Athletes Endorsement Earnings - Statistics Project Example

Athletes endorsement earnings The objective of the study was to find out whether the salary and bonuses paid to theathletes by clubs had an effect on the endosements paid to them by the corporations like Motorola, Nike, Adidas and others. In the literature review it outlines the some of contributions in theoretical and emprical reviews of the relationships of the variables used in the research. The population set, is gathered from various sources showing income earned between June 2011 to June 2013 period by the athletes, though not 100% accurate. The data collected was statistically analysed using Excel and findings were tabulated and illustrated with charts. Acknowledgments .To my lecture, for the maximum support he/she given to me during this course. And my friends who always encourage me to work hard. Table of Contents ABSTRACT 2 Acknowledgments 2 Chapter 1: INTRODUCTION 4 Chapter 2: LITERATURE REVIEW 5 Chapter 3: RESEARCH METHODOLOGY 7 Chapter 4: DATA ANALYSIS, FINDINGS AND CONCLUSIONS. 9 Chapter 5: SUMMARY OF THE FINDINGS AND CONCLUSIONS 15 APPENDIX 17 REFERENCES 19 Chapter 1: INTRODUCTION 1.1 Background of the study. This chapter gives a brief introduction of the research study by looking into the endorsements paid to athletes, salary/winnings and there age. The chapter also states the problem and explores the objectives of the study. Endorsement This is the act of endorsing a product by an an athlete, and being paid by the companies to be the brand ambassador of the product. This increases the brand portfolio and the companys image and portfolio resulting increase in financial market share. This also increases the athlete earning and leads to post-athletic opportunites. The use of the athlete as a means of brand communication has in the recent past increased, and has been tested for its effectiveness. The endorsement ranges from communication, sport drinks, cosmetics to organizations like UN. A good example is Lionel Messi, on his endorsement list is Addidas , Turkish Airline,Gillette, Pepsico and EA among others. Messi earned a total of $23 million in endorsement according to Forbes (Forbes, 2014). 1.2 Statement of the problem Today, people have become a brand by themselves and they earn through the endorsements of various brands. Most players excel in their own fields of play and they receive millions in salary contracts with the clubs or paid after winning the tournament. We sought to find out whether the pay peaks paid to the athletes from the club has an effect on overall endorsement fees, negotiated and paid to the athlete or other factors come into effect. 1.3 Aim of the study The aim of the study is to investigate where the high salary and bonuses paid to athletes has an effects to there endorsement earning. Ho = The salary/winnings earned by athletes has effects on their endorsement earnings. Ha = The salary/winnings earned by athletes has no effects on the endorsement earnings. Chapter 2: LITERATURE REVIEW 2.1 Introduction This chapter discusses a review of exisitng literature with the theories that provides the framework guide of this study. The specific areas to cover are the endorsements, athlete age and salary/winning. 2.2 Theoretical review This is the act of endorsing products by the athletes, and the athlete earns from the endorsement. The study shows that celebrities appear in one-fifth of ads, example Nike spends $ 0.5 Billion per year on endorsements; further, the studies show that the stock prices of the companies go up after the endorsement (Sager, 2011). The study shows that there is superstar effect across all the sports and that the athlete is accelerated to the top by endorsement money. With the techological advancement, fans have easy access to sports and they are distributed worldwide; this are the target audience the companies are appealing to communicate to, thus the companies are willing to spend more money to pay the best of the best players to be their brand ambassidors. Examples of endorsements earned are Lebron James $40m, Rory Mcllroy $11 and Victoria Azarenka $3m (Keating, 2013). In 1997 the TV viewing audience increased by 14% during PGA tours as a result of Tiger Woods, thus CBS rating rose up to 25% as it was holding rights to masters event. Tiger woods earned $0.8m on endorsement alone that year (Kathleen A. Farrell, 2000). 