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Annals of the
University of North Carolina Wilmington
International Masters of Business Administration
http://csb.uncw.edu/imba/
http://www.csb.uncw.edu/imba/

PORTFOLIOS OF ATHLETES: SECURITIZATION OF TENNIS PLAYERS
Pedro E M Mol
A Thesis Submitted to the
University of North Carolina Wilmington in Partial Fulfillment
of the Requirements for the Degree of
Master of Business Administration
Cameron School of Business
University of North Carolina Wilmington
2014
Approved by
Advisory Committee
Peter Schuhmann Adam Jones
Joseph Farinella
Chair
Accepted by
Dean, Graduate School

ii
TABLE OF CONTENTS
ABSTRACT ...................................................................................................................................... iii
LIST OF TABLES ............................................................................................................................ iv
LIST OF FIGURES ............................................................................................................................v
1. INTRODUCTION ..........................................................................................................................1
1.2 Purpose of Thesis ......................................................................................................................2
1.2.1 General Objective ......................................................................................................................2
1.2.2 Specific Objectives and Research Questions .............................................................................2
2. LITERATURE REVIEW ...............................................................................................................3
2.1 Securitization ............................................................................................................................3
2.2 Finance needs in professional tennis.........................................................................................4
2.3 Financing in other sports ...........................................................................................................6
2.4 Determinants of success in professional tennis ........................................................................7
2.5 Crowdfunding .........................................................................................................................11
2.6 Lead to the research questions ................................................................................................13
2.7 Other business models ............................................................................................................15
2.8 Conclusion of literature...........................................................................................................16
3. DATA AND METHODOLOGY ..................................................................................................16
4. RESULTS .....................................................................................................................................21
4.1 Back testing portfolios ............................................................................................................21
4.1.1 Portfolio I: Top 20 ITF Juniors 1998 .......................................................................................23
4.1.2 Portfolio II: ATP500600 under the age of 26 in 1998 ............................................................25
4.1.3 Portfolio III: Top 50 ITF Juniors 2004 ....................................................................................29
4.1.4 Portfolio IV: Top 50 ITF Juniors 2005 ....................................................................................31
4.1.5 Portfolio V: 20 College Players Who Turned Pro ...................................................................34
4.1.6 Comparing Portfolios ...............................................................................................................37
4.2 The relationship between ranking and earnings in men’s professional tennis ........................38
4.2.1 The relationship between singles ranking and earnings in singles ..........................................39
4.2.2 The relationship between doubles ranking and earnings in doubles........................................42
4.2.3 The relationship between earnings and rank in singles and doubles ......................................44
4.2.4 The change in impact of rank on total prize money over time ................................................47
4.3 Future prize money in tennis...................................................................................................53
5. CONCLUSION .............................................................................................................................60
5.1 Portfolios .................................................................................................................................60
5.2 Impact of ranking on earnings in men’s professional tennis ..................................................61
5.3 The change in impact of ranking on earnings in men’s professional tennis. ..........................62
5.4 The benefits of creating portfolios of tennis players. .............................................................63
5.5 Topics for new research ..........................................................................................................63
7. REFERENCES .............................................................................................................................65
8. APPENDIX – SLAMSTOX .........................................................................................................66

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ABSTRACT
The following thesis gives an insight in the financing needs, prize money structures and
determinants of success of professional tennis players. It will provide an analysis on how
combining tennis players into portfolios and selling shares of these portfolios to investors could
be beneficial to the players, the investors and the sport of tennis in general. The results of the
thesis are divided into different sections.
The first section examines the profitability of portfolios that could have been created in
the past and what players could have been selected for these portfolios. These portfolios will be
analyzed and compared to each other in terms of prize money and profitability. It will give an
insight in the earnings of junior players, active professional players and former college players.
The second section examines the relationship between ranking and earnings in men’s
professional tennis and how this relationship has changed over the period of 1998 until 2013. It
provides an analysis on how to predict prize money by ranking in tennis for singles, doubles and
the combination of the two. The formula that is created by this analysis in combination with
prize money during past editions of Grand Slam tennis tournaments will then be used to predict
the future income of professional tennis players.
In addition, an appendix will show information on the company that has been started out
of the idea of this thesis: creating portfolios of athletes and selling shares of these athletes to
investors. The name of the company is Slamstox and more information can be found on
www.slamstox.com.
http://www.slamstox.com/

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LIST OF TABLES
Table Page
1 Portfolio Top 20 ITF Juniors 1998 ................................................................................... 23
2 Earnings Portfolio Top 20 ITF Juniors 1998 .................................................................... 24
3 Portfolio ATP500600 under the age of 26 in 1998 ........................................................ 26
4 Earnings portfolio ATP500600 under the age of 26 in 1998 .......................................... 26
5 Portfolio Top 50 ITF Juniors 2004 ................................................................................... 30
6 Earnings portfolio Top 50 ITF Juniors 2004 .................................................................... 30
7 Portfolio top 50 ITF Juniors 2005 ..................................................................................... 32
8 Earnings portfolio Top 50 ITF Juniors 2005 .................................................................... 33
9 Portfolio 20 ex college players who turned pro ................................................................ 35
10 Earnings portfolio 20 ex college players who turned pro ................................................. 35
11 Results of regression ranking on log singles prize money ................................................ 41
12 Results of regression ranking on log doubles prize money .............................................. 43
13 Results of regression singles and doubles rank on total prize money ............................... 46
14 Regressions of ranking on prize money over the years 19992013 .................................. 48
15 Singles coefficients of regressions over the years 19992013 .......................................... 49
16 Regression of time on actual coefficients ......................................................................... 52
17 Prize Money Wimbledon; past 3 editions and expectation for next 2 editions ................ 54
18 Prize Money US Open; past 3 editions and expectation for next 2 editions..................... 55
19 Prize Money AUS Open; past 3 editions and expectation for next 2 editions .................. 56
20 Prize Money French Open; past 3 editions and expectation for next 2 editions............... 57
21 Expected difference in pay between top 128 players and rest .......................................... 59

v
LIST OF FIGURES
Figure Page
1 Profit and return for investors in Portfolio I ..................................................................... 25
2 Earnings by age group Portfolio II.................................................................................... 27
3 Profit and return for investors in Portfolio II .................................................................... 28
4 Profit and return for investors in Portfolio II with new conditions ................................... 29
5 Profit and return for investors in Portfolio III ................................................................... 31
6 Profit and return for investors in Portfolio IV .................................................................. 33
7 Profit and return for investors in Portfolio V .................................................................... 36
8 The relationship between singles rank and earnings......................................................... 40
9 Log linear model of singles prize money and singles ranking.......................................... 40
10 The relationship between doubles rank and doubles earnings .......................................... 42
11 Log linear model of doubles prize money and doubles ranking ....................................... 43
12 Relationship between singles rank, doubles rank and total prize money earned .............. 45
13 Average change in total prize money for a 1 unit change in rank .................................... 50
14 Trend line through the coefficients ................................................................................... 51
15 The 95% area to predict the coefficient in the future ........................................................ 53

INTRODUCTION
1.1 Justification of the selected topic.
It is well known that professional tennis players can earn large amounts of money during their
careers. For example, last year the average earnings of the top ten tennis players was $5,513,076.
Even though top players earn millions, it is often very difficult for tennis players to obtain
financing to begin their careers. New professionals do not earn this amount of prize money and
still need money for traveling, hotels, coaching and other expenses. There are many talented tennis
players who may be able to generate a profit in the future but don’t have the funds to start playing
professional tennis. The securitization of tennis players would make it possible for new
professional players to obtain funds for their careers. Securitization is a process used in finance to
sell percentage ownership interest in an asset. In this case, the asset is the earnings of the tennis
player. An investor would purchase the security and receive a percentage of future tournament
winnings and endorsement deals.
The securitization process becomes more attractive when several players are sold as a portfolio
to decrease risk. Creating a portfolio consisting of different tennis players and having investors
invest in this portfolio gives a group of players the chance to compete on the ATP World Tour.
There would probably only be a few of them that make it into the top 100 and earn significant
prize money. However, even a few top players would make the portfolios profitable for investors.
The most profitable players will end up paying back more money than they received but without
the portfolio they would never have gotten the chance to start playing professional tennis. There
have been a few cases where investors directly invested in tennis players, but a public investment
pool does not exist and no previous research has been done in this area.

