Annals of the University of North Carolina Wilmington International Masters of Business Administration http://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
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
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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.
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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 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
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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
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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. 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 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
4 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 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)
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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)
7 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
8 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
9 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.
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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 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
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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.
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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
13 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
14 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.
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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
16 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.
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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
18 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
19 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
20 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
21 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
22 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
23 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
24 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
Profit %Return $14.000.000,00 1400,0000%
$12.000.000,00 1200,0000%
$10.000.000,00 1000,0000%
$8.000.000,00 800,0000%
$6.000.000,00 600,0000%
$4.000.000,00 400,0000%
$2.000.000,00 200,0000%
$- 0,0000% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 $(2.000.000,00) -200,0000%
Profit Investors % Return
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.
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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
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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
Prize Money earned 1999-2013 by age group
$16.000.000,00 $14.000.000,00
$12.000.000,00 2013
- $10.000.000,00
$8.000.000,00
$6.000.000,00 $4.000.000,00 $2.000.000,00 $-
TPM 1999 TPM 25 24 23 22 21 20 19 18 17 16 AGE IN 1998
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.
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Figure 3 Profit and return for investors in Portfolio II
Profit %Return $- 0,0000% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 $(500.000,00) -20,0000% $(1.000.000,00) -40,0000% $(1.500.000,00)
$(2.000.000,00) -60,0000%
$(2.500.000,00) -80,0000% $(3.000.000,00) -100,0000% $(3.500.000,00)
$(4.000.000,00) -120,0000%
Profit Investors % Return
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
28 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
Profit %Return $4.000.000,00 200,0000%
$3.000.000,00 150,0000%
$2.000.000,00 100,0000%
$1.000.000,00 50,0000%
$- 0,0000% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 $(1.000.000,00) -50,0000%
$(2.000.000,00) -100,0000%
$(3.000.000,00) -150,0000%
Profit Investors % Return
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.
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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
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Figure 5 Profit and return for investors in Portfolio III
Profit %Return $5.000.000,00 200,0000%
$4.000.000,00 150,0000% $3.000.000,00 100,0000% $2.000.000,00 50,0000% $1.000.000,00 0,0000% $- 1 2 3 4 5 6 7 8 9 -50,0000% $(1.000.000,00)
$(2.000.000,00) -100,0000%
$(3.000.000,00) -150,0000%
Profit Investors %Return
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
31 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.
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Table 8 Earnings portfolio Top 50 ITF Juniors 2005
Figure 6 Profit and return for investors in Portfolio IV
Profit %Return $1.500.000,00 60,0000% $1.000.000,00 40,0000% $500.000,00 20,0000% $- 0,0000% $(500.000,00) 1 2 3 4 5 6 7 8 -20,0000% $(1.000.000,00) -40,0000% $(1.500.000,00) -60,0000% $(2.000.000,00) -80,0000% $(2.500.000,00) -100,0000% $(3.000.000,00) -120,0000%
Profit Investors %Return
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
33 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.
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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
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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
Profit %Return $1.200.000,00 150,0000%
$1.000.000,00 100,0000% $800.000,00
50,0000% $600.000,00
$400.000,00 0,0000%
$200.000,00 -50,0000% $- 1 2 3 4 5 6 7 8 9 10 11 12 $(200.000,00) -100,0000%
Profit Investors %Return
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
36 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<26 1998) was not profitable during the first 15 years, Portfolio III (ITF Top 50 2004) was profitable in year 6 and Portfolio IV (ITF Top 50 2005) and Portfolio V (20 College Players) were profitable in year 7.
Looking at this information, Portfolio I seems to be the most profitable portfolio of them all. However, this portfolio only consists of the top 20 players so the result might be a little inflated compared to portfolios using more than 20 players. The chance of the top 20 players earning prize money before players ranked between 20-50 needs to be taken into consideration. Portfolio I also seems to have one big outlier compared to all the other players in this study; Roger Federer. Roger
Federer is the best earning tennis player ever and has earned over $83,000,000 during his career.
Out of all players in this study, Juan Martin Del Potro is the second best earner with a little over
$15,000,000 of prize money earned. Of course the results of a portfolio consisting of Roger
Federer would be very good for investors, but the chance that Roger Federer would have entered into a deal like this might not be very high.
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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
38 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.
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Figure 8 The relationship between singles rank and earnings
$11.000.000,00 Relationship between singles rank and earnings $10.000.000,00 $9.000.000,00
$8.000.000,00
$7.000.000,00
$6.000.000,00
$5.000.000,00
$4.000.000,00
$3.000.000,00
$2.000.000,00
$1.000.000,00
$- 1400 1200 1000 800 600 400 200 0
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
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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.
41
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
42 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
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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 on average it explains doubles earnings the best.
4.2.3 The relationship between earnings and rank in singles and doubles
Of course it is interesting to see what the relationship is between singles rank and singles earnings and what the relationship is between doubles rank and doubles earnings. However, to fully understand the earnings of a tennis player in a given year we need to look at the combination of singles rank and doubles rank. A player can earn money by playing singles tournaments and by playing doubles tournaments. A player will also have two separate rankings for singles and doubles and in the following model I will combine these rankings to estimate total earnings of a given player in a given year. For illustration purposes, Figure 12 shows all data points of players that have earned $250,000 dollars and their singles and doubles ranks are shown accordingly. The blue dots are singles data points and the orange dots are doubles data points.
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Figure 12 Relationship between singles rank, doubles rank and total prize money earned
As figure 12 shows, the singles data points are a lot more clustered than the doubles data points. This can be due to the fact that singles rankings are more competitive and that tournament payouts are distributed less equally. It mostly shows that the effect of singles rank on total prize money earned is more significant than the effect of doubles rank on prize money earned. A player with a lower ranking in doubles can still earn a lot of prize money because his singles ranking is high which leads to high total earnings. Vice versa, a player with a lower singles rank has less chance to make up for this by earning a lot of money through doubles tournaments. I will now regress X variables (singles rank and doubles rank in year n) on the log of Y variable (log total prize money earned in year n) to create the function log PMyn = F (rankSyn) + (rankDyn). For this regression I will use all available data points and not just the data points that are shown in graph
11. In this function PMyn stands for prize money in year n, rankSyn and rankDyn respectively stand for singles rank in year n and doubles rank in year n. According to graph 11 rank in singles should have a more significant impact on total earnings than rank in doubles does. The results of the regression are shown in table 13.