2.3 Emprirical review The salaries of the athlete depends upon very many factors including the name and personal brand recognition, kind of sport and the athlete performance, this is coupled with short careers and very limited retirement planning time (McMahon, 2014). The name and the personal brand recognition may attract athletes endorsements and apperances, this increases the athletes earning. Due to high injury factor, athletes demand high pay to mitigate the future risks of not being to play and also take care of their families in case of a permanet injury. Chapter 3: RESEARCH METHODOLOGY 3.1 Introduction The chapter outlines the methodology used to carry the out study. This involves Data defination, collection, organization, visualizing and analyzing (DCOVA). 3.2 Research design We shall use secondary data to do this study, this are the data sources obtained externally. We shall get our data from private institutions and periodicals as external data sources (Mann, 2010). The organizations that collect data are the primary source or the external source, when the data is published for others to use it becomes secondary data. This data is published in the newspapers and online blogs. These sources are filled with information on sports statistics (David M. Levine, 2010) 3.3 Population data set The population set of this study is earnings of athletes between the period June 2011 to June 2012, since we cannot find accurate information on this we shall rely on online journals. 3.4 Data Collection methods We got our secondary data from online journals , we made estimates to makesure that the data as accurate as possible. 3.5 Data Analysis First the data was be edited for accuracy, to remove biasness, make it consistency and completeness and to visualize it; it was tabulated and arranged to simplify coding. Various measure of spread was used to analyse the data collected. We used Excel to analyse our data and regress it. The regression equation (Y = β0 + β1X1 + β2X2 + ε) Whereby Y = endorsements X1 = Salary X2 = Bonus Whereby Y is the dependent variable, X1 and X2 are the predictors and independent variable. β0 is the y-intecept, β1, β2 are population parameters that detemines the directional of independent variable and ε are random variable of other parameters that are unknown (David R. Anderson, 2011). Chapter 4: DATA ANALYSIS, FINDINGS AND CONCLUSIONS. 4.1 Introduction This chapter analysis the findings of the research methodology set out in chapter 3. The data was gathered from the external source (Forbes) as tabulated in Table 1.0 in the appendix. 4.2 Demographic information Wage is the statistical study of earnings. In our case the wage information includes name of the athlete, age, salary/winnings and endorsements for the period between June 2011 to June 2012. 4.2.1 Athlete Age We sought to know the age of the athlete as recorded in the external source.   Frequency Cumulative Frequency 25 > 3 3 26 - 29 9 12 30 - 34 6 18 35 - 39 6 24 40 < 2 26 N 26 The graph above shows, 36% of the athlete sampled we below the age of 29, only 8% were above the age of 40 years. 4.2.2 Salary & Winning The study sought to study the salaries and winning earnings earned during the period. Frequency Cumulative Frequency 10 > 6 6 11 -19 2 8 20 -29 9 17 30 -39 6 23 40 - 49 1 24 50 < 2 26 N 26 The graph shows that 34% of the athlete are earned between $20 -29m. 4.3 Multiple regression anaylsis. This section we shall discuss inferential statistics. A multiple regression analysis was used to determine the importance of each of the variables and establish the effects of independent variables on the endorsements. We applied the statistical package Excel and the findings are as below. SUMMARY OUTPUT Force Constant to Zero FALSE Regression Statistics   Multiple R 0.668 R Square 0.446 Goodness of Fit < 0.80 Adjusted R Square 0.398 Standard Error 13.384 Observations 26 Goodness to fit < 0.80 or 80% , thus reject Reject H0 if p-value ≤ α, where α is the level of significance for the test (David R. Anderson, 2011). Thus p-value ≤ 0.001 , thus the null hypothesis is rejected. R2 = 44.6% of variability in endorsement is low, an indication of no influence from the other variables. ANOVA   df SS MS F P-value Regression 2 3313.699 1656.850 9.249 0.001 Residual 23 4120.303 179.144 Total 25 7434.002       F-value is 9.249 > 0.356 at 95% confidence level, thus we reject the null hypothesis on indcation that the other expected values differ when p-value < 0.001. Confidence Level 0.95 0.99   Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 99% Upper 99% Intercept 5.929 16.654 0.356 0.725 -28.523 40.380 -40.825 52.682 Salary & Winnings -0.609 0.152 -4.005 