capital asset pricing model is typically used to value a security’s return relative to its systematic

risk. The literature examined the individual betas of 19 publicly traded clubs and companies to

determine their price movements relative to the market (Prigge & Tegtmeir, 2019). The authors

found that the stock prices of soccer clubs do not typically follow the same patterns as the entire

market. Additionally, Plumley et al. (2013) argue that clubs who fall under the public ownership

structure are in better financial standing than those that are not publicly owned. The stock prices

of these publicly listed teams could be driven by their financial health, leading to them

becoming overvalued relative to the market.

Measuring the Impact of Specific Wins

Another variable that has demonstrated the ability to influence the stock return of publicly

traded European soccer clubs are specific types of wins (Demir & Rigoni, 2017). Different

variations of wins including domestic league wins, international cup wins, and defeating a rival

club all can have an influence on the stock prices of publicly traded clubs (Berument & Ceylan,

2012; Demir & Rigoni, 2017; Frick & Semmelroth, 2021).

Domestic versus International Wins

Publicly traded soccer clubs play many matches both within their respective domestic leagues,

along with several international cup matches. When examining Borussia Dortmund of

Germany, Frick and Semmelroth (2021) found that the team’s stock price responded

significantly positive when the team won a match and insignificantly negative when the team

lost a match. Additionally, the price returns were relatively similar no matter if it was a domestic

league match or an international cup game. Existing literature supports these findings as

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 8 -

Berument and Ceylan (2012) demonstrate that the results of matches, whether it is a win or loss,

influence the stock prices of publicly traded European clubs. The authors state that the most

significant evidence of these claims comes from the English Premier League and La Liga

(Spain). However, Berument and Ceylan’s (2012) findings contradict those of Frick and

Semmelroth’s (2021), as the former found that losses (did not specify domestic or international)

had a greater negative impact on stock price than wins had positive impact. While these studies

had different results, they both demonstrated domestic and international match results have an

influence on stock prices in some way.

Rival Wins

European soccer rivalries are often derived from teams who are located in close proximity to

each other or just have a long history of constantly facing off against one another (Demir &

Rigoni, 2017). Rivalry games seem to mean more to players, owners, and the fans. Existing

literature has examined a specific rivalry in Italy between Lazio and AS Roma and concluded

that losing a match, along with poor match performance from a rival team has a greater negative

effect on a team’s stock price compared to when their rival wins a match (Demir & Rigoni,

2017). Even if the two rivals are not directly playing one another, the results of a rival’s game

can have an impact on a team’s stock price. Additionally, Berument and Ceylan (2012)

conducted a study and found that in the English Premier League and La Liga market returns for

publicly traded clubs tend to decrease especially after a loss to a rival team. This demonstrates

how rival match results have a greater influence on the price movement of publicly traded

European soccer clubs.

Individual Player Transactions

Another competitive factor impacting both match results and stock returns for publicly traded

European soccer clubs is player signings (Mourao, 2016). The signing of a star player on the

transfer market can impact a team’s performance on the pitch which could lead to influencing

their stock price. Big name players can greatly influence the equity value of a publicly traded

team one way or another (Hagen & Cunha, 2019; Putranto, 2019). Some well-known players

who have recently moved to new teams include Cristiano Ronaldo and Lionel Messi. When

Cristiano Ronaldo moved to Juventus in 2018, he led them to consecutive Italian League titles

in 2019 and 2020 (Limba et al., 2020). These league titles Ronaldo won also had a positive

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 9 -

financial impact on the club as a whole, demonstrating just how much of an effect the signing

of an individual player can have on a club.

Individual Player Contribution: Match Results

One player can have significant influence on the match performance of soccer clubs (Tenga et

al., 2009). There is existing evidence that a single player can have a significant positive impact

on the match results and financial performance of soccer clubs which can lead to positive stock

price returns (Saebo & Hvattum, 2019). Saebo & Hvattum (2019) demonstrate that the

performance of one player can push a team over the edge of a competitor. Specifically, the

authors analyzed the performance of Frank Lampard for Manchester City in 2015. Results

showed that the addition of Frank Lampard to Manchester City allowed the team to finish one

place ahead of rival team, Arsenal, whereas without Lampard they would have finished tied

with Arsenal (Saebo & Hvattum, 2019). Along with having a positive influence on match

performance, existing literature has also found player transfers to positively influence the stock

returns of publicly traded European clubs (Hagen & Cunha, 2019). In summary, individual

players can ultimately impact the stock prices of publicly traded soccer clubs (Hagen & Cunha,

2019; Putranto, 2019).

Individual Player Contribution: Financial Performance

A single player can also have a substantial impact on the financial metrics of both publicly and

privately owned soccer clubs. Existing literature suggests that an individual player can directly

influence the revenues of a club (Saebo & Hvattum, 2019). Saebo and Hvattum (2019) utilize

the example of Cesc Fabregas and Chelsea FC to illustrate that without the signing of Fabregas,

Chelsea would have lost 2.1 million pounds in total revenue over the course of the 2014-2015

Premier League season. Additionally, this loss in revenue could have led to Chelsea finishing

in a lower position in the Premier League table for the 2014-2015 season, as Chelsea only

finished slightly above rivals Manchester City to win the Premier League title. Cunha and

Haegan (2019) also argue that bringing in new players has had a positive impact on the stock

prices of publicly traded European soccer clubs.

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 10 -

Cristiano Ronaldo

There is existing evidence that Cristiano Ronaldo’s transfer from Real Madrid (Spain) to

Juventus (Italy) impacted the stock prices of publicly listed Italian League teams directly before

and after the official completion of the signing (Putranto, 2019). Putranto (2019) found that

there were differences in the stock prices of Italian League teams one to ten days before the

official transfer and one to ten days after completion of the transfer, as stock prices gradually

increased each of the ten days after Ronaldo’s move to Juventus. Existing literature has also

found that both the signing of new players and the transfer of current players has also positively

influenced the stock prices of publicly traded European clubs (Hagen & Cunha, 2019). This

illustrates the plethora of both financial and competitive factors that can cause substantial price

changes in the shares of publicly traded clubs.

The performance of soccer clubs can often influence international markets and national

economies. If it is found that positive match results for soccer clubs leads to positive effects on

stock markets and economies, then there could potentially be correlation between the economic

and fiscal well-being of citizens and on the field results of soccer clubs. The results of this study

could also be beneficial to owners and managers of soccer clubs, as match results are often a

direct result of decisions they make. If a significant relationship is found between on the field

performance and stock returns, then the decisions made by those in upper-level positions at the

club will have a direct impact on the financial health and wellbeing of the publicly traded club.

Analyzing the literature connects back to the overarching question this paper will answer: What

effect does match performance and other competitive factors have on the equity value of

publicly traded European soccer teams? Evaluating competitive variables such as the

macroeconomic environment, match results, rivalry games, and player transactions will aid

investors in understanding the price movements and true value of publicly traded European

soccer teams.

HYPOTHESES

The following hypotheses this study will seek to test are:

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 11 -

1. Match wins will have a strong positive correlation to the stock returns of publicly

traded European soccer clubs.

2. Rival match results will have a greater impact on the stock price of publicly traded

European soccer clubs than ordinary match results.

