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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: ATP500-600 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

  • iii

    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/

  • iv

    LIST OF TABLES

    Table Page

    1 Portfolio Top 20 ITF Juniors 1998 ................................................................................... 23

    2 Earnings Portfolio Top 20 ITF Juniors 1998 .................................................................... 24

    3 Portfolio ATP500-600 under the age of 26 in 1998 ........................................................ 26

    4 Earnings portfolio ATP500-600 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 1999-2013 .................................. 48

    15 Singles coefficients of regressions over the years 1999-2013 .......................................... 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.

  • 2

    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:

  • 3

    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.

  • 4

    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

    coupon-payment because they have a lower risk-investment.

    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 off-balance 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

  • 5

    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

  • 6

    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 year-end 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 buy-ins at tournaments in exchange for a percentage of the

    prize money and investors can invest in thoroughbred racehorses. (Passy, 2014)

  • 7

    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)

  • 8

    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 (2005-2008) 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 surface-biased 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. Left-handed lower-ranked players are more likely to

  • 9

    defeat right-handed, higher-ranked players. A higher-ranked player has 5.9% less chance of

    winning when they face a left-handed player. Corral & Rodriguez also state that models that use

    players’ past performances outperform those that do not. They used an out-of-sample (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, match-specific 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 talent-scouting

    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.

  • 11

    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 for-profit, 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 US-based 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 reward-based 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.

  • 13

    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 lump-sum 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 year-end 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 lower-tiered 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 year-end 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 year-end singles rank, year-end 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 year-end 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 (end-of-year n ranking in singles) + rankDyn (end-of-year 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 = PMyn-1 + G*0.4PMyn-1

    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 PMyn-1 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, ATP500-600

    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 end-of-year 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: ATP500-600 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 year-end 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 1999-2013.

    Table 3 Portfolio ATP500-600 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 ATP500-600 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

    9-2

    01

    3

    AGE IN 1998

    Prize Money earned 1999-2013 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 ATP500-600 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 2005-2013 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%

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    -20,0000%

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    $(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%

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    $(200.000,00)

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    1 2 3 4 5 6 7 8 9 10 11 12

    Profit %Return

    Profit Investors %Return

  • 37

    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 (ATP500-600

    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

    end-of-year n ranking in singles and end-of-year 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

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    $8.000.000,00

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    $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 Y-axis and singles rank is displayed on the X-axis. To create a function of this

    model I regress end-of-year singles rank on the log of end-of-year 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

    T-stat singles rank -67.8127

    P-value 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 = 12,099-0,00489X 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

    T-stat doubles rank -62.9879

    P-value 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