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Table 13 Results of regression singles and doubles rank on total prize money
Singles Rank Doubles Rank
R Square 0.754725
Adjusted R Square 0.754376
Coefficient 12.09876 -0.00367 -0.00111
T Stat -38.2527 -12.9826
P-value 4.9E-220 1.75E-36
LB 99.0% -0.00392 -0.00134
UB 99.0% -0.00343 -0.00898
Table 13 shows that the results are of the expected sign. Singles rank as well as doubles rank are both significant at the 1% level and have a significant impact on total earnings in a given year. Both of the P-values are extremely low, the singles rank coefficient is -0,003 and the doubles rank coefficient is -0.001. This means that for every one unit increase in singles rank, the log of total earnings goes down by 0.003 and for every one unit increase in doubles rank, the log of total earnings goes down by 0.001. As expected, singles rank has a more significant impact on total prize money earned than doubles rank does. Taking these numbers to create a function it would look like this: Log total earnings year n = 12.47 – 0.003rankSyn – 0.001rankDyn. This function however is not sufficient for this research because we are interest in finding the impact of singles and doubles rank on actual prize money earned. This is why I convert the log function back to create the following function;
PMyn = 260078*e(-0.00367rankSyn)*e(-0.00111rankDyn)
This function is on average the best function in explaining total prize money in year n as a function of singles rank in year n and doubles rank in year n over the period of 1999-2013.
46
4.2.4 The change in impact of rank on total prize money over time
The function to estimate total earnings in a given year by using singles rank and doubles rank is now known. This function is the most accurate when estimating the total earnings of tennis players over the period of 1999-2013. This model does not diversify between different years and suggests that ranking in 1999 would have the same impact on earnings as ranking in 2013 would have. In this section of the research I examine how the impact of ranking on total prize money in a given year changes over time. In other words, I examine what the impacts are on total prize money in a given year when a player moves up or down a spot in the rankings, and examine how this impact changes over time. In order to examine this change of impact, I separate all the data points of players with an active singles and doubles ranking and yearly earnings in singles and in doubles of at least $90 into yearly categories. I divide the data into sections per year and run regressions on those yearly categories to examine the difference in the coefficients for every year during the period of 1999-2013. Table 14 shows the results of these separate regressions.
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Table 14 Regressions of ranking on prize money over the years 1999-2013
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept 40261,55899 7227,246954 5,570801613 3,47882E-07 25873,21558 54649,90241 21179,16966 59343,94833 1999 singles rank -27,71032679 10,77151746 -2,572555529 0,011992989 -49,15477075 -6,265882831 -56,15079537 0,730141782 1999 doubles rank -19,13169093 8,736846494 -2,189770754 0,031529566 -36,52541617 -1,737965701 -42,19993634 3,936554471 Intercept 103668,963 20591,82462 5,034471928 4,15724E-06 62532,06432 144805,8617 49000,66809 158337,2579 2000 singles rank -79,03258388 39,19966087 -2,016154786 0,047985309 -157,3429088 -0,722258909 -183,1019736 25,03680582 2000 doubles rank -57,05713113 29,75526628 -1,917547321 0,059633501 -116,500109 2,385846682 -136,0530289 21,93876661 Intercept 152907,9865 28351,88488 5,393221196 1,03576E-06 96285,3624 209530,6105 77673,29803 228142,6749 2001 singles rank -168,5267363 53,59758589 -3,144297145 0,002509684 -275,5685069 -61,48496569 -310,7535297 -26,29994281 2001 doubles rank -31,77354383 36,90614178 -0,860928352 0,392442198 -105,4801986 41,93311097 -129,7078466 66,16075894 Intercept 285484,8281 61651,3209 4,630636034 2,05225E-05 162120,8199 408848,8363 121383,8851 449585,7711 2002 singles rank -292,4971789 140,4831977 -2,08207945 0,041681272 -573,6034081 -11,39094969 -666,4295598 81,43520199 2002 doubles rank -92,82738109 105,4998 -0,879882058 0,382493104 -303,9319932 118,277231 -373,642397 187,9876348 Intercept 438533,5832 104471,2046 4,197650298 7,90307E-05 230119,2325 646947,9339 161791,7886 715275,3779 2003 singles rank -295,0135253 234,0976105 -1,260215876 0,211836881 -762,02548 171,9984293 -915,1326584 325,1056077 2003 doubles rank -125,4135348 201,5991763 -0,622093488 0,535930696 -527,5928873 276,7658177 -659,4450706 408,6180011 Intercept 533328,2617 138622,0687 3,847354657 0,000217424 258090,8946 808565,6287 168870,377 897786,1463 2004 singles rank -371,9398415 186,3923574 -1,995467232 0,048886572 -742,0262366 -1,853446406 -861,9928679 118,1131849 2004 doubles rank -102,7504082 165,5792341 -0,620551295 0,536396565 -431,5118589 226,0110425 -538,0826603 332,581844 Intercept 549928,2285 113743,5199 4,834809306 3,99039E-06 324723,9751 775132,482 252213,5344 847642,9227 2005 singles rank -538,1570273 189,596587 -2,838432041 0,005325305 -913,5450745 -162,76898 -1034,411143 -41,90291149 2005 doubles rank -106,8036161 143,3322679 -0,745147047 0,457639788 -390,5915313 176,9842991 -481,9645249 268,3572926 Intercept 411257,93 129505,7503 3,175595901 0,001880163 154969,8572 667546,0027 72547,01999 749968,8399 2006 singles rank -481,5797579 227,9007748 -2,113111543 0,036563824 -932,5886914 -30,57082443 -1077,634208 114,4746923 2006 doubles rank -15,51775132 219,8896969 -0,070570616 0,943851382 -450,6729971 419,6374945 -590,6199311 559,5844285 Intercept 427313,0573 166215,4838 2,570837853 0,01156009 97701,51555 756924,5991 -8824,990134 863451,1048 2007 singles rank -612,7296391 403,9780089 -1,516740084 0,132366139 -1413,833191 188,3739125 -1672,740271 447,2809932 2007 doubles rank 47,05629603 419,9330647 0,112056659 0,910994478 -785,6867298 879,7993219 -1054,81931 1148,931902 Intercept 374732,1205 90205,6542 4,154197693 6,40254E-05 196001,1798 553463,0613 138353,6095 611110,6316 2008 singles rank -576,1695396 193,5907516 -2,976224508 0,003576264 -959,7447985 -192,5942807 -1083,462503 -68,87657628 2008 doubles rank 119,3689833 184,6848186 0,646338905 0,519382139 -246,5603109 485,2982775 -364,5865162 603,3244829 Intercept 607451,4278 167039,7901 3,636567236 0,000442691 275966,4007 938936,4549 168649,4089 1046253,447 2009 singles rank -893,14803 388,2587462 -2,300393845 0,023545975 -1663,634876 -122,6611843 -1913,077003 126,7809435 2009 doubles rank 82,68203509 387,6477079 0,213291691 0,831542543 -686,592225 851,9562952 -935,6417829 1101,005853 Intercept 569139,2756 145574,9773 3,909595496 0,000180139 279884,8965 858393,6548 185956,2102 952322,3411 2010 singles rank -624,8167793 389,209522 -1,605348132 0,111959883 -1398,167809 148,5342506 -1649,295714 399,6621558 2010 doubles rank -178,9039502 369,4897587 -0,484191905 0,629439054 -913,0722305 555,2643301 -1151,476442 793,6685414 Intercept 625764,8184 115299,8231 5,427283419 4,89191E-07 396666,5257 854863,111 322272,1407 929257,4961 2011 singles rank -514,6480836 272,0598564 -1,891672261 0,061786567 -1055,225223 25,92905601 -1230,765178 201,4690106 2011 doubles rank -346,5745144 279,1169726 -1,241681977 0,217616295 -901,1739937 208,0249649 -1081,26738 388,1183508 Intercept 266903,1793 166136,0638 1,606533665 0,111822429 -63364,38471 597170,7433 -170734,4849 704540,8435 2012 singles rank -1373,104856 321,043083 -4,277011181 4,88827E-05 -2011,317382 -734,8923293 -2218,800497 -527,4092145 2012 doubles rank 1300,785404 376,2224241 3,457490358 0,000849889 552,8799778 2048,69083 309,7356632 2291,835145 Intercept 518627,7016 100065,0901 5,182903457 1,70848E-06 319372,651 717882,7521 254336,0246 782919,3786 2013 singles rank -1223,439188 274,4982606 -4,457001606 2,78355E-05 -1770,035056 -676,8433204 -1948,443339 -498,4350381 2013 doubles rank 542,6343833 248,396892 2,184545784 0,031964913 48,01298026 1037,255786 -113,4308946 1198,699661
The most important results of these regressions are the significance of the singles rank coefficient and the doubles rank coefficient. The doubles rank coefficient is not significant in most of these years which is why for this section I will just focus on the singles coefficient and how this
48 coefficient has changed over time. Table 15 shows the singles coefficient per year and the level of significance it has over time.
Table 15 Singles coefficients of regressions over the years 1999-2013
Year Time coefficient P-value 1999 1 -28 5% 2000 2 -79 5% 2001 3 -169 1% 2002 4 -292 5% 2003 5 -295 21% 2004 6 -372 5% 2005 7 -538 1% 2006 8 -482 5% 2007 9 -613 13% 2008 10 -576 1% 2009 11 -893 5% 2010 12 -625 11% 2011 13 -515 6% 2012 14 -1373 1% 2013 15 -1223 1% Average change in price money for a 1 unit change (increase) in ranking In 1999, moving down one spot in the rankings (1 unit increase) would cost a player $28 on average In 2013, moving down one spot in the rankings (1 unit increase) would cost a player $1223 on average
The table shows how the singles coefficient was -28 in 1999 and decreased to -1223 in
2013. This means that in year 1999, a one-unit change in ranking would on average cause a decrease of 28 in total earnings. This means that if a player would move down one spot in the rankings (e.g. from 50 to 51 or from 500 to 501) this would cost a player $28 dollars on average.
The coefficient has increased over time and in 2013 the singles rank coefficient was -1223. This means that if a player would move down one spot in the rankings this would cost $1223 on average. The table also shows how significant these results were per year, and it shows how in most of the years these results were significant at the 1% or 5% level. Only the years 2003, 2007 and 2010 seem to be somewhat less significant. This change in coefficients is clearly shown in figure 13.
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Figure 13 Average change in total prize money for a 1 unit change in rank
These results clearly show that the gap between earnings of professional players is getting bigger over time. As Morales (2013) stated, players who are playing at futures and challengers event have not received more prize money in recent years whereas the top players are getting larger paychecks every year. This causes a difference in pay between the highest paid tennis players and the lower paid players which is increasing over time. The years 2010 and 2011 seem to show a dip in this increase but the results for these years were not as significant as they were for the other years. Figure 14 shows how the trend line fits through the coefficient data points.
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Figure 14 Trend line through the coefficients
The formula to determine the average change in total prize money for a 1 unit change in ranking is Y = -76.685X + 75.314. In this formula, the purple line Y equals the average change in total prize money for a one unit change in singles rank, and X equals time. Time equals 1 in 1999 and equals 15 in 2013. According to this formula, the coefficient will be $1151 in 2014 (time =
16), $1228 in 2015 (time = 17), and $1305 in 2016 (time = 18). Even though this line is the best fitting line through the actual data points, it is useful to predict an area in which I can say that I am
95% confident that the coefficient in the future will be in this particular area. I do this by regressing time on the actual coefficients and then I use the coefficient for the 95% UB and the
95% LB to determine the area in which the predictions will be significant at the 5% level. These regression results are shown in table 16.
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Table 16 Regression of time on actual coefficients
R Square 0.796943
Adjusted R Square 0.781324
Coefficient Intercept -75.3141
Coefficient Time 76.6852 t-Stat 7.142938
P-value 7.56E-06
LB 95.0% 53.49190215
UB 95.0% 99.87850015
Looking at the time variable, I now use the Lower 95% and the Upper 95% coefficients to determine the area in which I can predict the average increase in prize money for a one unit change in rank significant at the 5% level. If I plot these lines next to the actual coefficients line and the predicted coefficients line, I can determine the 95% area in which future coefficients will fall.