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Investors could carefully pick the right portfolio with players and have large potential returns a
few years down the road. By creating a trading platform that lets outside investors invest in
portfolios of tennis players, players can obtain the financial resources and a chance to compete on
the ATP World Tour. An online trading platform can not only bring financing to players and
returns back to investors, it will also determine the market price of several upcoming professionals
as their shares will be traded on the platform. There will be more interest in players and
tournaments which will increase publicity money. An increase in interest and publicity money can
then increase potential prize money in the future which will lead to higher possible returns for
investors. If this spiral keeps continuing, there are opportunities for the professional sport of tennis
to grow in the future. The results of this study are expected to give a better insight into the earnings
of tennis players and the impact that tennis rankings have on yearly earnings. The results are also
expected to provide an analysis of portfolios of profitability of various portfolios of tennis players.
1.2 Purpose of Thesis
1.2.1 General Objective
The primary purpose of this research is to investigate and analyze earnings of professional
tennis players and the impact that rankings have on earnings in men’s professional tennis. The
research should also give an overview of ways to create profitable portfolios of tennis players for
investors. This new way of investing could transform the future of sports finance and help the sport
of tennis grow.
1.2.2 Specific Objectives and Research Questions
The thesis offers an insight into the earnings of tennis players and how this could be
interesting for potential investors in the future. To achieve this insight, specific questions are
examined. The research questions are:

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RQ1: What would profits have been for investors if they would have invested in certain portfolios
of tennis players in the past?
RQ2: What players should be selected for the portfolios?
RQ3: What is the relationship between rankings and prize money of men’s
professional tennis players?
LITERATURE REVIEW
2.1 Securitization
Basu (2005) defines securitization as “a process through which homogenous illiquid
financial assets are pooled and repackaged into marketable securities”. He states that these assets
are usually held in a “bankruptcy remote” vehicle which is called a SPV (Special Purpose
Vehicle). Basu also states that most securitization issues are rated by a credit rating agency and
that these agencies determine the likelihood of interest and/or principal payments in the future. The
owner of the assets is called the originator or transferor and transfers the assets to be securitized to
a SPV as the asset purchaser. The SPV can be a corporation or other legal entity and issues
securities to public or private investors. Basu (2005) mentions that securitization transactions deal
with many different asset classes. Examples are mortgages, credit card receivables, real estate
assets, home equity loans and many more. Basu (2005) categorizes securitization into two different
categories: Asset backed securitization and Future flow securitization. Asset backed securities are
securities backed by a certain financial asset, such as a loan or a lease. In contrast, future flow
securities are backed by the generation of future cash flows. Future cash flows could be the
earnings of an athlete. Furthermore, Basu explains how almost all securitization deals involve
complex underlying contracts between the originator and the obligors. Rights and obligations of
the different parties should always be defined in these contracts.

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Fabozzi and Kothari (2007) see securitization as a financial instrument that has had an
important impact on the world’s financial system. They state that securitization has strengthened
the trend towards disintermediation and that it has made lending easier in the financial world. They
also describe the fact that sometimes securities are structured into different classes, such as Class
A, Class B and so on. The reason these various classes are created is so that certain investors can
have a superior right over other investors, depending on the class they have invested in. Usually
the lowest ranked investors will absorb the earliest losses and the higher ranked investors have the
first right on profits. The higher classes therefore have a cushion (usually about 5%) against losses
because there are lower class investors. However, higher class investors might have a lower
couponpayment because they have a lower riskinvestment.
Fabozzi and Kothari (2007) state that the four main motivations for securitization are;
potential for reducing funding costs, diversifying funding sources, managing corporate risk and
achieving offbalance sheet financing. They also describe the process of bringing a lender
(investor) and a borrower together and how the lender is usually the one responsible for analyzing
the financial condition of the borrower and to prepare the legal documentation. In most cases the
investor does not have enough information or resources to do so, which is why a financial
intermediary is asked for help and paid a fee to do this work.
2.2 Finance needs in professional tennis
The need for financing professional tennis players that are starting their careers is very
large. Tennis is an individual sport where players need to find their own way to the top to make
money. Some players use sponsorship money, personal funds or family money to finance the
beginnings of their professional careers. Timothy Russell (2010) calculated that the costs of being

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a pro tennis player are about $143,000 per year. These costs can be allocated as $70,000 for having
a coach traveling with the player, $60,000 for traveling expenses, hotel expenses and food
expenses, $12,000 for physical coaching and $1,000 for mental coaching. This calculation is based
on a schedule of 20 tournaments per year which would lead to a traveling cost of $3,000 per
tournament. If a player designs a schedule in which he plays several tournaments in the same
region, he could drastically save traveling money and thus play a full year of professional tennis at
lower cost.
Morales (2013) conducted an interview with professional tennis player Michael Russell,
ranked 92nd
in the world at that time. Russell had just won a tournament in Ecuador that netted him
around $5,000 dollars, yet he barely broke even in that week. Russell estimates his yearly costs
around $75,000 dollars, of which $35,000 was allocated to traveling and $9,000 dollars to racket
stringing. Russell has earned approximately $2.1 million in prize money during his 15 year career.
In comparison, Morales stated that Roger Federer has earned over $70 million in prize money and
over $60 million in sponsorship endorsements.
Morales (2013) states that the pay gap between the highest ranked professionals and the
vast majority of professional tennis players is widening. A player ranked inside the top 100 gets
direct entry into the Grand Slams, while everyone else usually plays in lower tiered events such as
futures, challengers and qualifying tournaments. Morales (2013) also states that prize money at
futures and challengers has remained flat since 1990, whereas prize money at the US Open (one of
the four grand slams) increased with 429% during the same period. Prize money at Wimbledon
increased with 554% since 1990 and with 210% since 2000 (Wimbledon.com). According to the
ATP World Tour, Wimbledon announced a 40% increase in prize money in 2013 and this year
(2014) they announced an increase in prize money of 10.8%. A player losing in the first round of

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Wimbledon received £27000 ($45,850) and the winner (Novak Djokovic) took home £1.760.000
($2,988,911). In 2013, a first round loser at the US Open received $32,000 and the winner (Rafael
Nadal) received $2,600,000. Russell played 32 tournaments in the previous year including 4 Grand
Slams. Out of all his prize money, 40% came from these 4 Grand Slams. Even though playing the
lower tiered challengers and futures might not seem profitable, they are opportunities to win points
to increase ranking and to increase the chances of entering the main draw of Grand Slam
tournaments. The risk of playing too many tournaments is that one or two serious injuries could
turn a promising year into a catastrophe (Morales, 2013).
The Association of Tennis Professionals (ATP) designed a pension program to support
players financially once they quit playing tennis. This is only available for the yearend top 125
singles players and the top 40 doubles players who qualify for at least five years. This program
pays out the same amount to the number one and the number 125 which makes it a socialistic
program. However, the vast majority of professional tennis players are not ranked in the top 125
for five years and they need financial support the most. Top players like Roger Federer and Novak
Djokovic have said that the income distribution for professional tennis players should be more
equal and the ATP has announced to come up with new initiatives to keep lower ranked players
and potential new tennis players attracted to the game (Robson, 2012).
2.3 Financing in other sports
Investments in athletes have been made in other games and sports; some professional
soccer clubs let private investors invest in some of their players to train and develop them before
they get signed and bought by bigger European clubs for a lot of transfer money. Several poker
players have other people pay their buyins at tournaments in exchange for a percentage of the
prize money and investors can invest in thoroughbred racehorses. (Passy, 2014)