3. European tournament match results will have a greater impact on the stock price of

publicly traded European soccer clubs than domestic league matches.

METHODOLOGY

Regression

The primary goals of this study are to analyze the impact of match performance on the stock

prices of publicly traded European soccer clubs, determine if publicly or privately owned teams

perform better in matches, and find out whether competitive factors such as rivalry games and

player transfers influence the equity value of these teams. This analysis will utilize different

forms of regression by using statistical software within Excel, similar to methods employed by

Demir and Rigoni’s (2017) study, in order to determine the effect of certain events on the stock

prices of soccer teams. The specific events that will be analyzed are the match results of teams

within the sample, outcomes of games against rivals, and European matches versus domestic

matches.

Linear Regression

Simple linear regression is used to determine the strength of a relationship between independent

and dependent variables. This study will utilize linear regression to demonstrate the overall

correlation between the stock prices of publicly traded European soccer teams and their match

performance (wins, draws, and losses). In order to make wins, draws, and losses continuous

variables, a win will be quantified as “3”, a draw will be quantified as “1”, and a loss will be

quantified as “0”. The results from the linear regression should highlight the strength of this

relationship through statistical interpretation of the r-squared and p-values.

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 12 -

Multi-Factor Regression

A multiple linear regression analysis will be utilized to look at the overall impact of rivalry

match outcome on the stock prices of publicly traded teams. Multiple linear regression is

typically used to predict how much of an effect more than one independent variable will have

on a singular dependent variable. This study will employ a multiple linear regression analysis

similar to one used by Frick and Semmelroth’s (2021) analysis to predict how much of an

impact “rival match”, match outcomes (win, loss, or draw), and European/Domestic matches

have on the share price of publicly traded soccer teams in Europe. In this case the three

independent variables will be “rival matches”, match results, and European/Domestic matches.

The “rival match” variable will be a binary variable where if a rival match is played the variable

will be set equal to “1”, and if a rival match is not played the variable will be set equal to “0”.

The European/Domestic variable will also be a binary variable where a European match will be

set equal to “1”, and a Domestic match will be set equal to “0”. The dependent variable will be

price return. The results should demonstrate the impact rivalry match outcomes have on stock

price.

Daily Return and Net Return

The daily stock price return for publicly traded teams will be calculated by obtaining the daily

historical opening and closing prices from Yahoo Finance. Daily return will be calculated by

subtracting the adjusted close price of the previous trading day from the adjusted close price of

the current trading day, and then dividing that number by the adjusted close price of the previous

trading day. A daily net return will also be calculated and used in the regression models. Net

returns will utilize daily benchmark returns using benchmarks from the team’s respective

country. Net return will be calculated as the daily return minus the benchmark return.

Sample: Publicly Traded Teams

This study will analyze soccer clubs throughout Europe that are publicly owned and traded on

the open market. The sample will include the stock price data and match statistics over the

twelve-year period (2010-21) from thirteen teams in Europe, spanning across eight countries.

Stock price data will be collected from the reliable financial database, Yahoo Finance. The

teams, along with the country they play in, and market they trade on are listed below:

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 13 -

• Ajax (AJAX.AS), Netherlands, Amsterdam Stock Exchange

• AS Roma (ASRAF), Italy, Milan Stock Exchange

• Borussia Dortmund (BVB.DE), Germany, Frankfurt Stock Exchange

• Benfica (SLBEN.LS), Portugal, Euronext Lisbon

• Celtic (CCP.L), Scotland, London Stock Exchange

• FC Copenhagen (PARKEN.CO), Denmark, Nasdaq Copenhagen

• FC Porto (FCP.LS), Portugal, Euronext Lisbon

• Galatasaray (GSRAY.IS), Turkey, Borsa Istanbul

• Juventus (JUV.MI), Italy, Milan Stock Exchange

• Lazio (SSL.MI), Italy, Milan Stock Exchange

• Manchester United (MANU), England, New York Stock Exchange

• Olympique Lyon (OLG.PA), France, Euronext Paris

• Sporting Club Portugal (SCP.LS), Portugal, Euronext Lisbon

Sample: Benchmark Indices

Benchmark indices are representative of the current state of the market in respective countries,

whereas the individual stocks of these publicly traded are mainly just representative of the

current state of each team. It is important to compare the daily returns of the individual teams

to the daily returns of the benchmark because on some days the individual teams could

outperform the benchmark, and other days they could underperform. The benchmarks, along

with their respective countries are listed below:

• DAX Performance Index, Germany

• BIST 100, Turkey

• FTSE MIB, Italy

• S&P 500, United States

• CAC 40 Index, France

• AEX All Share Index, Netherlands

• PSI 20, Portugal

• FTSE 100, United Kingdom

• OMX Copenhagen, Denmark

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 14 -

RESULTS & FINDINGS

When looking at the findings of this study, it is imperative to analyze the results on an individual

team by team basis. The price movements of each team are unique, and no one team’s regression

model is exactly the same. Some teams support all three of the previously stated hypotheses,

whereas others only support two, and some support only one. Most teams in the sample have a

positive correlation between their daily net return and match wins. However, the rival

hypothesis and European match hypothesis are not supported by every team. The results of this

study will therefore be presented on an individual team-by-team basis. The best predictive

regression model will be shown for each team based on their respective r-squared and p-values,

along with other important summary statistics.

Manchester United (MANU)

Manchester United play in the English Premier League and are publicly traded in the United

States on the New York Stock Exchange. Their current total market capitalization is about $3.6

billion. I measured the volatility of Manchester United’s returns by looking at the standard

deviation of their returns. Standard deviation is a measure of how widely returns are dispersed

from the average return. In terms of overall volatility, Manchester United’s standard deviation

of daily returns after a match was played was 1.68%. The benchmark’s standard deviation of

daily returns, which in this case was the S&P 500 (SPY), was 1.06%. The difference in standard

deviation for Manchester United and the benchmark was found to be 0.62%.

Figure 1: Line chart that shows the price movement of MANU from 2012 – 2021.

MANU Adj Close Price After Match Played

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 15 -

I started by first testing whether Manchester United had a positive correlation between match

wins and daily net return. Results clearly supported this hypothesis. Their average daily net

return after a win was 0.27%, and after a loss it was -0.13%. So, the difference in average return

between a win and a loss was found to be 0.40%.

For my rivalry hypothesis I initially had to determine rivals for each of the publicly traded teams

in my sample. I deemed Manchester United’s rivals to be Manchester City, Arsenal, Chelsea,

and Liverpool. Whenever Manchester United played a match against one of these teams, the

rivalry variable was set equal to 1. When conducting an analysis on Manchester United’s rivalry

matches, the results clearly did not support my hypothesis. Their average daily net return after

a win was found to be -0.14%, and their average daily net return after a loss was calculated to

be 0.33%. So, a negative correlation was found between Manchester United’s returns and

rivalry matches.

The last hypothesis I tested for each team was my European match hypothesis. For this variable

I set any European match Manchester United played in equal to 1, and all of their Premier

League matches equal to 0. Results did not support my hypothesis as Manchester United’s

average daily net return after a European match win was 0.17%, and their average daily net

return after a domestic match win was 0.29%.