Figure 15 shows the results.
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Figure 15 The 95% area to predict the coefficient in the future
These results show the actual coefficient line and the predicted coefficient line like the results in graph 13. However, I now add in the Predicted UB 95% and the Predicted LB 95%. The area between these green and purple lines marks the area in which the coefficient will fall in the future being 95% sure. In other words, there is a 95% chance that the coefficient that explains average increase in prize money for a one unit change in rank will fall between 569 and 1733 in
2014, between 623 and 1833 in 2015 and between 676 and 1933 in 2016.
4.3 Future prize money in tennis
As Morales (2013) stated, prize money at futures and challenger events has pretty much remained flat since 1990 whereas prize money at Grand Slam tournaments has increased rapidly during this period. I have collected data on prize money at the four Grand Slam tournaments
(Australian Open, French Open, Wimbledon and US Open) over the past 4 years and examine the percentage increase of singles prize money and doubles prize money per round of play. Taking the
53 average growth rate of prize money and applying this growth rate to future editions of these tournaments I estimated the prize money for the editions of 2015 and 2016. Table 17-20 show the results.
Table 17: Prize Money Wimbledon; past 3 editions and expectation for next 2 editions
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Table 18: Prize Money US Open; past 3 editions and expectation for next 2 editions
US Open 2011 US Open 2012 US Open 2013 Singles Player Total Player Total Increase Player Total Increase Winner $ 1.800.000,00 $ 1.800.000,00 $ 1.900.000,00 $ 1.900.000,00 5,5556% $ 2.600.000,00 $ 2.600.000,00 36,8421% Runner-up $ 900.000,00 $ 900.000,00 $ 950.000,00 $ 950.000,00 5,5556% $ 1.300.000,00 $ 1.300.000,00 36,8421% Semis $ 450.000,00 $ 900.000,00 $ 475.000,00 $ 950.000,00 5,5556% $ 650.000,00 $ 1.300.000,00 36,8421% Quarters $ 225.000,00 $ 900.000,00 $ 237.500,00 $ 950.000,00 5,5556% $ 325.000,00 $ 1.300.000,00 36,8421% 4th round $ 110.000,00 $ 880.000,00 $ 120.000,00 $ 960.000,00 9,0909% $ 165.000,00 $ 1.320.000,00 37,5000% 3rd round $ 55.000,00 $ 880.000,00 $ 65.000,00 $ 1.040.000,00 18,1818% $ 93.000,00 $ 1.488.000,00 43,0769% 2nd round $ 31.000,00 $ 992.000,00 $ 37.000,00 $ 1.184.000,00 19,3548% $ 53.000,00 $ 1.696.000,00 43,2432% 1st round $ 19.000,00 $ 1.216.000,00 $ 23.000,00 $ 1.472.000,00 21,0526% $ 32.000,00 $ 2.048.000,00 39,1304% TOTAL EVENT $ 8.468.000,00 $ 9.406.000,00 11,0770% $ 13.052.000,00 38,7625%
Doubles Team Total Team Total Increase Team Total Increase Winners $ 420.000,00 $ 420.000,00 $ 420.000,00 $ 420.000,00 0,0000% $ 460.000,00 $ 460.000,00 9,5238% Runners-up $ 210.000,00 $ 210.000,00 $ 210.000,00 $ 210.000,00 0,0000% $ 230.000,00 $ 230.000,00 9,5238% Semis $ 105.000,00 $ 210.000,00 $ 105.000,00 $ 210.000,00 0,0000% $ 115.000,00 $ 230.000,00 9,5238% Quarters $ 50.000,00 $ 200.000,00 $ 50.000,00 $ 200.000,00 0,0000% $ 58.000,00 $ 232.000,00 16,0000% 3rd round $ 25.000,00 $ 200.000,00 $ 26.000,00 $ 208.000,00 4,0000% $ 30.000,00 $ 240.000,00 15,3846% 2nd round $ 15.000,00 $ 240.000,00 $ 16.000,00 $ 256.000,00 6,6667% $ 18.750,00 $ 300.000,00 17,1875% 1st round $ 10.000,00 $ 320.000,00 $ 11.000,00 $ 352.000,00 10,0000% $ 12.500,00 $ 400.000,00 13,6364% TOTAL EVENT $ 1.800.000,00 $ 1.856.000,00 3,1111% $ 2.092.000,00 12,7155%
ALL EVENTS $ 23.718.000,00 $ 25.500.000,00 7,5133% $ 33.600.000,00 31,7647% US Open 2014 Expectation US Open 2015 Expectation US Open 2016 Singles Player Total Increase avg increase Player Total Player Total Winner $ 3.000.000,00 $ 3.000.000,00 15,3846% 19,2608% $ 3.577.823 $ 3.577.823 $ 4.266.939 $ 4.266.939 Runner-up $ 1.450.000,00 $ 1.450.000,00 11,5385% 17,9787% $ 1.710.691 $ 1.710.691 $ 2.018.251 $ 2.018.251 Semis $ 730.000,00 $ 1.460.000,00 12,3077% 18,2351% $ 863.116 $ 1.726.233 $ 1.020.507 $ 2.041.013 Quarters $ 370.250,00 $ 1.481.000,00 13,9231% 18,7736% $ 439.759 $ 1.759.037 $ 522.318 $ 2.089.271 4th round $ 187.300,00 $ 1.498.400,00 13,5152% 20,0354% $ 224.826 $ 1.798.610 $ 269.871 $ 2.158.968 3rd round $ 105.090,00 $ 1.681.440,00 13,0000% 24,7529% $ 131.103 $ 2.097.645 $ 163.555 $ 2.616.874 2nd round $ 60.420,00 $ 1.933.440,00 14,0000% 25,5327% $ 75.847 $ 2.427.099 $ 95.213 $ 3.046.803 1st round $ 35.754,00 $ 2.288.256,00 11,7313% 23,9714% $ 44.325 $ 2.836.784 $ 54.950 $ 3.516.802 TOTAL EVENT $ 14.792.536,00 13,3354% 21,0583% $ 17.907.592 $ 21.678.625