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Another example of an athlete who sold shares in his future earnings to acquire funds
during the initial stages of his career is Dutch professional golf player Maarten Lafeber. In 1997,
Lafeber set up a company representing himself called Future Golf BV. He set up an investment
policy with Dutch private bank Theodoor Gillissen Bankiers and sold 7500 shares worth 100
Dutch guldens each to raise 750,000 guldens. This amount translates to around €340,000 euros,
raised for Future Golf BV, and Lafeber calculated it would be enough for him to play professional
golf for about 5 to 7 years. Shareholders were not able to sell or short sell the shares and were
dependent on the results of Maarten Lafeber. Most of the shareholders were friends of Lafeber or
other golfers who considered themselves his “fan club”. De Raat (2002) states that the deal with
these shareholders was that they would have the right to receive dividends on their shares after the
5 to 7 year period if and only if Lafeber won enough prize money to pay dividends. Lafeber also
had the right to buy back all the shares at the original price plus five percent per year plus a 50%
premium per share. Seven years later in November 2004 this is what happened. Lafeber bought
back all outstanding shares for a price of €84 which is 185 guldens. This price was determined by
the original price of 100 plus five percent for seven years and a 50% premium per share
(maartenlafeber.com). Investors ended up with a profit of 85 guldens equivalent to a return of 9%
per year. The benchmark of the AEX stock exchange in The Netherlands during this period was
approximately 4%.
2.4 Determinants of success in professional tennis
Logically, winning tennis matches will result in earning more prize money and a higher
ranking which could then lead to better chances of entering tournaments with higher prize money.
Factors that affect the probability of success in professional tennis have been examined in the
literature. To estimate the chances of a tennis player winning a match, Corral & Rodriguez (2010)

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state that there are two main methods for predicting the outcome of a sport event; statistical models
and expert evaluations. Some scholars have compared the accuracies of these competing methods.
(Boulier & Stekler, 2003; Forrest, Goddard & Simmons, 2005). Caudill and Godwin, 2002 and
Clarke and Dyte, 2000 used tennis rankings to estimate the chance of winning as a function of the
difference in rating points, and were able to estimate a player’s chance of a tournament victory
once the draw for the tournament became available. Gilsdorf and Sukhatme (2007) found that if
there is a larger difference in potential prize money between the winner and loser of a match, there
is a smaller chance that an upset will occur.
Corral & Rodriguez (2010) use regression models to see if one could predict Grand Slam
tennis matches (20052008) looking at a player’s past performance, a player’s physical
characteristics and match characteristics. In their first model they use all three of these variables, in
their second model they remove past performance and in their third model they remove physical
characteristics. They find that rank differences are more important at the top of the distribution of
players for both men and women. Over the period of their study, the probability that a higher
ranked player wins is 71.2%, making rank the most significant variable in predicting wins.
Previous outcomes in the same tournament last year were also found to be significant determinants
of the outcome in a tournament this year. Corral & Rodriguez also found that tennis skills are
much more surfacebiased in men’s tennis than in women’s tennis. This means that the surface on
which a tournament is played (e.g. hard court, grass or clay) has a bigger impact on the results in
men’s tennis than it has on the results in women’s tennis. They find that if a player has previously
been ranked in the top 10, this is more important when predicting women’s matches than men’s
matches. They state that the probability that the higher ranked player will win decreases as the
player competes against younger players. Lefthanded lowerranked players are more likely to

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defeat righthanded, higherranked players. A higherranked player has 5.9% less chance of
winning when they face a lefthanded player. Corral & Rodriguez also state that models that use
players’ past performances outperform those that do not. They used an outofsample (Australian
Open 2009) dataset to analyze their forecasting accuracy. This dataset provided the same outcome;
the most important variables for forecasting accuracy are related to past performance and rankings.
Ovaska & Summell (2014) create models to show how different characteristics influence
the probability of the higher ranked player winning a tennis match. They state that a few players
make a lot of money; only 4% of professional tennis players will ever win an ATP tournament.
They also state that players need to enter the top 100 to make a 6 figure income. To make enough
money for life after tennis, a player needs to stay in the top 50 for several years. They find that
when prize money increases from mean to upper quartile, the probability of the higher ranked
player winning increases by 2.8%. Larger prize money spread is positively related with effort. The
retirement age in tennis is negatively correlated with a player’s highest recent ranking, which could
be explained by the cost of quitting. Ovaska & Summell (2014) suggest that there are a few flaws
in the ATP rankings; it only uses 52 weeks of information, it gives an equal weight to
performances in the near and distant past, it ignores the closeness of previous matches and doesn’t
differentiate among play surfaces.
Ovaska & Summell used a dataset of professional tennis matches over ten years (2000
2009), excluding matches with retirements. They collect 27,388 observations from 669
tournaments. The average total prize money for Grand Slam tournaments is $7.32 million and for
Masters Series tournaments it is $2.88 million. They state that the probability of a higher ranked
player winning the match is a function of player characteristics, matchspecific characteristics and
the expected net reward from winning. They find that a higher ranked player wins 64.8% of the

10
time, but this increases to 70% in Grand slam matches. The bigger the rank differential, the greater
the probability the higher ranked player wins. Like Gilsdorf and Sukhatme (2007), Ovaska &
Summell also state that higher ranked players are more likely to win more meaningful matches.
They created variables such as total prize money, importance of the tournament and the round of
play. Winning more important tournaments translates into more prize money, but often also into
other lucrative promotions and sponsorship deals. In a final match of a tournament the higher
ranked player is 5.8% more likely to win the match compared to earlier rounds. They also state that
higher ranked players have the best financial means to improve the psychological side of their
game, and this could give them an advantage as well. Higher ranked players are less likely to win
on clay (1.5%) and grass (2.0%) compared to other surfaces. A higher ranked taller player was
3.1% more likely to win, and they state this as a significant variable. For each additional inch in
height above their opponent, the probability the higher ranked player wins increases by 0.5%.
Ovaska & Summell state that the ideal height for a professional tennis player (in terms of
probability of winning against other heights) is 6’3 to 6’4. These players are 9.0% more likely to
win compared to the shortest players.
Ovaska & Summell also find that if a player plays in his home country, the probability of
winning increases by 3.8% to 6.6%. This effect is even bigger (13.4%) in close Grand Slam
matches. They state that Australia, US, Sweden, Spain and Germany have a good combination of
factors to produce successful tennis players. Big groups of top players tend to come from a
relatively small group of countries, this might be due to highly effective systems of talentscouting
and training. They state that momentum is important in deciding matches; a player that previously
won the set is more likely to win the match.

