Impacts of Income on Professional Tennis Players' Performance

Introduction

Tennis is one of the most popular sports globally. Like most of the popular sports, tennis is perceived as a lucrative profession. It is considered by many to be the sport of the elite because of its uniqueness and style of play. It is referred to as a racket sport since it is played by an individual against a single opponent. Each of the players uses the tennis racket that is strung with cords for striking a hollow rubber ball. The points are awarded to a player when the opponent fails to strike the ball correctly to the prescribed dimension of the court. The game was introduced in 1873 by Major Walter Clopton Wingfield and was recognized in 1973. Tennis gained momentum starting from Australia, Europe, the USA and it had spread globally. Nowadays, tennis is played by millions of clubs worldwide in public courts. This has led to the rapid growth of spectators and participants. There have emerged professional and world champion players such as William and Ernest Renshaw, who were brothers and seven times in three occasion champions. 

Four Grand Slam tournaments are popular in the world of tennis (WTA, 2016). They occur at different times on the tennis calendar. Players travel from all over the globe to participate in this major tournament. The four Grand Slam tournaments are; first, Australia Open that takes place in January and it is played on a hard surface. Second, the French Open takes place between May and June. This tournament is usually played in clay or red surface. Third, Wimbledon is played between June and July on grass or green surface. Finally, the US opens closes the years between August and September and it is played on a hard surface. These tournaments happen between January to September at given tie intervals on different surfaces. Because of the competitive nature of the sport, the player earns according to the tournament they play in and effort. 

The International Tennis Federation is a body tasked to rank the players. Based on the international tennis federation criteria, a professional tennis player should register for a Pro IPIN (International Player Identification Number) Membership. This allows the player to earn ranking points by participating in official tournaments. According to the International Tennis Federation (2020), there are about 5,000 female and 9,000 male professional players globally. 

The statistics show that top tennis players are among the best sportspeople across the globe (ITF, 2016). It is this richness that attracts most of the people to participate in the sport. In 2013, it was estimated that male tennis players earn approximately $38,000 lower than their female counterparts at $40,180, with the average amount on career prize of a tennis player being at $300,000. This implies that a winner in a given tournament will pocket about 300,000 US dollars while 80 % of the professional players earn close to nothing. This range is very wide compared to what Roger Federer earned at the age of 30 years. Therefore, tennis has the highest level of inequality in earnings. 

This study will delve into an examination of how the income of the pro tennis players is affected by factors such as experience, games played, hours practiced, body type and gender. The study is structured into four main sections: a literature review that gives background information about the relationship between income and experience, games played, hours practiced, body type and gender of a pro tennis player. Also, the data analysis section that explains the data that was used in the study, estimation of the parameters, results, and conclusion. 

Problem Statement

Tennis is a racket sport that has gained popularity across the globe. It is the most played and watched Olympic sport by all ages and sectors of the society. The origin of modern tennis is Birmingham, England and it has developed in many countries over time. The international tennis federation has indicated that there are more male professional players (55 %) compared to that of females (45 %). It was established that the female earns more compared to the male. However, a professional tennis player has four sources of income; prize money, starting fees, earnings from sponsorship and licensing and other occupations such as interclub tennis or teaching. Also, the sport is very competitive because of the high prizes based on various tournaments. This makes tennis to exhibit income inequalities because the winner of the tournament pockets the lion share. The issue of inequality of income arises from the manner the income is distributed across all the pro tennis players. Few types of research have been done on factors that affect the income of the professional tennis players. Therefore, the study will investigate the effects of the experience of the player, games played, hours practiced, body type and gender factors that affect the income of the professional tennis players. The findings of the study will help to model the wages of the professional tennis players. 

Objective 

To examine the factors that affect the income of the professional tennis players.

Research question

Does experience, games played, hours practiced, body type and gender affect the income of a tennis player? 

Hypothesis

H0: There is no relationship between income and experience, games played, hours practiced, body type and gender of a pro tennis player. 

H1: There is a relationship between income and experience, games played, hours practiced, body type and gender of a pro tennis player.

Motivation

The study is aimed to investigate the impact of the pro tennis player’s income on experience, games played, hours practiced, body type and gender. There has been inconsistency in the incomes of the professional tennis players, which has raised a lot of concern among various stakeholders in the tennis sport and the general public. Hence, the study will model the factors that impact the income of the professional tennis player. It will focus on five key factors that affect the income of a player, that is, the experience of the player, the number of games played, hours of practice, body type, and gender. The findings from this study will help and inform the players and coach on the areas that require improvement to achieve successes and increases incomes. Further, the literature on the factors affecting the income of pro tennis players is limited. The study will play a great role on enhancing the existing literature on the subject topic. 