Doubles Team Total Increase Winners $ 520.000,00 $ 520.000,00 13,0435% 7,5224% $ 559.117 $ 559.117 $ 601.176 $ 601.176 Runners-up $ 250.000,00 $ 250.000,00 8,6957% 6,0732% $ 265.183 $ 265.183 $ 281.288 $ 281.288 Semis $ 124.450,00 $ 248.900,00 8,2174% 5,9137% $ 131.810 $ 263.619 $ 139.605 $ 279.209 Quarters $ 62.060,00 $ 248.240,00 7,0000% 7,6667% $ 66.818 $ 267.272 $ 71.941 $ 287.763 3rd round $ 32.163,00 $ 257.304,00 7,2100% 8,8649% $ 35.014 $ 280.114 $ 38.118 $ 304.945 2nd round $ 20.063,00 $ 321.008,00 7,0027% 10,2856% $ 22.127 $ 354.026 $ 24.402 $ 390.439 1st round $ 13.375,00 $ 428.000,00 7,0000% 10,2121% $ 14.741 $ 471.708 $ 16.246 $ 519.879 TOTAL EVENT $ 2.273.452,00 8,6736% 8,1667% $ 2.459.119 $ 2.659.949
ALL EVENTS $ 38.251.760,00 13,8445% 17,7075% $ 45.025.192 $ 52.998.029
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Table 19: Prize Money Australian Open; past 3 editions and expectation for next 2 editions
Australian Open 2011 Australian Open 2012 Australian Open 2013 Singles Player Total Player Total Increase Player Total Increase Winner AUD 2.200.000,00 AUD 2.200.000,00 AUD 2.300.000,00 AUD 2.300.000,00 4,5455% AUD 2.430.000,00 AUD 2.430.000,00 5,6522% Runner-up AUD 1.100.000,00 AUD 1.100.000,00 AUD 1.150.000,00 AUD 1.150.000,00 4,5455% AUD 1.215.000,00 AUD 1.215.000,00 5,6522% Semis AUD 420.000,00 AUD 840.000,00 AUD 437.000,00 AUD 874.000,00 4,0476% AUD 500.000,00 AUD 1.000.000,00 14,4165% Quarters AUD 210.000,00 AUD 840.000,00 AUD 218.500,00 AUD 874.000,00 4,0476% AUD 250.000,00 AUD 1.000.000,00 14,4165% 4th round AUD 93.000,00 AUD 744.000,00 AUD 109.250,00 AUD 874.000,00 17,4731% AUD 125.000,00 AUD 1.000.000,00 14,4165% 3rd round AUD 54.500,00 AUD 872.000,00 AUD 54.625,00 AUD 874.000,00 0,2294% AUD 71.000,00 AUD 1.136.000,00 29,9771% 2nd round AUD 32.000,00 AUD 1.024.000,00 AUD 33.300,00 AUD 1.065.600,00 4,0625% AUD 45.500,00 AUD 1.456.000,00 36,6366% 1st round AUD 20.000,00 AUD 1.280.000,00 AUD 20.800,00 AUD 1.331.200,00 4,0000% AUD 27.600,00 AUD 1.766.400,00 32,6923% TOTAL EVENT AUD 8.900.000,00 AUD 9.342.800,00 4,9753% AUD 11.003.400,00 17,7741%
Doubles Team Total Team Total Increase Team Total Increase Winners AUD 454.500,00 AUD 454.500,00 AUD 454.500,00 AUD 454.500,00 0,0000% AUD 475.000,00 AUD 475.000,00 4,5105% Runners-up AUD 227.250,00 AUD 227.250,00 AUD 227.250,00 AUD 227.250,00 0,0000% AUD 237.500,00 AUD 237.500,00 4,5105% Semis AUD 113.000,00 AUD 226.000,00 AUD 113.000,00 AUD 226.000,00 0,0000% AUD 118.750,00 AUD 237.500,00 5,0885% Quarters AUD 56.000,00 AUD 224.000,00 AUD 56.000,00 AUD 224.000,00 0,0000% AUD 60.000,00 AUD 240.000,00 7,1429% 3rd round AUD 31.500,00 AUD 252.000,00 AUD 31.500,00 AUD 252.000,00 0,0000% AUD 33.500,00 AUD 268.000,00 6,3492% 2nd round AUD 17.200,00 AUD 275.200,00 AUD 17.200,00 AUD 275.200,00 0,0000% AUD 19.500,00 AUD 312.000,00 13,3721% 1st round AUD 9.600,00 AUD 307.200,00 AUD 9.600,00 AUD 307.200,00 0,0000% AUD 12.500,00 AUD 400.000,00 30,2083% TOTAL EVENT AUD 1.966.150,00 AUD 1.966.150,00 0,0000% AUD 2.170.000,00 10,3680%
ALL EVENTS AUD 25.000.000,00 AUD 26.000.000,00 4,0000% AUD 30.000.000,00 15,3846% Australian Open 2014 Expectation Australian Open 2015 Expectation Australian Open 2016 Singles Player Total Increase avg increase Player Total Player Total Winner AUD 2.650.000,00 AUD 2.650.000,00 9,0535% 6,4170% AUD 2.820.051,62 AUD 2.820.051,62 AUD 3.001.015,52 AUD 3.001.015,52 Runner-up AUD 1.325.000,00 AUD 1.325.000,00 9,0535% 6,4170% AUD 1.410.025,81 AUD 1.410.025,81 AUD 1.500.507,76 AUD 1.500.507,76 Semis AUD 540.000,00 AUD 1.080.000,00 8,0000% 8,8214% AUD 587.635,37 AUD 1.175.270,74 AUD 639.472,83 AUD 1.278.945,66 Quarters AUD 270.000,00 AUD 1.080.000,00 8,0000% 8,8214% AUD 293.817,69 AUD 1.175.270,74 AUD 319.736,42 AUD 1.278.945,66 4th round AUD 135.000,00 AUD 1.080.000,00 8,0000% 13,2965% AUD 152.950,32 AUD 1.223.602,54 AUD 173.287,40 AUD 1.386.299,24 3rd round AUD 75.000,00 AUD 1.200.000,00 5,6338% 11,9468% AUD 83.960,07 AUD 1.343.361,11 AUD 93.990,58 AUD 1.503.849,22 2nd round AUD 50.000,00 AUD 1.600.000,00 9,8901% 16,8631% AUD 58.431,54 AUD 1.869.809,31 AUD 68.284,90 AUD 2.185.116,80 1st round AUD 30.000,00 AUD 1.920.000,00 8,6957% 15,1293% AUD 34.538,80 AUD 2.210.482,94 AUD 39.764,28 AUD 2.544.913,98 TOTAL EVENT AUD 11.935.000,00 8,4665% 10,4053% AUD 13.176.871,35
Doubles Team Total Increase Winners AUD 520.000,00 AUD 520.000,00 9,4737% 4,6614% AUD 544.239,17 AUD 544.239,17 AUD 569.608,21 AUD 569.608,21 Runners-up AUD 260.000,00 AUD 260.000,00 9,4737% 4,6614% AUD 272.119,58 AUD 272.119,58 AUD 284.804,11 AUD 284.804,11 Semis AUD 130.000,00 AUD 260.000,00 9,4737% 4,8541% AUD 136.310,28 AUD 272.620,56 AUD 142.926,86 AUD 285.853,72 Quarters AUD 65.000,00 AUD 260.000,00 8,3333% 5,1587% AUD 68.353,17 AUD 273.412,70 AUD 71.879,33 AUD 287.517,32 3rd round AUD 36.000,00 AUD 288.000,00 7,4627% 4,6040% AUD 37.657,43 AUD 301.259,42 AUD 39.391,16 AUD 315.129,29 2nd round AUD 21.000,00 AUD 336.000,00 7,6923% 7,0215% AUD 22.474,51 AUD 359.592,13 AUD 24.052,55 AUD 384.840,77 1st round AUD 13.500,00 AUD 432.000,00 8,0000% 12,7361% AUD 15.219,38 AUD 487.020,00 AUD 17.157,73 AUD 549.047,41 TOTAL EVENT AUD 2.356.000,00 8,5714% 6,3131% AUD 2.504.737,47 AUD 2.662.864,94