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Ovaska & Summell also state that higher ranked players are less likely to win matches as
they age, but this result might be outdated. There has been a shift in professional tennis where
older players seem to perform better than younger players. For example, the ATP top 200 players
in July 2013 had no single player under the age of 20 but more than 50 players over the age of 30.
During the same season, there were four out of eight quarter finalists at the French Open that were
over the age of 30 (Tommy Haas, Tommy Robredo, David Ferrer and Roger Federer) The way to
the top and to compete successfully in Grand Slams is getting longer and players need more years
to develop mentally and physically in order to compete at the highest level. The longer the way to
the absolute top, the more resources and financing is needed in the beginning years of their careers
but the more years investors could receive possible dividends on their investments.
2.5 Crowdfunding
When looking at several ways to find funding for a career as a tennis player, investing
through crowdfunding may be a very good option. Mollick (2013) describes crowdfunding as a
way for founders of forprofit, artistic and cultural ventures to fund their efforts by drawing on
relatively small contributions from a relatively large number of individuals using the internet,
without standard financial intermediaries. Often these individuals will fund projects in return for
future products or equity. Mollick (2013) suggests that personal networks and underlying project
quality are associated with the success of crowdfunding efforts. Mollick also states that the area of
crowdfunding is understudied and that scholars know very little about the dynamics of successful
crowdfunding. Mollick examines all USbased projects on Kickstarter. Kickstarter is the largest
crowdfunding website which facilitated over $237 million in funding for 48,526 projects (Mollick,
2013). Mollick looks at the goals of founders and the goals of funders doing crowdfunding projects
and finds that crowdfunding increasingly seems to be a viable source for entrepreneurs; 45 of the

12
50 highest funded projects through 2012 on Kickstarter have turned into ongoing entrepreneurial
firms (Mollick, 2013). He also states that rules around crowdfunding for equity are evolving
rapidly, for example through the JOBS Act. Mollick, 2013 also believes that crowdfunding has
been used by founders to demonstrate a certain demand for a proposed product, which in turn can
lead to funding from more traditional sources. If a project lacks demand for investments during
early stages, it is more likely to lack demand later on and additional investments might be
unnecessary. Mollick, 2013 adds that crowdfunding can also be used to market certain projects and
to create interest during the early stages of development. Looking at the whole picture;
crowdfunding does not only attract financing, it also creates media attention, attracts ideas from
other developers, delivers marketing and thus offers a potential set of resources that are all
beneficial to founders (Mollick, 2013).
Mollick, 2013 states that there are four different ways in which individuals can fund
projects, however, these methods may overlap as projects develop down the road. The first method
places the funder in the position of a philanthropist, someone who does not expect a direct return
for a donation. The second model is a lending model, where funds are offered as a loan and
funders expect a rate of return on their investment in the project. The third and most common
model is called rewardbased crowdfunding. Funders receive a reward for financing a project,
which can include access to products, meeting the founders or being credited in a movie. The
fourth method, broadly legalized in the US by the Jumpstart Our Business Startups Act, treats
crowd funders as investors and gives them equity stakes or similar consideration in return for their
funding (Mollick, 2013). Mollick states that no matter what kind of model funders will use for
their crowdfunding, the one similar thought they all have is that the project they invest in is a
potential successful project.

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To determine what factors make a crowdfunding project successful, Mollick looks at the
following variables; project goal (is it realistic?), funding level (percentage of goal actually raised),
backers (number of funders), pledge/backer (average pledge by backer), category (Kickstarter
categorizes projects), Updates (Information posted by founders about their projects), comments
(funders can post comments about projects), duration (number of days for which a project starts
funding) and Facebook friends of founders (number of Facebook connections of each founder).
Mollick, 2013 found that successful fundraising projects have in common that they use quality
pitches and videos to promote their projects and that they provide rapid updates to their funders.
He also states that the size of a social network can influence the success of entrepreneurial
financing efforts, as larger social networks leads to more potential “friends and family” money.
Mollick finds that an increasing goal size is negatively associated with success and that being
promoted on the Kickstarter website is strongly associated with success. Mollick calculated that a
company founder with 10 Facebook friends would have a 9% chance of succeeding, one with 100
friends would have a 20% chance of success and one with 1000 friends would have 40% chance of
success. These findings are encouraging for the securitization of tennis players into a portfolio. A
portfolio of tennis players should have a large network of Facebook contacts and a management
agency could provide investors with a good pitch and a video to attract funds.
2.6 Lead to the research questions
The literature provided in this section provides good information on securitization, the
finance needs of a professional tennis player, how certain athletes have been sold on financial
markets, what makes projects successful for crowdfunding and what determines the winner of a
tennis match. The combination of these aspects of the literature provide a good understanding of

14
how investments could be made in the earnings of professional tennis players, or in athletes in
general.
In the case of securitizing athletes, players would be the assets that generate income and
that will be securitized by a company or other legal entity. This company will then securitize the
athletes and issue them as securities to investors. Investors will receive interests and dividends
when these players generate prize money and receive their full principal payment if they sell their
security. The main advantages for investors are the offer of an alternative investment, a chance for
high returns and the satisfaction from giving young athletes a chance to compete in their sports.
Logically, securitizing the income of a tennis player would fall under the category of future flow
securitization. This refers to securitizing receivables (prize money) which are to be generated in
the future. The obligation of future payments depends on the performance of the originator
(athlete). A company securitizing tennis players could analyze the future success of several players
trying to decrease risk and provide investors with the right legal documentation to support their
investments.
From an originator’s (athlete’s) perspective, the main advantages of this process are the
ability to raise funds at a relatively low cost, a diversification of funding sources and a chance to
finance the beginning of their professional athletic career. A company that provides management
services to these players can provide financial management, traveling schedules and other services
so that the athlete can focus on athletic performance. Even if these players are not ranked in the
top 125 for at least five years and thus won’t be eligible for the ATP pension program, they have
had extra financing to support their careers and to start developing their own pension. When trying
to find people to invest in tennis players, the idea of crowdfunding can be useful for a portfolio of

15
tennis players and for the future of the sport of tennis as more attention will be drawn to market the
sport.
2.7 Other business models
An American company called Fantex Inc. started a similar business model in 2013. The
company allows investors to buy shares of professional American Football players and the shares
are linked to the total value of the football player as a brand. The company has formed contracts
with football players Vernon Davis and Arian Foster and shares in these players are being traded
online. The players receive a lumpsum for entering in the contract and in exchange they give up a
percentage of their future income. Income includes all money received from activities related to
their brand as a football player; sponsorship deals, endorsement money and salary are part of the
package. Whether investors earn a profit depends on the future earnings of the player.
There is a big risk involved in this situation as football players frequently are injured and
investors don’t when a football player’s career will end. Forming portfolios of tennis players will
significantly reduce risk. Signing contracts with players that state a minimum amount of
tournaments that need to be played per year can guarantee investors a return on their investment
because players receive prize money even if they lose in the first round of a tournament. Players
could be signed to a sports agency that will create portfolios of players, attract investors and assure
optimal training facilities, tournament schedules and other tennis related issues for the player as
well as for the investor’s safety. A model can be created to estimate future earnings of a
professional tennis player and when shares are sold in the market, a fair market price will be
established.

16
2.8 Conclusion of literature
There is compelling evidence to indicate that securitizing tennis players into portfolios is
helpful for the players, investors and the sport of tennis. The remainder of this thesis focuses on
three areas to motivate the profitability of portfolios and the variables that will help determine the
optimal portfolios of players.
First, it is important to back test what previous earnings of tennis players have been and
thus earnings for investors could have been. Second, it is important to identify the relationship
between rankings and earnings and how this will change over time. With increasing prize money
there will always be players that are going to receive big checks of money, but there will also be
players that will not make it to the top. What is the relationship between ranking and earnings and
what will this relationship be in the future? This leads to the third question; what are potential
future earnings of tennis players and, knowing these earnings, what can potential profits for
investors be? These questions will be discussed in the next sections.
DATA AND METHODOLOGY
To address the questions mentioned, I use data on different tennis players (inactive and still
active) that shows what their yearly ATP prize money has been during their careers. I examine
players who were successful during their junior careers and use data on the yearend top 20 junior
players in 1998 and top 50 junior players in 2004 and 2005. I examine data on players that were
ranked between ATP500 and ATP600 and under the age of 26 in 1998. An additional dataset of
players I also examine is a sample randomly selected college players that have turned pro after
their college careers. I use data on these players because these groups of players are probably
upcoming professional players that might need financing during the beginning of their careers with
chances of significant prize money in the future. As Morales (2013) stated, many tennis