Manchester United Predictive Model

After running multiple types of regression models, I found the strongest predictive model for

Manchester United to include the match result variable and rivalry variable. Their predictive

model came out to be Average Daily Net Return = -0.03% + .11%(Result) – 0.36%(Rival). This

model has an r-squared value of 0.017, and this low percentage of variance captured by the

model can be attributed to the many other external factors that influence stock price. The p-

values for both the match result variable and rivalry variable were both below 0.05, meaning

they are both statistically significant.

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 16 -

Juventus (JUV.MI)

Juventus play in the Italian Serie A, and they are publicly traded in Italy on the Milan Stock

Exchange. Their current total market capitalization is about $853 million. In terms of overall

volatility, Juventus’ standard deviation of daily returns after a match was played was 3.02%.

The benchmark’s standard deviation of daily returns after matches, which in this case was the

FTSE MIB, was 1.39%. The difference in standard deviation for Juventus and the benchmark

was found to be 1.63%.

Figure 2: Line chart that shows the price movement of JUV.MI from 2012 – 2021.

The results clearly indicate Juventus’ net return has a strong positive correlation with match

results. Juventus’ average daily net return after a win was calculated to be 0.63%, whereas it

was -1.13% after a loss. This shows a 1.77% difference in the average daily net return between

when Juventus wins a match versus when they lose a match. These calculations clearly support

my first hypothesis.

I determined Juventus’ main rivals to be AC Milan, Inter Milan, Napoli, and AS Roma. This

allowed me to set the rival variable equal to 1 whenever Juventus played a match against one

of these opponents. The results demonstrated a positive relationship between the results of

rivalry matches and the net return of Juventus’ stock. After Juventus won a match against a

rival, they had an average daily net return of 0.79%, compared to an average daily net return of

only 0.61% after a regular win. So, Juventus had stronger returns after defeating a rival

compared to defeating a non-rival.

0.5

1.5

JUV.MI Adj Close Price After Match Played

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 17 -

Juventus has had a strong history of qualifying for European tournaments, which allowed me

to test whether their returns were stronger after winning a European match versus winning a

domestic match. Results clearly did not support my third European match hypothesis as

Juventus’ average daily net return after winning a European match was calculated to be just

0.54%, compared to 0.65% after winning a match in Serie A.

Juventus Predictive Model

After running multiple types of regression models, I found the strongest average daily net return

predictive model to include only the match result variable. The Juventus predictive model came

out to be Average Daily Net Return = -0.78% + 0.48%(Result). The R-squared for this model

came out to be about 0.03, meaning that only 3% of the variance in Juventus’ average daily net

return is accounted for by their match results. The p-value for the match result variable was

0.0383, meaning there is a statistically significant relationship between Juventus’ match results

and their average daily net return.

Borussia Dortmund (BVB.DE)

Borussia Dortmund plays in the German Bundesliga, and they are publicly traded in Germany

on the Frankfurt Stock Exchange. Their current total market capitalization is about $451.4

million. In terms of overall volatility, Borussia Dortmund’s standard deviation of daily returns

after a match was played was 2.24%. The benchmark’s standard deviation of daily returns after

matches, which in this case was the DAX Performance Index, was 1.27%. The difference in

standard deviation for Borussia Dortmund and the benchmark was found to be 1.63%.

BVB.DE Adj Close Price After Match Played

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 18 -

Figure 3: Line chart that shows the price movement of BVB.DE from 2012 – 2021.

The results demonstrate that there is a strong positive correlation between Borussia Dortmund’s

average daily net returns and their match results. Borussia Dortmund’s average daily net return

after a match win was calculated to be 1.03%, whereas after a match loss their average daily

net return was -1.77%. This resulted in a difference of 2.80% in average daily net return between

when Borussia Dortmund wins a match versus when they lose a match. This large range in

differences clearly supports my match result hypothesis.

I determined Borussia Dortmund to have four main rivals in the German Bundesliga. Their

main rivals are Bayern Munich, FC Schalke, Borussia Monchengladbach, and Wolfsburg. The

rival variable was set equal to 1 whenever Borussia Dortmund played a match against one of

these teams. The results did not support my second rivalry hypothesis, as Borussia Dortmund’s

average daily net return after defeating a rival was less than when they won a non-rival match.

Their average daily net return after winning a rival match was calculated to be just 0.78%,

whereas it was 1.09% after Borussia Dortmund beat a non-rival. So, there was a difference of

0.31% in Borussia Dortmund’s average daily net return after defeating a rival versus a non-

rival.

When testing my European match hypothesis for Borussia Dortmund, the calculated results

strongly supported it. Borussia Dortmund had a calculated average daily net return of 1.14%

after a win in a European match compared to just a 1.01% return after winning a German

Bundesliga match. Although the difference was just 0.13%, this is still a significant difference

when it comes to the daily returns of an individual stock.

Borussia Dortmund Predictive Model

After running multiple types of regression models for Borussia Dortmund I found the strongest

model for their predicted average daily net return to include the match result and rival variables.

The Borussia Dortmund predictive model came out to be Average Daily Net Return = -1.55%

+ 0.86%(Match Result) + 0.26%(Rival). The R-squared for this model was calculated to be

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 19 -

0.246, meaning that 24.6% of the variance in the average daily net return of Borussia

Dortmund’s stock is captured by the match result and rival variables. The respective p-values

for the match result and rival variables are 2.08 x 10

-29

and 0.25. This shows both variables are

statistically significant in Borussia Dortmund’s predictive regression model.

Olympique Lyon (OLG.PA)

Olympique Lyon plays in the French Ligue 1, and they are publicly traded in France on the

Euronext Paris Exchange. Their current total market capitalization is about $251 million. In

terms of overall volatility, Olympique Lyon’s standard deviation of daily returns after a match

was played was 2.54%. The benchmark’s standard deviation of daily returns after matches,

which in this case was the CAC 40 Index, was 1.16%. The difference in standard deviation for

Olympique Lyon’s stock and the benchmark was found to be 1.38%.

Figure 4: Line chart that shows the price movement of OLG.PA from 2012 – 2021.

The results demonstrate a strong positive correlation between Olympique Lyon’s match results

and their daily net returns. Olympique Lyon’s average daily net return after winning a match

was calculated to be 1.03%, whereas after suffering a defeat their average daily net return was

only -1.29%. This resulted in a difference of 2.32% in daily net return between when Olympique

Lyon wins a match versus when they lose a match. This range clearly supports my first match

result hypothesis.

0.5

1.5

2.5

3.5

4.5

OLG.PA Adj Close Price After Match Played

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 20 -

I determined Olympique Lyon to have four main rivals in the French Ligue 1. These four rivals

are Marseille, Bordeaux, Paris Saint-Germain, and Lille. The rival variable was set equal to 1

whenever Olympique Lyon played a match against one of these teams. The results clearly did

not support my rivalry hypothesis, as Olympique Lyon’s average daily net return after defeating

a rival was calculated to be only 0.54%, whereas when they won a non-rival match their average

daily net return was 1.08%. Olympique Lyon’s average net return after defeating a non-rival

was double their return after beating a rival.

When testing my European match hypothesis for Olympique Lyon, the results slightly

supported my hypothesis. Olympique Lyon’s average daily net return after a European match

win was found to be 1.04%, and their return for domestic match wins was 1.03%. While this

0.01% difference is not extremely significant, it still supports my European match hypothesis.