ALL EVENTS AUD 33.000.000,00 10,0000% 9,7949% AUD 36.232.307,69 AUD 39.781.215,78
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Table 20: Prize Money French Open; past 3 editions and expectation for next 2 editions
French Open 2011 French Open 2012 French Open 2013 Singles Player Total Player Total Increase Player Total Increase Winner € 1.200.000,00 € 1.200.000,00 € 1.250.000,00 € 1.250.000,00 4,1667% € 1.500.000,00 € 1.500.000,00 20,0000% Runner-up € 600.000,00 € 600.000,00 € 625.000,00 € 625.000,00 4,1667% € 750.000,00 € 750.000,00 20,0000% Semis € 300.000,00 € 600.000,00 € 310.000,00 € 620.000,00 3,3333% € 375.000,00 € 750.000,00 20,9677% Quarters € 150.000,00 € 600.000,00 € 155.000,00 € 620.000,00 3,3333% € 190.000,00 € 760.000,00 22,5806% 4th round € 75.000,00 € 600.000,00 € 80.000,00 € 640.000,00 6,6667% € 100.000,00 € 800.000,00 25,0000% 3rd round € 42.000,00 € 672.000,00 € 47.000,00 € 752.000,00 11,9048% € 60.000,00 € 960.000,00 27,6596% 2nd round € 25.000,00 € 800.000,00 € 28.000,00 € 896.000,00 12,0000% € 35.000,00 € 1.120.000,00 25,0000% 1st round € 15.000,00 € 960.000,00 € 18.000,00 € 1.152.000,00 20,0000% € 21.000,00 € 1.344.000,00 16,6667% TOTAL EVENT € 6.032.000,00 € 6.555.000,00 8,6704% € 7.984.000,00 21,8002%
Doubles Team Total Team Total Increase Team Total Increase Winners € 330.000,00 € 330.000,00 € 340.000,00 € 340.000,00 3,0303% € 360.000,00 € 360.000,00 5,8824% Runners-up € 165.000,00 € 165.000,00 € 170.000,00 € 170.000,00 3,0303% € 180.000,00 € 180.000,00 5,8824% Semis € 82.500,00 € 165.000,00 € 85.000,00 € 170.000,00 3,0303% € 90.000,00 € 180.000,00 5,8824% Quarters € 42.000,00 € 168.000,00 € 43.000,00 € 172.000,00 2,3810% € 50.000,00 € 200.000,00 16,2791% 3rd round € 22.000,00 € 176.000,00 € 23.000,00 € 184.000,00 4,5455% € 28.000,00 € 224.000,00 21,7391% 2nd round € 12.000,00 € 192.000,00 € 12.000,00 € 192.000,00 0,0000% € 15.000,00 € 240.000,00 25,0000% 1st round € 7.500,00 € 240.000,00 € 8.000,00 € 256.000,00 6,6667% € 8.000,00 € 256.000,00 0,0000% TOTAL EVENT € 1.436.000,00 € 1.484.000,00 3,3426% € 1.640.000,00 10,5121%
ALL EVENTS € 17.520.000,00 € 18.700.000,00 6,7352% € 21.017.000,00 12,3904% French Open 2014 Expectation Australian Open 2015 Expectation Australian Open 2016 Singles Player Total Increase avg increase Player Total Player Total Winner € 1.650.000,00 € 1.650.000,00 10,0000% 11,3889% € 1.837.916,67 € 1.837.916,67 € 2.047.234,95 € 2.047.234,95 Runner-up € 825.000,00 € 825.000,00 10,0000% 11,3889% € 918.958,33 € 918.958,33 € 1.023.617,48 € 1.023.617,48 Semis € 412.500,00 € 825.000,00 10,0000% 11,4337% € 459.663,98 € 919.327,96 € 512.220,54 € 1.024.441,08 Quarters € 220.000,00 € 880.000,00 15,7895% 13,9012% € 250.582,53 € 1.002.330,13 € 285.416,39 € 1.141.665,55 4th round € 125.000,00 € 1.000.000,00 25,0000% 18,8889% € 148.611,11 € 1.188.888,89 € 176.682,10 € 1.413.456,79 3rd round € 72.000,00 € 1.152.000,00 20,0000% 19,8548% € 86.295,44 € 1.380.727,05 € 103.429,21 € 1.654.867,35 2nd round € 42.000,00 € 1.344.000,00 20,0000% 19,0000% € 49.980,00 € 1.599.360,00 € 59.476,20 € 1.903.238,40 1st round € 24.000,00 € 1.536.000,00 14,2857% 16,9841% € 28.076,19 € 1.796.876,19 € 32.844,69 € 2.102.059,92 TOTAL EVENT € 9.212.000,00 15,3808% 15,2838% € 10.619.941,77 € 12.243.070,25
Doubles Team Total Increase Winners € 400.000,00 € 400.000,00 11,1111% 6,6746% € 426.698,36 € 426.698,36 € 455.178,72 € 455.178,72 Runners-up € 200.000,00 € 200.000,00 11,1111% 6,6746% € 213.349,18 € 213.349,18 € 227.589,36 € 227.589,36 Semis € 100.000,00 € 200.000,00 11,1111% 6,6746% € 106.674,59 € 213.349,18 € 113.794,68 € 227.589,36 Quarters € 55.000,00 € 220.000,00 10,0000% 9,5533% € 60.254,34 € 241.017,35 € 66.010,64 € 264.042,56 3rd round € 31.000,00 € 248.000,00 10,7143% 12,3330% € 34.823,22 € 278.585,73 € 39.117,95 € 312.943,59 2nd round € 17.000,00 € 272.000,00 13,3333% 12,7778% € 19.172,22 € 306.755,56 € 21.622,01 € 345.952,10 1st round € 8.500,00 € 272.000,00 6,2500% 4,3056% € 8.865,97 € 283.711,11 € 9.247,70 € 295.926,45 TOTAL EVENT € 1.812.000,00 10,4878% 8,1142% € 1.959.029,02 € 2.117.988,24
ALL EVENTS € 23.968.900,00 14,0453% 11,0569% € 26.619.127,76 € 29.562.389,70
These tables give an overview of what amount of prize money per round of play could be expected during Grand Slam tournaments in 2015 and in 2016. However, there are only 128 players who enter the main draw of a Grand Slam tournament which means that these increases in prize money are only available to the approximately top 128 players. If the top 128 players are the only players who receive this increase in prize money, the pay gap between these top 128 players and the rest of the players will increase over time. The coefficient determined in section 4.2 and the predicted coefficient for 2014, 2015 and 2016 suggests that this increase in pay gap will