17
professionals that are playing in the lowertiered events such as challengers and futures financially
struggle. These players are usually ranked between ATP200 and ATP800 and most of them could
use some extra financing to support their careers. They would probably be willing to sell a piece of
their future earnings in exchange for a lump sum of money invested by investors. These
investments made by investors will yield immediate results because these players are already
active on the tour and have the possibility to earn prize money every week by playing tournaments.
Junior players that are ranked high in ITF junior rankings and consider a professional career might
have sponsorships already that will finance their careers, but other junior players will need
financing to become a professional. A shift in the professional tennis world is that more players
decide to play college tennis for several years before they turn pro. As the game gets more physical
and players peek at older ages, playing college tennis before turning pro is a great option for a lot
of tennis players. Following recent graduates during their professional tennis career is a good
addition to provide a realistic dataset of players who would be interested in selling a share of their
future income.
ATP prize money is public information thus the players career earnings can be collected. I
track their yearend rankings in singles and doubles and their yearly ATP prize money (singles and
doubles) during their whole career. Using this information, I back test how profitable these players
would have been for investors and what a yearly profit/loss would have been per portfolio. An
important thing to note is that all earnings are based on prize money earned during official ATP
Events. Other earnings such as club league money, sponsorship money and endorsement deals are
not included in the earnings of the player. Of course, these additional revenue streams would make
the portfolios more profitable for investors.

18
After looking at previous earnings and checking how profitable players could have been for
investors in the past, I examine the possibilities in the future and use data on prize money for the
four Grand Slam tennis tournaments to show how prize money has increased. An estimation of
future prize money for the Australian Open, French Open, Wimbledon and US Open is made.
Using this information, a large part of the income of professional tennis players can be estimated.
If potential prize money in the future is known, potential profits for investors can be calculated.
To analyze the relationship between ranking and earnings I create a function to estimate
earnings with ranking in singles and doubles. I also run a regression to see how ranking relates to
earnings for singles and for doubles separately, and how this result might change over time with
rankings getting more competitive and prize money increasing. I do this over the period of 1999
2013. The coefficients show how much earnings are impacted when a player moves up or moves
down one spot in the rankings. Using this regression analysis over time, I show how this
coefficient changes and how earnings are impacted by ranking during these years.
Putting the data of the five different portfolios together, this research is based on data
coming from 216 different players. On average I track yearly data on these players for 12.4 years
counting for a total of 2675 yearly measures of a players yearend singles rank, yearend doubles
rank, yearly prize money in singles and yearly prize money in doubles. All data is collected from
the ATP website (www.atpworldtour.com) and the ITF website (www.itftennis.com).
The formulas I use are:
TPMplayer = PMy1 + PMy2 + … + PMyn
In which TPMplayer stands for Total Prize Money for a certain player, PMy stands for the amount
of prize money earned in a certain year of their career. Adding up prize money earned in all years
http://www.atpworldtour.com/http://www.itftennis.com/

19
will give the prize money earned by one player. The results of this formula can be easily calculated
because all data on yearly prize money is public information.
TPMportfolio = TPMplayer1 + TPMplayer2 + … + TPMplayern
In which TPMportfolio is the Total Prize Money earned by a portfolio of players, consisting of
total prize money of all players in the portfolio. These portfolios can be standardized portfolios by
looking at a certain yearend ranking such as the ITF junior rankings, but it can also be a modified
portfolio in which I will randomly select different players from different years of birth and
different backgrounds in tennis.
PROFITinvestor = (PCTPMplayer1 – TIplayer) + (PCTPMplayer2 – TIplayer2) + … +
(PCTPMplayern – TIplayern)
In which PROFITinvestor stands for the profit an investor makes of a certain portfolio by looking
at PCTPM (agreed percentage of prize money) – TI (total investment) per player. Doing this for all
players in a portfolio will give the total profit/loss of a certain investor in the portfolio. This
formula is mostly significant if the investor has had his money invested in a portfolio for several
years and we want to know what his total profit on the portfolio is so far. However, investors will
particularly be interested to see what amount of their investment will be returned in what year. The
following formula will help determine this:
RETURNyn = (TPMPORTyn * PCTPM)
In which RETURNyn stands for total profit in year n, TPMPORTyn stands for total prize money
of portfolio in year n and PCTPM stands for agreed percentage of prize money that will be
returned to investors. RETURNyn can be calculated every year an investor owns a certain portfolio
and by adding RETURNyn up per year an investor gets a yearly update on the returns of his

20
portfolio. This way an investor will get an insight in what years he can expect most returns as
players are starting to win more prize money during their careers.
All the formulas above will be used to back test the profitability of certain portfolios that could
have been made in the past. Looking forward, we first have to estimate what future prize money
for players could be by looking at the growth rate of prize money for the grand slam tournaments
(Australian Open, French Open, Wimbledon and US Open). I estimate future prize money in each
tournament by taking the average percentage increase of prize money for each tournament for the
past three years. Doing so will give a growth rate which I apply to future editions of each Grand
Slam tournament. The formulas to do so are as follows:
• AUSPMyn = AUSPM2014 * (GAUSPM(2012+2013+2014) / 3) ^ n
• FREPMyn = FREPM2014 * (GFREPM(2012+2013+2014) / 3) ^ n
• WIMPMyn = WIMPM2014 * (GWIMPM(2012+2013+2014) / 3) ^ n
• USPMyn = USPM2014 * (GUSPM(2012+2013+2014) / 3) ^ n
In which AUS stands for Australian Open, FRE stands for French Open, WIM stands for
Wimbledon, US stands for US Open, PMyn stands for Prize Money in year n and G stands for
growth rate in prize money. Using these formulas for all the Grand Slam tournaments, future prize
money of editions of these tournaments can be estimated.
PMyn = F (rankSyn) + (rankDyn)
To estimate a function in which Yearly Prize Money for a certain player is a function of his
rankSyn (endofyear n ranking in singles) + rankDyn (endofyear n ranking in doubles). By
running a regression of ranking in singles and doubles (X variables) on prize money (Y variable) I
estimate how a certain ATP ranking effects the amount of prize money earned in a year. By doing

21
this for several different years, I estimate how a certain ranking earns more or less money in a later
year compared to previous years. I estimate how prize money earned through a certain ranking in
singles is different from prize money earned through this same certain ranking in doubles. Graphs
show the relationship between prize money earned and ranking, for singles as well as for doubles.
As Morales (2013) stated, a player who plays all Grand Slam tournaments in one year will
have about 40% of his prize money coming from these four Grand Slam tournaments. When prize
money for future Grand Slam tournaments is estimated, we can estimate future income of players
and especially of players who will play in all the Grand Slam tournaments. The top 128 players
compete in Grand Slam tournaments and thus are the ones that receive the increases in prize
money every year. Using this information, I examine the increase in pay for players ranked inside
the Top 128: The formula for this will be:
• FPMyn = PMyn1 + G*0.4PMyn1
In which FPMyn is Future Prize Money in year n, G is the average growth rate of prize money in
the four Grand Slam tournaments and PMyn1 is total prize money earned in the previous year. We
use the number 0.4 because 40% of yearly prize money is explained by prize money earned in
Grand Slam tournaments (Morales, 2013). Once I calculate the prize money increase for players
ranked inside the top128 I calculate the difference between the average income of these players
and the average income of players ranked outside the top 128.
RESULTS
4.1 Back testing portfolios
To address the first and second research questions in this research, (“what would profits
have been for investors if they would have invested in certain portfolios of tennis players in the