Literature Review

A review in the literature is an integral part of any research study since it helps to frame the objectives and methodology. Also, it helps in the identification of the research gaps and the need for conducting the study. In this chapter, we review various studies that have been conducted by different scholars and their findings that are relevant to the study subject.

Beaton et al. (2014) and Kutz (2014) conducted a study on the best-ranked sports in the world. They found out that among the best world-ranked sport tennis player earns ten times in prize money compared to the 32nd ranked player. They also revealed that only 1 % of the top players earned half of the total prize money. The findings were far different compared to other ports that they analyzed, such as golf that had a horizontal prize distribution. Furthermore, success in tennis is linked to individual effort compared to football based on collective effort. 

Case & Fair (2013) assessed the income inequality in tennis among professional players. They identified three sources of income among tennis players. First, a tennis player can earn money via prize money. This is the amount of money spent on a given tournament. Second, earning from the starting fee, sponsorships and licensing, basically earned by the top-ranked players. Lastly, gains from occupations. Further, De Borger et al., (2015), stated that the inequality experienced among player is determined by the income distribution. There found out that top player earns the maximum amount of money compared to another player, creating a big inequality. 

According to the International Tennis Federation (ITF) (2014), income among players differs from gender. The female gender is likely to earn more compared to males. However, there are more male participants or players compared to females. Out of the 128 players who qualified for the Grand Slam tournaments, 18 % of the total prize money was taken home by the winner. 0.3 % were awarded to those who exited the tournament in the first round. However, this did not affect the endorsements and sponsorships that the top-ranked player received.

Estimation/ Model

The purpose of this study is to examine the effect of experience, games played, hours practiced, body type and gender on the income of a professional tennis player. We will employ the Ordinary Least Squares (OLS) regression for cross-section data. The OLS regression is a statistical method that models the relationship linking the response and explanatory variables. The method minimizes the sum of squared errors for the observed and predicted value on a straight line of the response variable. The OLS model is written as;

Y = a + bxi + e

 Where Y is the response variable, a is the constant term, b is the coefficient of the explanatory variables xi and e is the error component.

The main aim of the model is to minimize the error component. To get a good estimate, the OLS regression model should satisfy the linear regression. 

To establish the relationship between income and experience, games played, hours practiced, body type and gender of a pro tennis player. The income of the player to be the response variable and predictors to be the experience, games played, hours practiced, body type and gender. The trends will either be positively or negatively correlated.

The analysis will access the income of the pro tennis players. This will be done by developing a model explains the association linking income and the predictor variables. The following regression equation will be used

Y = ao + a1 X1 + a2 X2 + a3 X3 + a4 X4 +a5X5

Where Y represents income,  X1, X2, X3, X4 & X5  are the predictor variables and  a1, a2, a3, a4 & a5  are the coefficients. 

Variables

The response variable  is the variable under investigation. In this context, the response variable is the income of the player that is a continuous variable measured in US dollars. 

Predictor 0r explanatory variable helps to examine the effect of the response variable based on the given phenomenon. The experience of the player measured in years, games played, hours practiced, body type and gender (male or female). 

Data

Secondary data was obtained from 100 professional tennis players. For the study to be conducted there should be a questionnaire to be filled by the respondents, participant, and pen to be used to fill the questionnaire. The study used STATA for analysis. The questionnaire gathered demographic information and income, years of experience, games played, hours practiced and body type. 

The stratified random sampling technique was used in this study. This is a sampling method where data is divided into sections based on gender called strata. Each stratum shares distinct characteristics. The sample is then selected from each group at random. In this context, the population is divided into two sections based on gender. The sampling of players consisted of individuals who have registered as professional tennis players by ITF. The data collected is administrative, implying that it is extracted from the surveys that have been done, recorded and stored in the departmental database. The study took a sample of 100 players both male and female. Data was collected based on the income of the player that is a predictor variable. The other is the years of experience, games played, hours practiced, body type and gender of player, which is an explanatory variable (0= male, 1=female).

Results and Analysis

Descriptive statistics

The pie chart above is for gender of the player. The number of males who participated in the study is greater compared to that female. 