Olympique Lyon Predictive Model

After running multiple types of regression models for Olympique Lyon I found the strongest

model for their predicted average daily net return to include only the match result variable. The

Olympique Lyon predictive model came out to be Average Daily Net Return = -1.14% +

0.74%(Match Result). The R-Squared value for this model was calculated to be 0.1458, which

means 14.58% of the variance in the average daily net return was captured by the match result

variable. The p-value for the match result variable was 1.18 x 10

-17

, illustrating a statistically

significant relationship between the independent match result variable and the dependent

average net return variable.

Galatasaray SK (GSRAY.IS)

Galatasaray SK plays in the Turkish Super League, and their shares are publicly traded on the

Borsa Istanbul Exchange in Turkey. Their current total market capitalization is roughly $4.9

billion. Based on many of the underlying market conditions in Turkey, Galatasaray SK’s overall

volatility was much higher compared to some of the other publicly traded clubs. Galatasaray

SK’s standard deviation of returns after they played a match was 3.62%. The standard deviation

of the Turkish stock market benchmark, the BIST 100, over the same period was just 1.47%.

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 21 -

This resulted in a difference in standard deviations of about 2.15%, between their individual

stock and the benchmark.

Figure 5: Line chart that shows the price movement of GSRAY.IS from 2012 – 2021.

The results of Galatasaray SK’s net returns indicate a strong positive correlation between their

daily net return and match results. After Galatasaray SK won a match, their average daily net

return was calculated to be 1.03%. However, after a match loss, their average daily net return

was -1.73%. This 2.76% difference in net return after a win versus a loss clearly supports my

first match result hypothesis as there was clearly a positive relationship between Galatasaray

SK’s daily net returns and their match results.

The Turkish Super League is home to some of the most intense rivalries in the world. I

determined Galatasaray’s main rivals to be Istanbul BBSK, Trabzonspor, Fenerbahce, and

Besiktas. Anytime Galatasaray SK played a match against one of these teams, I set the rivalry

variable equal to 1. The results strongly supported my second rivalry hypothesis. When

Galatasaray SK won a match against a rival club, their average daily net return was 2.17%,

compared to only 0.88% after they won a match against a non-rival. Additionally, their average

daily net return after losing a rivalry match was -1.89%, compared to -1.69% after suffering

defeat in a non-rival match. Galatasaray SK’s returns were more positive after beating a rival,

and more negative after losing to a rival.

1/3/2012 1/3/2013 1/3/2014 1/3/2015 1/3/2016 1/3/2017 1/3/2018 1/3/2019 1/3/2020 1/3/2021

GSRAY.IS Adj Close Price After Match Played

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 22 -

When testing my third European match hypothesis for Galatasaray SK, the results did not

support my hypothesis. After winning a European tournament match their average daily net

return was just 0.68%, compared to 1.05% after winning a Turkish Super League match. This

difference could be attributed to fans being more passionate about beating teams within your

own country than winning games continentally.

Galatasaray SK Predictive Model

After running various combinations of regression models for Galatasaray SK, I found the

strongest model for them to include the Match Result variable and the European match type

variable. The Galatasaray SK predictive model came out to be Average Daily Net Return =

-1.96% + 0.97%(Result) – 0.70%(Match Type). The overall R-squared value for this model was

calculated to be 0.109, so this means that 10.9% of the variance of the average daily net return

is captured by the independent Match Result and Match Type variables. The respective p-values

were also both below 0.30, indicating a statistically significant relationship between the

independent and dependent variables.

AS Roma (ASRAF)

AS Roma plays in the Italian Serie A, and they are publicly traded in Italy on the Milan Stock

Exchange. Their current total market capitalization is about $290 million. In terms of overall

volatility, AS Roma’s standard deviation of daily returns after a match was played was 4.82%,

which is one of the most volatile in the sample. The benchmark’s standard deviation of daily

returns after matches, which in this case was the FTSE MIB, was 1.39%. The difference in

standard deviation for AS Roma and the benchmark was found to be 3.43%.

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 23 -

Figure 6: Line chart that shows the price movement of ASRAF from 2012 – 2021.

The results of AS Roma’s net results indicate a slightly negative correlation between their match

results and average daily net returns. Based on calculations, after AS Roma won a match their

average daily net return after a win was -0.08%, compared to -0.32% after a loss. While AS

Roma’s returns were better after a match win, they still had a negative average net return after

this positive result. While these results do not completely support my first match result

hypothesis, the returns for AS Roma were still slightly better after a match win compared to a

loss.

I determined AS Roma’s main rivals in the Italian Serie A to be AC Milan, Lazio, Juventus,

and Atalanta. When AS Roma played a match against one of these teams, the rivalry variable

was set equal to 1. My second rivalry hypothesis was not supported by the calculated results.

After defeating a rival, AS Roma’s average daily net return was -1.20%, whereas after winning

a non-rival match their average daily net return was -0.03%. A negative relationship was

demonstrated between rivalry matches and AS Roma’s net return.

When analyzing AS Roma’s European matches versus their domestic league matches, their

average daily net return after a European win was -0.99%. When AS Roma won a domestic

Serie A match, their return was -0.24%. These results did not support my European match

0.1

0.2

0.3

0.4

0.5

0.6

0.7

ASRAF Adj Close After Match Played

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 24 -

hypothesis, as AS Roma had a better average daily net return after winning a domestic match

compared to a European match.

AS Roma Predictive Model

After running several different regression models for AS Roma, their strongest predictive model

for their average daily net return included just the rivalry variable. The AS Roma predictive

model came out to be Average Daily Net Return = -0.20% + 1.57%(Rival). The R-squared

value for this model was calculated to be 0.008, meaning 0.80% of the variance in the average

daily net return of AS Roma’s stock was captured by the rival variable. The p-value for the

rivalry variable in this model 0.215, indicating a statistically significant relationship between

the rivalry variable and the dependent average daily net return variable.

SL Benfica (SLBEN.LS)

SL Benfica plays in the Portuguese Primeira Liga and is publicly traded in Portugal on the

Euronext Lisbon Exchange. Their current total market capitalization is about $87 million. SL

Benfica’s overall standard deviation of net returns is 4.45%, compared to 1.28% for the PI 20

which is the benchmark for the Euronext Lisbon exchange. There is an overall 3.17% difference

in the standard deviation of the returns for SL Benfica and the PI 20 benchmark.

Figure 7: Line chart that shows the price movement of SLBEN.LS from 2013 – 2021.

SLBEN.LS Adj Close After Match Played

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 25 -

The results of SL Benfica’s net returns indicate a positive relationship between their average

daily net return and match results. When SL Benfica won a match their average daily net return

was 0.59%, compared to 0.43% after they suffered a defeat. While there was only a 0.13%

difference in SL Benfica’s daily average net returns after a win versus a loss, it still supports

my first match result hypothesis.

In the Portuguese Primeira League SL Benfica’s main rivals are Sporting Club Portugal, FC

Porto, and Sporting Club de Braga. When SL Benfica played a match against one of these

teams, the rivalry variable was set equal to 1. The calculated results did not support my second

rivalry hypothesis. SL Benfica’s average daily net return after a win against one of their rivals

was 0.16%, compared to a net return of 0.63% after a win against a non-rival. SL Benfica’s

average daily net return was 0.47% higher after a win against a non-rival than a win against a

rival.