57 happen in the future. To make an estimation of what the difference in pay between the top 128 players and the rest of the players will be I calculate the average prize money earned by all top 128 players in my model in 2011. After that, I calculate the average prize money earned by all players outside the top 128 in my model in 2011. These results should give an estimation of the average prize money of players ranked inside the top 128 and an estimation of the average prize money of players ranked outside the top 128. In 2011, the average prize money of players used in my model ranked in the top 128 of the ATP rankings was $701,743 and the average prize money of players used in my model ranked outside the top 128 was $35,310. This means that in 2011 the average difference in prize money earned between the top 128 players and players ranked outside the top
128 was $666,433. If the statement of Morales (2013) that players playing at challenger and futures level are not receiving any increase in prize money is true, we can calculate what the average difference in price money between these two groups of players will be in the future.
According to the increases in prize money at the grand slam tournaments, the average increase in prize money over the last three years is 17.69%. Morales states that 40% of yearly prize money earned by a player that plays all Grand Slam tournaments (i.e. a player ranked inside the top 128) is explained by prize money during these four Grand Slam tournaments. Taking these two factors into account, a player ranked in the ATP top 128 will earn a yearly increase in prize money of 40% of 17.69% which equals 7.08%. Stating that a player will earn on average earn 7.08% more prize money per year by just being ranked inside the top 128 I calculate what the expected average difference in pay will be in future years. Table 21 shows the results
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Table 21 Expected difference in pay between top 128 players and rest
2010 2011 2012 2013 2014 2015 2016 Average Income Top 128 626738 701743 822653 763194 Average Income Rest 27538 35311 41974 44639 Actual Difference Avg Inc top 123 and Avg inc. Rest 599200 666433 780679 718556 Expected Average income top 128 713586 764076 818138 876025 938008 Expected average income Rest 35311 35311 35311 35311 35311 Expectation Difference avg inc top 128 and avg. Inc. Rest 678275 728765 782827 840714 902697
As shown in Table 21, the expected difference in average income between the top 128 ranked players and the rest of the players is increasing over time. I calculate the actual difference to be $780,679 in 2012 and $718,556 in 2013. Using the income growth rate of 7.08% to estimate the annual prize money of players ranked in the top 128 I estimate prize money for players in this category until 2016. If Morales’ (2013) statement were true and prize money for the players ranked outside the top 128 would remain flat, I can calculate the expected difference in average income
(table 20). However, there might be some players every year that end up with an end of year ranking outside the top 128 but that have played some of the Grand Slam tournaments in the beginning of the year. There might also be players that were ranked outside the top 128 but were able to compete in Grand Slam tournaments by receiving wild cards. This might be one of the reasons that the actual average income of players ranked outside the top 128 has increased over time as well. The actual average growth rate of income of players ranked inside the top 128 was
7.3% which is very close to the 7.08% I predicted by using the growth rate of prize money at
Grand Slams. Using the predicted growth rate of 7.08% for top 128 players and the expectation that prize money for other players would remain flat on average, I calculate the expected difference in average income between the two different categories of players to be $728,765 in
2013. Using the data of players in this research I calculate the actual difference in average income between the two different categories of players to be $718,556. It seems like this formula is quite accurate in predicting the difference in income over time. In 2012 the difference was larger, which
59 was also suggested by our 2012 coefficient in section 4.2. Our 2012 coefficient was higher than the coefficient in 2013, even though prize money at Grand Slams was higher in 2013. The reason for this could be that players used in the portfolios during this research performed very well in 2012.