22
past?” and “what players should be selected for the portfolios?”), I back test what previous
earnings of players in the five portfolios have been and what earnings for investors could have
been. The analysis of the five different portfolios (ITF Juniors 1998, 2004, 2005, ATP500600
U26, College 20) are performed using a few standard assumptions. The basic assumption is that
players have received a sum of money in the form of an investment at the beginning of their
professional careers, to finance part of the early stages of their careers. In return for this
investment, a player will give back a certain percentage of his prize money to the investor. All
calculations and results of these five portfolios are generated to create an understanding of what
could have happened in the past and how this could be useful in the future. The idea is that once a
portfolio of players is established, investments will be attracted and given out equally to all players
in the portfolio at the beginning of their careers or at the beginning of period. The second
assumption is that an investor does not know which players are going to make more money than
others and to diversify risk they give the same amount of investment to each player in the
portfolio. Accordingly, each player in a portfolio will give back the same percentage of their prize
money to the investors and this percentage will remain unchanged throughout their careers. For
comparison purposes, I assume that every player in these portfolios would have taken an upfront
investment of $50,000 dollars and that the dividend payout to investors would be 10% of their
career ATP prize money. I choose $50,000 dollars as the initial investment per player because, if a
player designs an efficient travelling schedule, with that money they would be able to cover the
traveling costs for approximately two years of playing professional tennis. I choose a dividend
payout of 10% of ATP prize money because I believe that being able to keep 90% of prize money
gives a player enough motivation to keep competing. If for example a player would have to payout
25% of his earnings as dividends to investors, the motivation to play a next tournament will

23
decrease since only $0,75 of every extra dollar earned will be for the player. An investor would
make a profit on a portfolio if the average earnings by the players in the portfolio is higher than
$500,000. I believe this is a fair amount for the players and the investors. Obviously, when
creating portfolios to attract investors in the future, these terms and conditions should be clearly
stated in legal contracts and these contracts could differ from player to player. Also, I assume that
all players in a certain portfolio would agree to the terms of such contract, this could be different
when these portfolios are being created in the future.
4.1.1 Portfolio I: Top 20 ITF Juniors 1998
The first portfolio I analyze is the top 20 ITF junior players from 1998. Table 1 shows
information on these 20 players including their names, nationalities, year of birth, current rank,
highest rank, year they turned pro and career prize money.
Table 1 Portfolio Top 20 ITF Juniors 1998
Since these players were ranked in the top 20 ITF juniors by the end of 1998, I used data on
their endofyear ATP rankings in singles and doubles and their yearly prize money starting in
1999. I assume that the portfolio would have attracted all the necessary investments ($50,000 per

24
player) by the end of 1998, and the first year this portfolio would have hit the market would be
1999. The idea of the portfolio is that it doesn’t matter which player will earn the prize money
because they all belong to the same portfolio which in turn will result into profits for investors who
have invested in this portfolio. As stated before, all players have received the same investment and
will pay back the same percentage of their prize money. Table 2 shows the amount of prize money
earned per year (TPMportfolio/year) by this portfolio and the amount of prize money in total
earned by this portfolio (TPMportfolio/year). As is displayed at the bottom, in the period of 1999
2013 (15 years) this portfolio has earned $125,961,336.00 in prize money and 10 out of 20 players
were still active by the end of 2013. During 2007 (year 9), the highest amount of prize money was
earned totaling $14,602,967.00
Table 2 Earnings Portfolio Top 20 ITF Juniors 1998
If we look at how this portfolio could have been profitable for investors, we take 10% of
total prize money earned by this portfolio and subtract it with the initial investment that has been
made in 1999. Over time, profits will always rise because every dollar of prize money earned by
players in the portfolio will have an impact on dividends to investors. I examined how profitable

25
this portfolio would have been to investors by the end of every year from 1999 until 2013. Figure 1
shows the results.
Figure 1 Profit and return for investors in Portfolio I
As shown in graph 1, investors would have had a loss until year 5. Year 5 would have been
the first year in which investors had a profit and the profit would rise after year 5. By the end of
2013 (year 15 of the portfolio) their profit would have been $11,596,133.60 which means they
would have had nearly 1200% return on their investment. Logically, different investment amounts
and percentages of dividend payouts can influence these results.
4.1.2 Portfolio II: ATP500600 under the age of 26 in 1998
The next dataset I analyze is the dataset consisting of all players aged 25 and below and
who were ranked between ATP500 and ATP600 by the end of 1998. In total, there were 76 players
that met these requirements and I collected data on their yearend rankings in singles and doubles
and their yearly prize money from 1999 until 2013. Table 3 shows information on these 76 players.
200,0000%
0,0000%
200,0000%
400,0000%
600,0000%
800,0000%
1000,0000%
1200,0000%
1400,0000%
$(2.000.000,00)
$
$2.000.000,00
$4.000.000,00
$6.000.000,00
$8.000.000,00
$10.000.000,00
$12.000.000,00
$14.000.000,00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Profit %Return
Profit Investors % Return

26
It shows their ATP rankings by the end of 1998, their names and the amount of prize money they
have earned in the period 19992013.
Table 3 Portfolio ATP500600 under the age of 26 in 1998
As was the case for the portfolio of the ITF juniors top 20 in 1998, all prize money coming
from these players adds up to the same portfolio which will be invested in by the investors. This
dataset can be a nice comparison to the previous dataset because both portfolios are assumed to
“hit the market” in 1999. Table 4 shows information on yearly prize money by this portfolio.
Table 4 Earnings portfolio ATP500600 under the age of 26 in 1998

27
Total prize money earned by this portfolio in 15 years was $37,474,734.00 and 8 out of the
76 players were still active by the end of 2013. An interesting fact about this portfolio is that the
biggest part of prize money earned came from players who were 16 (Tommy Robredo) or 17
(Feliciano Lopez, Filippo Volandri, Irakli Labadze) at the end of 1998. Figure 2 shows how prize
money earned by this portfolio is divided by age groups in 1998. Note that $24,258,405.00 was
earned by these four players. Taking these facts into consideration, it seems important to look at
the actual age of a player at a certain time you look at his rankings. Younger players with higher
rankings have more potential to earn prize money than older players with the same rankings.
Figure 2 Earnings by age group Portfolio II
Similar to the calculation for the previous portfolio, I calculate what profits would have
been for investors if they would have invested $50,000 per player by the end of 1998 to receive
10% of future prize money of this portfolio. Figure 3 shows the results.
$
$2.000.000,00
$4.000.000,00
$6.000.000,00
$8.000.000,00
$10.000.000,00
$12.000.000,00
$14.000.000,00
$16.000.000,00
25 24 23 22 21 20 19 18 17 16TP
M 1
99
92
01
3
AGE IN 1998
Prize Money earned 19992013 by age group

28
Figure 3 Profit and return for investors in Portfolio II
After the investment would have been out for 15 years, investors would almost have earned
their money back (a loss of $52,526.60) and they probably would have ended up with a profit in
2014 or 2015. This portfolio might not seem very profitable in the first place using these numbers.
However, the average career outlook for these players is probably not as hopeful as was the case
for ITF players and they would probably have taken a lower investment for a higher amount of
prize money returned to investors. Also, there is no information on the reasons why some of these
players quit playing so early after 1998. If a company would create a portfolio of players ranked
between ATP500600 they obviously will test the motivation of these players to keep playing
professional for a few more years. Players will also be more likely to play professional tennis at
later ages then they were back in 1998 because the game is getting more physical and players need
more time to develop their physical strength and their game to reach the top. I assume that players
in this portfolio would be willing to take a deal to receive $30,000 in exchange for 15% of their
future prize money. Figure 4 shows how in this situation profits would be $3,341,210.10 for
120,0000%
100,0000%
80,0000%
60,0000%
40,0000%
20,0000%
0,0000%
$(4.000.000,00)
$(3.500.000,00)
$(3.000.000,00)
$(2.500.000,00)
$(2.000.000,00)
$(1.500.000,00)
$(1.000.000,00)
$(500.000,00)
$
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Profit %Return
Profit Investors % Return