When testing my European match hypothesis for SL Benfica, the results strongly supported my

hypothesis. SL Benfica’s average daily net return after winning a European tournament match

was 0.68%, compared to just 0.56% after winning a domestic Portuguese Primeira League

match. European matches seemed to have a more positive influence on SL Benfica’s stock than

domestic league matches.

SL Benfica Predictive Model

After running a number of regression models with different combinations of variables I found

the strongest predictive model to include only the rivalry variable. The SL Benfica predictive

model came out to be Average Daily Net Return = 0.66% - 0.44%(Rival). The R-squared value

for this model was calculated to be 0.0011, which means that 0.11% of the variance in the

dependent average daily net return was captured by the independent rival variable. The

respective p-value for the rival variable was 0.50, which indicates a statistically insignificant

relationship between the independent and dependent variables.

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 26 -

Celtic FC (CCP.L)

Celtic FC plays their matches in the Scottish Premiership and are publicly traded in the United

Kingdom on the London Stock Exchange. Their current total market capitalization is $106.8

million. Celtic FC’s net returns were among the least volatile in the sample, as their standard

deviation of returns was calculated to be 1.50%. The FTSE 100’s standard deviation of returns

during this time period was 1.29%. This resulted in only a 0.21% difference in the standard

deviation of returns for Celtic FC and the benchmark FTSE 100.

Figure 8: Line chart that shows the price movement of CCP.L from 2012 – 2021.

The results of Celtic FC’s net returns do not indicate a positive relationship between match

results and average daily net return. When Celtic FC won a match, their calculated average

daily net return was -1.03%, compared to -1.07% after they suffered a defeat. Celtic FC had a

strong negative relationship between match results and average daily net return. During the

observed time period Celtic FC’s overall average daily net return was -1.02%, so returns after

a match was played were actually lower than their overall average return.

One of the most intense rivalries in all of professional soccer is between Celtic FC and Rangers.

In addition to Rangers, I determined Celtic FC’s other rivals to be Hearts, Aberdeen, and Saint

Mirren. When Celtic FC played a match against one of these teams the rival variable was set

equal to 1. The calculated results did not support my second rivalry hypothesis as Celtic FC’s

average daily net return after defeating a rival was calculated to be -0.05%, whereas it was

100

150

200

CCP.L Adj Close After Match Played

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 27 -

0.05% after beating a non-rival. While it was only a 0.10% difference, Celtic FC still had better

average daily net returns after beating a non-rival compared to a rival.

When testing my European match hypothesis for Celtic FC, the results did not strongly support

my hypothesis. Their returns were nearly the same after winning a domestic match versus a

European match. After winning a European match, Celtic FC’s average daily net return was

calculated to be 0.02% and 0.04% after winning a domestic Scottish Premiership match. This

could be attributed to the fierce rivalries between club teams in Scotland.

Celtic FC Predictive Model

When running different regression models for Celtic FC, I found the strongest predictive model

to include the match result and match type variables. The Celtic FC predictive model came out

to be Average Daily Net Return = -1.98% + 0.17%(Match Result) + 1.72%(Match Type). The

R-squared value for this model was calculated to be 0.28, meaning that 28% of the variance in

Celtic FC’s average daily net returns is accounted for by the independent match result and match

type variables. The p-value for the match result variable was 0.001, and the p-value for the

match type variable was 9.19 x 10

-28

, indicating a statistically significant relationship between

the independent and dependent variables.

FC Porto (FCP.LS)

FC Porto plays in the Portuguese Primeira Liga and is publicly traded in Portugal on the

Euronext Lisbon Exchange. Their current total market capitalization is about $23.6 million. FC

Porto’s overall standard deviation of net returns over the observed period was 5.09%, compared

to 1.01% for the PI 20 which is the benchmark for the Euronext Lisbon exchange. There is an

overall 4.08% difference in the standard deviation of the returns for SL Benfica and the PI 20

benchmark, which was one of the largest differences in standard deviations among teams in the

sample.

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 28 -

Figure 9: Line chart that shows the price movement of FCP.LS from 2012 – 2021.

The results of FC Porto’s returns indicated a strong positive relationship between their daily net

returns and match results. FC Porto had an average daily net return of 0.41% after a match win

compared to a -0.88% return after a loss. This resulted in a 1.29% difference in FC Porto’s

average daily net return after they win a match versus when they lose. During the observed time

period, FC Porto’s overall average daily net return was 0.17%, so winning a match had a strong

positive influence on their stock price.

FC Porto’s rivals were determined to be Sporting Club Portugal, Club de Braga, and SL

Benfica. Anytime FC Porto played a match against one of these clubs, the rival variable was set

equal to 1. The results strongly supported my second rivalry hypothesis. FC Porto’s average

daily net return after a rivalry win was calculated to be 1.79%, compared to just 0.25% after a

non-rivalry win. This resulted in a difference of 1.54% in average daily net return between when

FC Porto defeats a rival compared to a non-rival.

When testing my third European match hypothesis for FC Porto, the results did not support my

hypothesis. FC Porto’s average daily net return after winning a European match was 0.23%,

compared to 0.45% after winning a domestic match. This can be attributed to the intense

rivalries within the Portuguese Primeira League.

0.2

0.4

0.6

0.8

1.2

1.4

1.6

1.8

FCP.LS Adj Close After Match Played

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 29 -

FC Porto Predictive Model

When running different regression models for FC Porto, the strongest predictive model included

the match result and rival variables. FC Porto’s predictive model came out to be Average Daily

Net Return = -0.83% + 0.38%(Match Result) + 1.26%(Rival). The R-squared for this model

was calculated to be 0.014, which means that 1.4% of the variance in FC Porto’s average daily

net return is captured by the match result and rival variables. The respective p-values for the

match result and rival variables were 0.08 and 0.10, indicating a statistically significant

relationship between the dependent and independent variables.

Lazio (SSL.MI)

Lazio plays in the Italian Serie A, and they are publicly traded in Italy on the Milan Stock

Exchange. Their current market capitalization is about $72.5 million. In terms of overall

volatility, Lazio’s standard deviation of daily returns after a match was played was 2.38%. The

benchmark’s standard deviation of daily returns after matches for the observed time, which was

1.39%. The difference in standard deviation for Lazio and the benchmark was found to be

0.99%.

Figure 10: Line chart that shows the price movement of SSL.MI from 2012 – 2021.

The results of Lazio’s returns indicate a strong positive relationship between their average daily

net return and match results. Lazio had an average daily net return of 1.10% after a match win

compared to just a 0.54% net return after they suffered a defeat. Lazio’s average daily net return

after a win was over double what it was after a match loss. During the observed period, Lazio

0.5

1.5

2.5

SSL.MI Adj Close After Match Played

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 30 -

had an average daily net return of 0.87% after all matches. This demonstrates that match wins

had a positive impact on Lazio’s average daily net return.

Lazio’s rivals in Serie A were determined to be Atalanta, Fiorentina, Napoli, and Juventus.