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

Income | 100 107.0658 34.94046 38 174

Experience | 100 4.815789 1.710443 0 7

nogames | 100 36.03947 10.29102 9 54

Time | 100 7.210526 4.940488 0 26

Body | 100 1.960526 1.204888 0 3

-------------+--------------------------------------------------------

Gender | 100 .4342105 .4989463 0 1

The above table shows that the mean income of the pro tennis layers is 107065.8 dollars with a standard deviation of 34,940.46. The average experience period is 4.815 years with a standard deviation of 1.71 years. Further, the mean number of games per player is 36 with the maximum number being 54 and a minimum of 9 games. The average time players took to practice is 7.2 hours per week with a standard deviation of 4.9 hours.

Regression analysis 

Source | SS df MS Number of obs = 100

-------------+------------------------------ F( 5, 99) = 15.26

Model | 47751.3314 5 9550.26628 Prob > F = 0.0000

Residual | 43811.3396 98 625.876281 R-squared = 0.5215

-------------+------------------------------ Adj R-squared = 0.4873

Total | 91562.6711 98 1220.83561 Root MSE = 25.018

------------------------------------------------------------------------------

Income | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

Experience | 7.771444 1.996034 3.89 0.000 3.790479 11.75241

nogames | .2083964 .2908809 0.72 0.476 -.3717473 .7885401

Time | 2.858361 .6635843 4.31 0.000 1.534883 4.181838

Body | 20.47838 5.8133 3.52 0.001 8.884123 32.07265

Gender | 63.99685 13.67723 4.68 0.000 36.71846 91.27523

_cons | -26.41714 22.15652 -1.19 0.237 -70.60693 17.77264

In the above table, F (5, 98) = 15.26, p < 0.05. Since the p-value I smaller compared to alpha = 005, we reject the null hypothesis. Thus, we deduce that there is a significant association between income and experience, games played, hours practiced, body type and gender of a pro tennis player. 

The regression equation is;

Income = -26.41+ 7.77 Experience + 0. 2083games+2.8583time +20.478body +63.99 gender.

The coefficients are positive, implying that there is a positive association between the response variable and the explanatory variables. The coefficient for years of experience is 7.77, which implies that an increase by one year of experience will result in a rise in the income of the player by 7,770 dollars. A unit increase in the number of games played will lead to a rise in the income of the player by 2083 dollars. The coefficient for hours practiced is 2858.30, which implies that an increase by hours practiced will result in a rise in the income of the player by 2858.30 dollars. The body types and gender have a significant correlation with income. 

Conclusion

The study examined the effect of experience, games played, hours practiced, body type and gender on the income of a tennis player. The results from the analysis suggested that there is a significant association linking the predictors and response variables. The relationship between the variables is positive, implying that an increase on a variable will result to rise in the other variable. The number of games played was insignificant, therefore it cannot be used in the prediction of the income of the player. The whole model is significant; thus, it will be used to model the association between the income of the player and predictor variables. Therefore, the player should focus on increasing the experience, increase the hours of training and have a good body type.

References

Beaton, A., Thompson, S., & Kutz, S. (2014). No 1 vs No 32: how much does the 32nd Player Make in Tennis and Other Sports?. Retrieved from http://graphics.wsj.com/usopen-32 on 10 October 2015.

Capranica, L., Piacentini, M. F., Halson, S., Myburgh, K. H., Ogasawara, E., & Millard-Stafford, M. (2013). The gender gap in sport performance: equity influences equality.  International journal of sports physiology and performance ,  8 (1), 99-103.

Case, K., & Fair, R. (2007). Principles of Economics. Pearson Education, New Jersey. 

De Borger, B., Van Poeck, A., Bouckaert, J., & De Graeve, D. (2015). Algemene economie. Uitgeverij De Boeck, Antwerpen.

International Player Identification Number (2020). https://www.itftennis.com/en/about-us/organisation/about-ipin/

ITF. (2014). ITF Pro Circuit Review Stage One: Data Analyis. Retrieved from http:// www.itftennis.com/media/194256/194256.pdf on 22 October 2015. 

ITF. (2016). About pro circuit. Retrieved from http://www.itftennis.com/procircuit/about-procircuit/overview.aspx on 22 July 2016.

Kutz, S. (2014). How Tennis's Pay Gap Compares to Other Sports. Retrieved from http:// www.wsj.com/articles/how-tenniss-pay-gap-compares-to-other-sports-1408997701 on 22 July 2016 .

WTA. (2016). Tournaments. Retrieved from http://www.wtatennis.com/tournaments