29
investors after year 15. It also shows how important the initial investment and percentage return
policies are for portfolios and how these terms can influence the situation for investors drastically.
Figure 4 Profit and return for investors in Portfolio II with new conditions
4.1.3 Portfolio III: Top 50 ITF Juniors 2004
The next portfolio I analyze is the ITF Juniors top 50 in 2004. To create a larger pool of
players, I decided to use the top 50 players instead of the top 20 players as I did in 1998. Using a
portfolio consisting of 50 players means that the portfolio is more diversified and that there is a
larger pool of players to invest in. Table 5 shows information on which players are included in this
portfolio, what their ITF Junior rank was in 2004 and what their career prize money has been until
July of 2014.
150,0000%
100,0000%
50,0000%
0,0000%
50,0000%
100,0000%
150,0000%
200,0000%
$(3.000.000,00)
$(2.000.000,00)
$(1.000.000,00)
$
$1.000.000,00
$2.000.000,00
$3.000.000,00
$4.000.000,00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Profit %Return
Profit Investors % Return

30
Table 5 Portfolio Top 50 ITF Juniors 2004
Again, all these players would have received the same amount of investment by the end of
2004 assuming they would start playing professional in 2005. They would also start paying out
dividends to investors at that point. The results of this portfolio where all players have received
$50,000 and will pay back 10% of their prize money are shown below in table 6 and figure 5.
Table 6 Earnings portfolio Top 50 ITF Juniors 2004

31
Figure 5 Profit and return for investors in Portfolio III
As the figure and table show, in the period 20052013 this portfolio has earned
$66,146,957.00. For investors this portfolio would have been profitable after 5 years, in 2013 (year
9) they would have a profit of $4,135,163.30 which is a profit of 165%. It is notable that the yearly
total prize money of this portfolio has gone up every year since this portfolio would have hit the
market. The only outlier in this statement is the year 2009; the portfolio earned more money in
2009 than it did in 2010, 2011 or 2012. 2009 was the year that Argentinian player Juan Martin Del
Potro won the US Open and earned a lot of prize money for this portfolio. With only 9 years
played and 33 players active in 2014, this portfolio is likely to increase its profits for investors in
the future. Players will have several years left to play and to win prize money for this portfolio.
4.1.4 Portfolio IV: Top 50 ITF Juniors 2005
To create a portfolio that can be compared to the ITF top 50 in 2004 I created a portfolio
consisting of the top 50 ITF Juniors players from 2005. Table 7 shows information on these
players, including their ITF ranking, name and total prize money earned. An important thing to
150,0000%
100,0000%
50,0000%
0,0000%
50,0000%
100,0000%
150,0000%
200,0000%
$(3.000.000,00)
$(2.000.000,00)
$(1.000.000,00)
$
$1.000.000,00
$2.000.000,00
$3.000.000,00
$4.000.000,00
$5.000.000,00
1 2 3 4 5 6 7 8 9
Profit %Return
Profit Investors %Return

32
note is that some of these players are also in the calculations of the previous portfolio (ITF Top 50
in 2004), this means that they have been ranked in the top 50 juniors for two consecutive years.
Table 7 Portfolio Top 50 ITF Juniors 2005
The statistics show that this group of players has not been as successful as the ITF top 50 in
2004, at least until the end of 2013. The total earnings of this portfolio is lower than the previous
portfolio and less players from this portfolio were active during the first few years after their junior
careers. Table 8 and figure 6 will show the results of a $50,000 investment with a 10% of prize
money payout as dividends.

33
Table 8 Earnings portfolio Top 50 ITF Juniors 2005
Figure 6 Profit and return for investors in Portfolio IV
As shown in Table 8, total prize money earned by this portfolio after 8 years is
$33,370,711.00 and after 8 years investors would have had a profit of $860,163.40. From all 50
players in this portfolio, 32 are still active by the end of 2013. The portfolio of ITF juniors 2004
generated over $52 million dollars in profit after 8 years and created a higher profit for investors.
An important thing to note is that this Top 50 ITF juniors 2005 portfolio is likely to generate more
profits for investors in the future. The average age of the players in this portfolio was 26 by the end
of 2013 and, given the fact that tennis players peek at older ages compared to previous generations,
there are good chances that these players will earn more prize money in later stages of their
120,0000%
100,0000%
80,0000%
60,0000%
40,0000%
20,0000%
0,0000%
20,0000%
40,0000%
60,0000%
$(3.000.000,00)
$(2.500.000,00)
$(2.000.000,00)
$(1.500.000,00)
$(1.000.000,00)
$(500.000,00)
$
$500.000,00
$1.000.000,00
$1.500.000,00
1 2 3 4 5 6 7 8
Profit %Return
Profit Investors %Return

34
careers. An example is Croatian tennis player Marin Cilic, who won the US Open in 2014 and won
nearly $3 million dollars by winning it. These earnings are not impacting the model used in this
study because it only accounts for earnings until the end of 2013.
4.1.5 Portfolio V: 20 College Players Who Turned Pro
The last portfolio I analyze is a portfolio consisting of twenty randomly selected college
players that are active professional tennis players as of September 2014. College tennis is
becoming an important and competitive step in the careers of many professional tennis players and
an increasing rate of college tennis players is turning pro after their collegiate career. The intense
competition, high quality American facilities and the funds that are reserved for college tennis at
the big universities make these college tennis programs very attractive for junior players. The fact
that players can earn a college degree while they are training intensively with a possible future
professional career is an opportunity that many athletes are willing to take. I picked out different
players from different universities. Many of these college players just turned pro in 2012 and 2013,
which is why I also analyze their earnings until August 2014. Prize money earned by these players
in 2014 will also be taken into account when calculating profits for investors. Table 9 shows the
names, the college at which they played, the year they turned pro and the career prize money of the
twenty college players in this portfolio.

35
Table 9 Portfolio 20 ex college players who turned pro
Just like other portfolios I assume that all these players receive an investment of $50,000 in
return for 10% of their career prize money. This investment is made in the year they finished their
college career and decided to start playing professional tennis. Since this portfolio consists of
players turning pro in different years, the investment is spread out over several years and according
to when these players finished their collegiate careers. Table 10 shows information on how
profitable this portfolio would have been and what amount of prize money the portfolio would
have earned.
Table 10 Earnings portfolio 20 ex college players who turned pro

36
An important thing to note is that the total investment is growing when more players
became active as a professional. The total prize money earned by the portfolio after 12 years is
$20,464,238.00 and almost 75% of that amount is earned by John Isner, Kevin Anderson and
Benjamin Becker. These three players have been very successful college players and professional
players and turned pro a lot earlier than most of the players in this portfolio. Figure 7 shows the
profit and % return investors would have made on this portfolio.
Figure 7 Profit and return for investors in Portfolio V
Since there were not many active players during the first six years of this portfolio, the
losses stayed relatively low and constant. However, when more players were added to the portfolio
the prize money earned grew a lot and especially in year 11 (2013) and year 12 (2014) prize
money has increased very rapidly. The fact that even the college players that just turned
professional are making this amount of prize money in such short periods of time, suggests that
100,0000%
50,0000%
0,0000%
50,0000%
100,0000%
150,0000%
$(200.000,00)
$
$200.000,00
$400.000,00
$600.000,00
$800.000,00
$1.000.000,00
$1.200.000,00
1 2 3 4 5 6 7 8 9 10 11 12
Profit %Return
Profit Investors %Return