Anytime Lazio played a match against one of these teams the rivalry variable was set equal to

1. The results did not support my second rivalry hypothesis, as Lazio’s average daily net return

after defeating a rival was calculated to be 0.96%, compared to 1.11% after defeating a non-

rival.

Lazio has had a long history of qualifying for European tournaments. When testing my third

European match hypothesis, the results did not support my hypothesis. Lazio’s average daily

net return after winning a European tournament match was 1.06%, compared to 1.12% after a

domestic win. This can be attributed to fans being more passionate about matches in Serie A

than continental matches against clubs from other countries.

Lazio Predictive Model

After running different types of regression models for Lazio, the strongest predictive model for

their average daily net return included only the match result variable. Lazio’s predictive model

came out to be Average Daily Net Return = 0.60% + 0.17%(Match Result). The R-squared for

this model was calculated to be 0.01, meaning that 1% of the variance in Lazio’s average daily

net return is captured by the independent match result variable. The p-value for the match result

was 0.18, indicating a statistically significant relationship between the independent and

dependent variables.

Ajax (AJAX.AS)

Ajax are a professional club based in Amsterdam and they play their matches in the Eredivisie,

which is the top professional soccer league in the Netherlands. They are publicly traded on the

Amsterdam Stock Exchange. Their current total market capitalization is about $201 million.

When looking at their volatility metrics, Ajax’s overall standard deviation of net returns over

the observed period was calculated to be 2.19%. The Amsterdam Stock Exchange benchmark’s

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 31 -

standard deviation of daily returns over the same period was only 1.02%. This shows Ajax’s

individual stock was about 1.17% riskier than the benchmark over the same period.

Figure 11: Line chart that shows the price movement of AJAX.AS from 2012 – 2021.

The results of Ajax’s returns indicate a positive relationship between average daily net return

and match wins. After Ajax won a match, their calculated average daily net return was 0.20%,

compared to just 0.12% after they suffered a defeat. Over the entire observed time period, Ajax

had an average daily net return of 0.22% after a match was played. While there was still a

positive relationship daily net return and match wins, Ajax’s positive match results did not have

a great influence on their daily net returns.

In the Eredivisie, it was determined that Ajax had four main rivals. Their rival clubs are

Feyenoord, PSV Eindhoven, Vitesse, and AZ Alkmaar. Whenever Ajax played a match against

one of these clubs the rivalry variable was set equal to 1. The results slightly supported my

second rivalry match hypothesis. After winning a match against a rival, Ajax’s average daily

net return was 0.22%, compared to an average net return of 0.20% when winning a match

against a non-rival. This resulted in a small difference of only 0.02% when Ajax defeated a

rival club versus a non-rival.

When testing my third European match hypothesis for Ajax, the results strongly supported my

hypothesis. When Ajax won a European match, their average daily net return was calculated to

AJAX.AS Adj Close After Match Played

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 32 -

be 0.45%, compared to just 0.15% after winning a domestic match in the Eredivisie. Ajax had

a 0.30% better net return when winning a European match versus winning a match in the

Netherlands.

Ajax Predictive Model

When running different regression models for Ajax, their strongest predictive model included

only the match type variable. Ajax’s predictive model came out to be Average Daily Net Return

= 0.14% + 0.34%(Match Type). The R-squared for this model was calculated to be 0.0045,

meaning that 0.45% of the variance in Ajax’s average daily net return is captured by the

independent match type variable. The p-value for the match type variable in this model was

0.16, indicating a statistically significant relationship between the independent and dependent

variables in this model.

FC Copenhagen (PARKEN.CO)

FC Copenhagen are a professional club based in Copenhagen and they play their matches in the

Danish Superliga, which is the top professional soccer league in Denmark. They are publicly

traded on the Nasdaq Copenhagen. Their current total market capitalization is about $1.09

billion. When looking at their volatility metrics, FC Copenhagen’s overall standard deviation

of net returns over the observed period was calculated to be 2.31%. The Nasdaq Copenhagen

benchmark’s standard deviation of daily returns over the same period was only 1.11%. This

shows FC Copenhagen’s individual stock was about 1.20% riskier than the benchmark over the

same period.

100

120

140

PARKEN.CO Adj Close After Match Played

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 33 -

Figure 12: Line chart that shows the price movement of PARKEN.CO from 2012 – 2021.

The results of FC Copenhagen’s returns indicate a negative relationship between average daily

net return and match results. After FC Copenhagen won a match their average daily net return

was calculated to be -0.21%, and when they lost a match it was -0.27%. While their returns

were slightly better after winning a match, the results still do not support my first match

hypothesis as a negative relationship was demonstrated. Over the observed period FC

Copenhagen’s overall average daily net return was -0.23%, so their net returns were not heavily

affected by any match results.

In the Danish Superliga, FC Copenhagen was determined to have four main rivals. These four

rivals were AGF, Midtjylland, Nordsjælland, and Randers. When FC Copenhagen played a

match against one of these teams the rivalry variable was set equal to 1. The results did support

my second rivalry hypothesis as FC Copenhagen’s average daily net return after a rivalry win

was -0.11%, and after a non-rivalry win it was -0.23%. So, FC Copenhagen’s net returns were

better after defeating a rival compared to a non-rival.

When testing my third European match hypothesis for FC Copenhagen, the results did not

support my hypothesis. After winning a European match, FC Copenhagen’s average daily net

return was -0.44%, compared to -0.14% when winning a match in the Danish Superliga. They

had a 0.30% better return after winning a Domestic game versus a European game.

FC Copenhagen Predictive Model

When running different regression models for FC Copenhagen, their strongest predictive model

included just the rival variable. FC Copenhagen’s predictive model came out to be Average

Daily Net Return = -0.28% + 0.29%(Rival). The R-squared value for this model was calculated

to be 0.0025, meaning that 0.25% of the variance in FC Copenhagen’s average daily net return

is captured by the independent rivalry variable. The p-value for the rivalry variable was 0.36,

indicating a weakly significant statistical relationship between the independent and dependent

variables in the model.

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 34 -

Sporting Club Portugal (SCP.LS)

Sporting Club Portugal are a professional club based in Lisbon and they play their matches in

the Primeira Liga, which is the top professional soccer league in Portugal. They are publicly

traded on the Euronext Lisbon Exchange. Their current total market capitalization is about

$48.25 million. When looking at their volatility metrics, Sporting Club Portugal’s overall

standard deviation of net returns over the observed period was calculated to be 6.13%. The

Euronext Lisbon benchmark’s standard deviation of daily returns over the same period was

only 1.05%. This shows Sporting Club Portugal’s individual stock was 5.08% riskier than the

benchmark over the same period.

Figure 13: Line chart that shows the price movement of SCP.LS from 2012 – 2021.

The results of Sporting Club Portugal’s returns indicated a negative relationship between

average daily net returns and match wins. After Sporting Club Portugal won a match, there

average daily net return was -0.11%, whereas after a loss it was 0.80%. This was a rare case in

the sample, as Sporting Club Portugal’s net returns were better after a loss. Their overall average

daily net return in the observed period was 0.07%, so their returns were better than their average

after a loss.

In the Primeira League, it was determined that Sporting Club Portugal had three main rivals.