0.001 -0.923 -0.294 -1.035 -0.182 Age 0.830 0.513 1.618 0.119 -0.231 1.891 -0.610 2.270 y = 5.929 -0.609*Salary & Winnings +0.83*Age There is a high negative correlation between the variable endorsement and salar/winning, an indication that there is no relationship between the two. Interpretation The regression equation Y = β0+ β1X1 + β2X2+ β3X3 + Ɛi y = 5.929 -0.609*Salary & Winnings +0.83*Age The random error Ɛi, are the other factors that are directly influencing the the variable endorsements The positive coefficient and the negative coefficient shows the effect of the predictors on the endorsement variable. An increase in salary & winnings results to a decline in endorsements, and as the athlete gets older the endorsements increases i.e as the age increases. Further, p-value < 0.001 and t > 0.356, show the null hypothesis is invalid and therefore is rejected. The regression errors are normally distributed thus, the two sided of the confidence interval 100 ( 1- α )% will be -28.523 ≤ βj ≤ 40.380 (Paul Newbold, 2013). At 95% confidence level (-28.523, 40.380), this are plausible values of parameter where mean may lie; thus, the Xi may be zero. Therefore salary and winnings don’t impact the endorsement earnings. Chapter 5: SUMMARY OF THE FINDINGS AND CONCLUSIONS 5.1 Introduction This chapter gives a summary of the findings and conclusion based on the study of chapter four. 5.2 Summary of the findings This section gives a conclusive summaries of endorsements, Athlete Age and Salary & winnings. 5.2.1 Endorsements The endorsement fees are influenced by age and other factors that have not been studied in this report. The fees earned by the athlete, translates to player age and achivements on and off the field. 5.2.2 Athlete age As the athlete ages the endorsement fees increases showing that most companies trust their brands with more established personal brand. Building personal brand comes with age and performance of the athlete. 5.2.3 Salary & Winnings The salary and winnings earned do not influence the companies to pay more on endorsement fees. 5.3 Conclusion For an athlete to earn more in endorsements it’s a must to build a personal brand and age comes in as a factor. Companies pay endorsement fees are not influenced by the players salary/ winning, rather by other factors and performance of the player; thus if consumers tend to associate with the athlete the endorsement fees rises. APPENDIX Table 1.0 Earning between June 2011 - June 2012 Name Endorsements Salary & Winnings Age Floyd Mayweather - 85.0 35 Manny Pacquaio 6.0 56.0 33 Tiger Woods 55.0 4.4 36 LeBron James 40.0 13.0 27 Roger Federer 45.0 7.7 30 Kobe Bryant 32.0 20.3 33 Phil Mickelson 43.0 4.8 41 David Beckham 37.0 9.0 37 Cristiano Ronaldo 22.0 20.5 27 Peyton Manning 32.4 42.4 36 Lionel Messi 19.0 20.0 24 Haloti Ngata 0.2 37.1 28 Larry Fitzgerald 1.5 35.3 29 Ndamukong Suh 0.5 35.5 25 Charles Johnson 0.1 34.3 26 Rafael Nadal 25.0 8.2 26 Mario Williams 0.3 32.9 27 Alex Rodriguez 2.0 31.0 36 Fernando Alonso 3.0 29.0 31 Valentino Rossi 13.0 17.0 33 Michael Schumacher 10.0 20.0 43 Darrelle Revis 1.3 27.0 27 Wladimir Klitschko 4.0 24.0 36 Lewis Hamilton 3.0 25.0 27 Maria Sharapova 22.0 5.9 25 Tom Brady 4.0 23.1 34 (Forbes) REFERENCES David M. Levine, D. F. (2010). Statistics for Managers: Using Microsoft Excel. Upper Saddle River: Pearson. David R. Anderson, D. J. (2011). Statistics for Business and Economics. Mason: Cengage. Forbes. (n.d.). Retrieved from Forbes: www.forbes.com Forbes. (2014, June). Lionel Messi. Retrieved from Forbes: http://www.forbes.com/profile/lionel-messi/ Kathleen A. Farrell, G. V. (2000). Managerial Finance. Celebrity performance and endorsement value: the case of Tiger Woods, 1-15. Retrieved from http://www.emeraldinsight.com/action/showCitFormats?doi=10.1108%2F03074350010766756 Keating, P. (2013, June 11). Espn MLB. Retrieved from ESPN: http://espn.go.com/mlb/story/_/id/9360858/race-sport-gender-determines-how-much-athletes-make-endorsements-espn-magazine Mann, P. S. (2010). Introductory Statistics. Haboken: John Wiley & Sons. McMahon, M. (2014, October 28). What Factors Affect Professional Athlete Salaries? Retrieved from Conjecture Corporation: http://www.wisegeekedu.com/what-factors-affect-professional-athlete-salaries.htm#didyouknowout Paul Newbold, W. L. (2013). Statistics for Business and Economics. Upper Saddle River: Prentice Hall. Sager, R. (2011, May 21). Smartmoney. Retrieved from Market Watch: http://www.marketwatch.com/story/do-celebrity-endorsements-work-1300481444531 Read More