Another reason for this could be that the players ranked in the top 128 by the end of 2012 had been very dominant and consistent during that year, which would cause no outflow of Grand Slam prize money to players ranked outside the top128. If players ranked between 100 and 150 are switching places in the rankings during the season, the prize money at Grand Slam tournaments will be more equally divided over these 50 players and cause a smaller pay gap between the top 128 and the rest of the players. If the top 128 players stay dominant at the top 128, all Grand Slam prize money will go to these top 128 players which will cause a bigger pay gap with players ranked outside the top
128. As table 20 shows, with increases in prize money at Grand Slams and less increases in prize money at other tournaments, the pay gap between professional tennis players ranked inside the top
128 and professional tennis players ranked outside the top 128 is expecting to grow in the future.
In 2016, I expect the pay gap between the average income of these two groups of players to be
$902,697.
CONCLUSION 5.1 Portfolios
If portfolios would have been made in the past consisting of the top players in the ITF junior rankings, they could have been profitable for investors. The top 20 junior players from 1998 have earned an average income of $6.5 million dollars and a few of them are still active on the
ATP world tour. A deal in which every player would have received $50,000 in exchange for 10% of their prize money would have earned investors a profit of $11.5 million by the end of 2013.
Under these same conditions, the portfolio of players ranked between ATP500 and ATP600 that
60 were aged under the age of 26 would have caused a loss for investors of $52,000 by the end of
2013. The portfolios of Top50 ITF juniors in 2004 and Top50 ITF juniors in 2005 would have generated profits of respectively $4.1 million and $800,000 by the end of 2013. Many of the players in these portfolios are still active as professional tennis players which means that profits for investors will keep increasing over time. The portfolio consisting of twenty former college tennis players would have generated a profit of $1.1 million by the end of 2013, even though many of these players have just started their professional career.
5.2 Impact of ranking on earnings in men’s professional tennis
Over the period 1998-2013 the best formula to predict yearly earnings in men’s professional tennis by singles ranking is;
Earnings year n = 179692*e(-0.0049X). In which X is singles rank in year n.
The higher a player gets in the rankings, the more exponential his earnings are increasing.
Players ranked outside the top 200 struggle financially and players ranked in the top 10 make an average income of $5 million per year. In doubles, the earnings model is less exponential than in singles, and a player’s doubles prize money over the period 1998-2013 can be best determined by using ranking in the following formula:
Doubles earnings year n = 27621.42*e(-0.00348X). In which X is doubles rank in year n.
Tennis players can earn prize money by playing singles and doubles tournaments at the same time and earn a ranking in singles and doubles during the same year. Over the period 1998-
2013, the most accurate formula to predict yearly total prize money of a player knowing his end- of-year singles rank and end-of-year doubles rank is:
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PMyn = 260078*e(-0.00367rankSyn)*e(-0.00111rankDyn). In which PMyn stands for prize money in year n, rankSyn stands for singles rank in year n and rankDyn stands for doubles rank in year n.
5.3 The change in impact of ranking on earnings in men’s professional tennis.
Lower ranked players generally earn less prize money than players that are ranked higher on the ATP rankings. In 1998, the average increase in prize money for a player that moves up one spot in the rankings was $28. Over the years the pay gap between higher ranked players and lower ranked players has increased due to the fact that prize money at Grand Slam tournaments is increasing rapidly whereas prize money at futures and challenger events is remaining flat. In 2013, the average increase in prize money for a player that moves up one spot in the rankings was $1223.
Taking this information forward, there is a 95% chance that the average increase in prize money for a one unit change in rank will fall between 569 and 1733 in 2014, between 623 and 1833 in
2015 and between 676 and 1933 in 2016. Prize money at Grand Slams has increased with 17.69% over the past three years. This trend is expected to continue in the future. A player that plays all four Grand Slam tournaments in a given year is expected to generate 40% of his prize money from these tournaments. This means that a player will earn on average 7.08% more every year by keeping the same ranking and by playing all Grand Slam tournaments. There are 128 players who enter a main draw of a Grand Slam tournament, and the average yearly earnings of the top 128
ATP players is expected to be $818,138 in 2014, $876,025 in 2015 and $938,008 in 2016. If the average income of players ranked outside the top 128 will remain flat, the difference in pay between the top 128 players and the other players will increase to an average of $902,697 in 2016.
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5.4 The benefits of creating portfolios of tennis players.
If portfolios are created of tennis players in the future, it can benefit players, investors and the sport of tennis in general. Tennis players have the potential to earn a lot of prize money and income with their sport but they do need financing during the early stages of their careers. An investment pool could allow these players to start their careers and if a few of them break into the top 128 for several years they can make these portfolios profitable for investors. In addition to the financial benefits for players and investors, the sport of tennis will be followed with more interest and enthusiasm by investors and fans in general. If there is more publicity for tennis players and tournaments at the lower level, prize money of these tournaments can increase and potentially lead to more profits for investors. If profit increases, more investors will be attracted and publicity will increase again. This continuing spiral could potentially be a new breakthrough in professional sports. Creating a business model that allows investors to deal in shares of athletes could be the future of sports finance and a new way of investing. I am currently setting up a business model and information on it can be found on www.slamstox.com. Copies of the pages of the website are included in the appendix.
5.5 Topics for new research
This research can be used to create business models or theories to combine tennis players into portfolios and to sell shares of these portfolios to investors. The portfolios of this research are standardized and do not take into account the fact if a player would have gotten into a deal like this in the first place. Assuming that not every player in a portfolio would have taken the deal, it could be interesting for new research to generate hundreds of random smaller portfolios out of the current portfolios and to see how many of those would have been profitable in the past. In this
63 research I have mostly examined the impact of ranking on earnings in tennis. New research could examine the impact of certain factors on ranking, such as height, serve speed, nationality, weight, number of tournaments played and more. Just as in football, researchers could set up international tennis combines in which tennis players will be tested on several physical factors. The results from these test can then be used to predict the future ranking of a tennis player, and with ranking the future prize money can be estimated. This research could also be done for women’s tennis or for other sports.
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APPENDIX – SLAMSTOX
Slamstox Homepage
About Slamstox
68
Slamstox infographic
69
Slamstox for Athletes
70
Slamstox for investors
71
Slamstox for Business
72
Slamstox Scholarships
73