These rivals were FC Porto, SL Benfica, and Sporting Club de Braga. Whenever Sporting Club

Portugal played a match against one of these teams, the rivalry variable was set equal to 1. The

0.2

0.4

0.6

0.8

1.2

1.4

SCP.LS Adj Close After Match Played

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 35 -

results did not support my second rivalry hypothesis, as after Sporting Club Portugal won a

rivalry match their average daily net return was -0.20%, compared to -0.11% after a non-rival

win. Sporting Club Portugal’s average daily net return was 0.09% better after winning a match

against a non-rival compared to beating a rival.

When testing my third European match hypothesis for Sporting Club Portugal, the results

strongly supported my hypothesis. Their average daily net return after winning a European

match was 2.04%, compared to -0.40% after winning a domestic match. European matches for

Sporting Club Portugal seemed to generate more positive investor sentiment than domestic

matches.

Sporting Club Portugal Predictive Model

When running different regression models for Sporting Club Portugal, their strongest predictive

model included only the rivalry variable. Sporting Club Portugal’s predictive model came out

to be Average Daily Net Return = -0.15% + 1.47%(Rival). The R-squared for this model was

calculated to be 0.007, meaning that 0.70% of the variance in Sporting Club Portugal’s average

daily net return was captured by the independent rivalry variable. The p-value for the rivalry

variable was 0.11, indicating a statistically significant relationship between the independent and

dependent variables in the model.

CONCLUSION

After taking into account the existing literature and results from the regression analysis, the

overarching hypothesis, “Match wins will have a strong positive correlation to the stock returns

of publicly traded soccer teams”, was proven to be true. Twelve of the thirteen teams in the

sample demonstrated stronger average daily net returns after the team won a match compared

to when they lost a match. The only team from the sample to not show this was Sporting Club

Portugal. The other two hypotheses surrounding rivalry matches and European matches were

proven to be true for some teams, but not others. Each club’s individual stock told their own

respective story. The strength to which the rivalry and European match variables affected each

individual team’s stock was unique. The respective p-values of the independent variables in

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 36 -

each club’s predictive model showed how significant they were in the model. Every predictive

model had a relatively low R-squared value which can be attributed to the multitude of factors

that affect the price movement of stocks every day. While competitive match factors do play a

role in affecting the daily net returns of publicly traded soccer teams, they only account for a

small portion of what influences these stocks. Publicly traded soccer teams are very specialized

assets, and it can be seen they are all very volatile compared to their respective benchmarks. If

an individual were looking to incorporate these assets into an investment portfolio, hedging

strategies must be used to mitigate the amount of risk that comes with these stocks.

Taking all these results into consideration, competitive match factors such as match results,

rivalries, and European matches play a role in affecting the day-to-day price movements of

publicly traded soccer teams. However, the degree to which each of these variables, along with

many others, affect the daily net returns of these assets is unique to the individual team. Winning

a rivalry or European match could generate more positive investor sentiment for one club than

another. It is in the best interest of each of the teams in the sample to do everything they can to

win, as winning seems to have a positive influence on their stock price.

FURTHER ANALYSIS

One of the ways I would like to conduct a further analysis for each of these teams would be to

look at how player signings and other financial transactions affected the daily returns for each

of the clubs. Utilizing an event study and regression would be beneficial methods for me to use

for this. While some clubs spend their money more freely and frequently than others, an event

study would allow me to analyze the efficiency of individual player transactions for these clubs

in terms of how well their stock performs on a day-to-day basis. Additionally, I would also like

to conduct further analysis into the match performance of publicly traded clubs versus privately

owned clubs. A basic statistical analysis of how teams perform in each season using variables

such as expected match outcome, player efficiency ratings, and points would allow me to

perform an in-depth analysis on how ownership structure influences the performance of soccer

clubs. With all the variables that affect publicly traded soccer teams, it would be interesting to

get a closer look at player transactions and ownership structure in particular.

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 37 -

APPENDICES

Appendix A – Complete Regression Results

Manchester United (MANU)

Regression

Statistics

Multiple R

0.1301671

R Square

0.0169435

Adjusted R Square

0.0131184

Standard Error

0.016681

Observations

517

Coefficients

Standard

Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

-0.0003

0.0014

-0.1888

0.8504

-0.0030

0.0025

Result

0.0011

0.0006

1.9849

0.0477

0.0000

0.0023

Rival

-0.0036

0.0019

-1.8973

0.0583

-0.0073

0.0001

Juventus (JUV.MI)

Regression

Statistics

Multiple R

0.185865

R Square

0.034546

Adjusted R Square

0.032689

Standard Error

0.029672

Observations

522

Coefficients

Standard

Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

-0.0078

0.0028

-2.7592

0.0060

-0.0134

-0.0023

Result

0.0048

0.0011

4.3135

0.0000

0.0026

0.0070

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 38 -

Borussia Dortmund (BVB.DE)

Regression

Statistics

Multiple R

0.49578

R Square

0.245798

Adjusted R

Square

0.242446

Standard Error

0.019489

Observations

453

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

-0.0155

0.0018

-8.8500

0.0000

-0.0189

-0.0121

Result

0.0086

0.0007

12.1086

0.0000

0.0072

0.0100

Rival

0.0026

0.0023

1.1431

0.2536

-0.0019

0.0072

Olympique Lyon (OLG.PA)

Regression

Statistics

Multiple R

0.3817971

R Square

0.1457691

Adjusted R Square

0.143932

Standard Error

0.0234641

Observations

467

Coefficients

Standard Error

t Stat

P-value

Lower

95%

Upper

95%

Intercept

-0.0114

0.0018

-6.2724

8.141E-10

-0.0149

-0.0078

Result

0.0074

0.0008

8.9078

1.181E-17

0.0057

0.0090

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 39 -

Galatasaray SK (GSRAY.IS)

Regression Statistics

Multiple R

0.3297765

R Square

0.1087525

Adjusted R Square

0.1022471

Standard Error

0.0343464

Observations

415

Coefficients

Standard

Error

t Stat

P-value

Lower

95%

Upper

95%

Intercept

-0.0196

0.0035

-5.60159

3.895E-08

-0.0264

-0.0127

Result

0.0097

0.0014

7.0495

7.637E-12

0.0070

0.0124

Match Type

0.0070

0.0050

1.3968

0.1632

-0.0028

0.0168

AS Roma (ASRAF)

Regression Statistics

Multiple R

0.09264109

R Square

0.00858237

Adjusted R Square

0.00304373

Standard Error

0.0481474

Observations

570

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

-0.0021

0.0037

-0.5701

0.5693

-0.0095

0.0053

Rival

0.0157

0.0126

1.2448

0.2148

-0.0092

0.0406

SL Benfica (SLBEN.LS)

Regression

Statistics

Multiple R

0.033831

R Square

0.001145

Adjusted R Square

-0.0014

Standard Error

0.044536

Observations

394

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

0.0066

0.0024

2.7218

0.0068

0.0018

0.0113

Rival

-0.0044

0.0065

-0.6702

0.5031

-0.0172

0.0085

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 40 -

Celtic FC (CCP.L)