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profits for investors are likely to go up in the near future. The total profit on this portfolio in year
12 would be $1,096,423.80
4.1.6 Comparing Portfolios
When comparing all five portfolios and looking at which one would have been the best
option for investors, it would not be fair to just look at total prize money earned right now. Some
portfolios have had more years to win prize money than others and some portfolios consist of more
players than others. It could however be useful to look at how many years a portfolio needed to
become profitable for investors when invested $50,000 per player for a return of 10% of prize
money. Portfolio I (ITF Top 20 1998) was profitable starting in year 5, Portfolio II (ATP500600
age

38
Portfolio II seems to be the least profitable portfolio but it does have the most players and
thus was the most expensive portfolio of them all. If this portfolio would have consisted of players
receiving $30,000 in return for 15% of their prize money, the portfolio would have been profitable
in year 8. There are sufficient reasons to believe that these players would have taken this second
deal. If the creator of a portfolio carefully picks players that are under the age of 25 and ranked
between ATP300 and ATP800, it might be a very good and profitable portfolio with quick results.
These players need financing to give their careers a boost and if they have the potential to reach
the top 100 in the world they have the potential to earn lots of prize money. Looking at portfolio
III and IV, it seems like older generations of Top 50 ITF players struggle more to earn a lot of
prize money on the ATP tour. This could be due to the fact that the ATP Tour is getting more
competitive and players are getting older before they reach the top of their careers. The game is
getting more physical and players need more years to develop their bodies and to develop their
mental strength in order to compete at the highest level. Portfolio V and other college players have
had the chance to develop these aspects of their game during their collegiate career and are often
very strong physically and mentally before they start a professional career. This can be seen by
looking at some of the college players that just turned pro but immediately earn a lot of prize
money in the beginning of their professional career. The future of this portfolio might be very
profitable for investors.
4.2 The relationship between ranking and earnings in men’s professional tennis
To address the third research question (What is the relationship between rankings and prize
money of men’s professional tennis players?) I analyze the relationship between ranking and
earnings in men’s professional tennis. The goal is to create a function of PMyn = F (rankSyn) +
(rankDyn) in which PMyn stands for prize money in year n and rankSyn and rankDyn stand for

39
endofyear n ranking in singles and endofyear n ranking in doubles respectively. In order to
create this function I take the total amount of 2675 yearly observation and I collect only those
observation in which a certain player had a singles ranking, a doubles ranking and earned at least
$90 in singles and $90 in doubles. The reason a player needs a ranking in singles and doubles is
that the results of a regression would be less accurate if we add the players with no ranking in the
model. The lowest amount of prize money a player can get at an official ATP event is $90 dollars,
which is why every player in the model needs to have earned at least this amount. After taking out
all yearly observations of players that had no ranking in singles or doubles and/or had earned $0 in
a given year, I have 1410 observations left over to use in this model. To start analyzing the
relationship between ranking and men’s earnings I will break down the formula into two parts; the
relationship between ranking and earnings in singles and the relationship between ranking and
earnings in doubles.
4.2.1 The relationship between singles ranking and earnings in singles
Figure 8 shows a plot of the 1410 observations I use in my model to estimate singles
earnings by singles rank. Earnings in $ are shown next to the Y axis and ranking is displayed on
the X axis. The graph clearly is extremely exponential and there is basically no way to distinguish
the data points. Extreme outliers such as the top data point (Roger Federer in 2007 earned more
than $10 million and was ranked number 1) are examples of the fact that the top players earn
significantly more prize money than the lower ranked players.

40
Figure 8 The relationship between singles rank and earnings
To be able to analyze this relationship, I first create a log linear model of this graph. A log
linear model takes out the exponential factor and molds it into a linear function. I do this by taking
the log of every Y variable (earnings) in the model while keeping the X variable (ranking) the
same. The relationship between singles rank and the log of earnings is shown in figure 9.
Figure 9 Log linear model of singles prize money and singles ranking
$
$1.000.000,00
$2.000.000,00
$3.000.000,00
$4.000.000,00
$5.000.000,00
$6.000.000,00
$7.000.000,00
$8.000.000,00
$9.000.000,00
$10.000.000,00
$11.000.000,00
0200400600800100012001400
Relationship between singles rank and earnings

41
The log version of the graph shows a more linear model in which the log of earnings is
displayed on the Yaxis and singles rank is displayed on the Xaxis. To create a function of this
model I regress endofyear singles rank on the log of endofyear singles earnings. The key results
of this regression are shown in table 11.
Table 11 Results of regression ranking on log singles prize money
R Square 0.766611
Adjusted R Square 0.766444
Intercept coefficient 12.09876
Singles Rank coefficient 0.00489
Tstat singles rank 67.8127
Pvalue Singles Rank 0.0000
Singles Rank LB 99.0% 0.005078
Singles Rank UB 99.0% 0.004706
As table 11 shows, the effect of singles rank on log earnings is of the expected sign and
significant at the 1% level, the coefficient of the intercept is 12,09876 and the coefficient of singles
rank is 0,00489. This means that for every one unit change in ranking, log earnings would
decrease by 0,00489 on average. Taking this information into account I can set up a formula to
estimate log earnings as a function of singles ranking; Log earnings year n = 12,0990,00489X in
which X is singles rank in year n. This result is not yet what I’m looking for because I want to
know the effect of ranking on actual prize money and not on the log of prize money. In order to
create that function I convert this function into a regular function;
Earnings year n = 179692*e(0.0049X). In which X is singles rank in year n.

42
This formula is not perfect and cannot accurately explain singles earnings by ranking for
every player, but it is the closest fitting line that on average explains earnings the best as a function
of rank. Adjusted R squared for this model is 0.766 which means that almost 77% of singles
earnings in year n can be explained by singles rank in year n.
4.2.2 The relationship between doubles ranking and earnings in doubles
Like the analysis for singles, I will also analyze the relationship between ranking and
earnings in doubles in a given year. The graph that showed the singles data points was extremely
exponential. For doubles, the graph is still exponential but it’s definitely not as clear as the singles
graph. Figure 10 shows the relationship between doubles rank and doubles’ earnings, only
including yearly data points.
Figure 10 the relationship between doubles rank and doubles earnings
For the analysis of the relationship of doubles rank and doubles’ earnings I also create a log
version of the graph. The effect of the log version takes out the exponential factor and gives us a

43
chance to estimate a linear function through the set of data points. Figure 11 shows the graph of the
log linear model.
Figure 11 Log linear model of doubles prize money and doubles ranking
I now perform a regression of doubles ranking on log doubles earnings and it yields the
following results.
Table 12 Results of regression ranking on log doubles prize money
R Square 0.741103
Adjusted R Square 0.740916
Intercept coefficient 10.22635
Doubles Rank coefficient 0.00348
Tstat doubles rank 62.9879
Pvalue doubles Rank 0.0000
Doubles Rank LB 99.0% 0.00362
Doubles Rank UB 99.0% 0.00334

44
As shown in table 12, this model is significant at the 1% level with an Adjusted R Square
of 0.74. The intercept coefficient is 10.23 and the doubles rank coefficient is 0,003 which means
that for every one unit increase in doubles rank, log earnings of doubles will decrease by 0.003 on
average. Taking this information into account I can create a function to estimate log earnings as a
function of doubles rank; Log earnings in doubles year n = 10.23 – 0.003X in which X is doubles
rank in year n. As was the case for the singles model, I will now convert this function into a
function to estimate regular doubles earnings and not the log of doubles earnings. The function to
estimate regular doubles is;
Doubles earnings = 27621.42*e(0.00348X)
Again, this function is not perfect and it does not accurately explain the doubles earnings of
every player, but it is the closest fitting line through all data points and