Regression Statistics

Multiple R

0.533212

R Square

0.284315

Adjusted R Square

0.280305

Standard Error

0.01269

Observations

360

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

-0.0198

0.0015

-13.4839

0.0000

-0.0227

-0.0169

Result

0.0017

0.0005

3.1436

0.0018

0.0006

0.0027

Match Type

0.0172

0.0014

11.9089

0.0000

0.0144

0.0200

FC Porto (FCP.LS)

Regression

Statistics

Multiple R

0.116434

R Square

0.013557

Adjusted R Square

0.008181

Standard Error

0.050722

Observations

370

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

-0.0083

0.0057

-1.4587

0.1455

-0.0196

0.0029

Result

0.0038

0.0022

1.7287

0.0847

-0.0005

0.0082

Rival

0.0126

0.0077

1.6382

0.1022

-0.0025

0.0278

Lazio (SSL.MI)

Regression Statistics

Multiple R

0.097692

R Square

0.009544

Adjusted R Square

0.00419

Standard Error

0.023708

Observations

489

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

0.0060

0.0027

2.2097

0.0284

0.0006

0.0113

Result

0.0017

0.0013

1.3351

0.1835

-0.0008

0.0043

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 41 -

Ajax (AJAX.AS)

Regression Statistics

Multiple R

0.067072

R Square

0.004499

Adjusted R Square

0.002226

Standard Error

0.021827

Observations

440

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

0.0014

0.0012

1.1271

0.2603

-0.0010

0.0037

Match Type

0.0034

0.0024

1.4069

0.1602

-0.0013

0.0081

FC Copenhagen (PARKEN.CO)

Regression Statistics

Multiple R

0.050251

R Square

0.002525

Adjusted R Square

-0.00046

Standard Error

0.02309

Observations

336

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

-0.0028

0.0014

-2.0282

0.0433

-0.0056

-8.5536E-05

Rival

0.0029

0.0032

0.9195

0.3585

-0.0034

0.0093

Sporting Club Portugal (SCP.LS)

Regression Statistics

Multiple R

0.085588

R Square

0.007325

Adjusted R Square

0.004414

Standard Error

0.061138

Observations

343

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

-0.0015

0.0036

-0.4100

0.6821

-0.0085

0.0056

Rival

0.0147

0.0093

1.5863

0.1136

-0.0035

0.0330

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 42 -

REFERENCES

Antoniewicz, R. (S., &; Heinrichs, J. (2014). Understanding exchange-traded funds: How etfs

work. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2523540

Baker, S., Bloom, N., Davis, S., Kost, K., Sammon, M., & Viratyosin, T. (2020). The

unprecedented stock market impact of COVID-19. https://doi.org/10.3386/w26945

Baur, D. G., & McKeating, C. (2011). Do Football Clubs Benefit from Initial Public

Offerings? International Journal of Sport Finance, 6(1).

Bernile, G., & Lyandres, E. (2011). Understanding investor sentiment: The case of soccer.

Financial Management, 40(2), 357–380. https://doi.org/10.1111/j.1755-053.2011.01145.

Berument, M. H., & Ceylan, N. B. (2012). Effects of soccer on stock markets: The return–

volatility relationship. The Social Science Journal, 49(3), 368–374.

https://doi.org/10.1016/j.soscij.2012.03.003

Brown, A. (2007). ‘Not for sale’? The destruction and reformation of football communities in

the Glazer takeover of Manchester United. Soccer & Society, 8(4), 614–635.

https://doi.org/10.1080/14660970701440972

Demir, E., & Rigoni, U. (2017). You lose, I feel better. Journal of Sports Economics, 18(1),

58–76. https://doi.org/10.1177/1527002514551801

Drewes, M., Daumann, F., & Follert, F. (2020). Exploring the sports economic impact of

covid-19 on professional soccer. Soccer & Society, 22(1-2), 125–137.

https://doi.org/10.1080/14660970.2020.1802256

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 43 -

Freestone, C. J., & Manoli, A. E. (2017). Financial fair play and competitive balance in the

premier league. Sport, Business and Management: An International Journal, 7(2), 175–

196. https://doi.org/10.1108/sbm-10-2016-0058

Frick, B., & Semmelroth, D. (2021). The effects of (UN)expected match outcomes on stock

return: A case study of Borussia Dortmund. International Journal of Sport Finance,

16(4). https://doi.org/10.32731/ijsf/164.112021.01

Gerretsen, S. & Jakar, G. (2021). Ownership in European soccer, Financial Fair Play, and

performance in UEFA’s 2006–2018 Champions League tournaments. Journal of Sport

Management, 35(6), 511–521. https://doi.org/10.1123/jsm.2020-0217

Hagen, J., & Cunha, M. (2019). The History of Investing in Football and Factors Affecting

Stock Price of Listed Football Clubs. International Journal of Financial Management,

9(4).

Limba, F. B., Rijoly, J. C., & Tarangi, M. I. (2020). Black Swan Global Market: Analysis of

the effect of the COVID-19 death rate on the volatility of European Football Club Stock

prices (case study of juventus F.C., Manchester United, Ajax Amsterdam and Borussia

Dortmund). 7(3). https://doi.org/10.35794/jmbi.v7i3.32690

Mazur, M., Dang, M., & Vega, M. (2020). Covid-19 and the March 2020 Stock Market Crash.

Evidence from S&P1500. https://doi.org/10.1016/j.frl.2020.101690

Mourao, P. R. (2016). Soccer transfers, team efficiency and the sports cycle in the most

valued European soccer leagues – have European soccer teams been efficient in

trading players? Applied Economics, 48(56), 5513–5524.

https://doi.org/10.1080/00036846.2016.1178851

Match Results and the Equity Value of Publicly Traded Soccer Teams

Honors Thesis for Ryan Bebyn

- 44 -

Plumley, D., Wilson, R., & Ramchandani, G. (2013). The relationship between ownership

structure and club performance in the English premier league. Sport, Business and

Management: An International Journal, 3(1), 19–36.

https://doi.org/10.1108/20426781311316889

Prigge, S., & Tegtmeier, L. (2019). Market valuation and risk profile of listed European

Football Clubs. Sport, Business and Management: An International Journal, 9(2), 146–

163. https://doi.org/10.1108/sbm-04-2018-0033

Putranto, P. (2019). The effect of Cristiano Ronaldo’s recruitment on Football Club’s stock

prices in Italian League. Journal of Economics and Sustainable Development, 10(4).

https://doi.org/10.7176/jesd/10-4-18

Rohde, M., & Breuer, C. (2016). Europe’s Elite Football: Financial Growth, sporting success,

transfer investment, and private majority investors. International Journal of Financial

Studies, 4(2), 12. https://doi.org/10.3390/ijfs4020012

Sæbø, O. D., & Hvattum, L. M. (2019). Modelling the financial contribution of soccer players

to their clubs. Journal of Sports Analytics, 5(1), 23–34.

https://doi.org/10.3233/jsa-

170235

Sevil, T., Kamish, S., & Kamish, M. (2014). Analyzing the Interactions between European

Sport Indexes. International Journal of Business and Social Science, 5(10).

Tenga, A., Kanstad, D., Ronglan, L. T., & Bahr, R. (2009). Developing a new method for

Team Match Performance Analysis in professional soccer and testing its reliability.

International Journal of Performance Analysis in Sport, 9(1), 8–25.

https://doi.org/10.1080/24748668.2009.11868461