Game, Set, Money!Department of Economics/Finance

Meagan Jackson

Ali Ballard

Heather Brantley

AbstractThis study estimates the determinants of career prize money for the top 100 Women’s Tennis Association (WTA) players in 2009.  We use various measures of performance and physical attributes to analyze whether these variables can explain the variation in women’s career prize money.  In addition to total wins, other important determinants of career prize money include number of years in the WTA, height, nationality, and whether the player is right-handed. For every ten wins on the tour, a player’s career salary will increase approximately five percent. Each year in the WTA will increase a player’s career salary by six percent. Each additional inch in height will increase salary by roughly 5 percent. While a player from the United States does not earn significantly more or less than a Russian player, players of non-American origin earn approximately 28 percent less than a Russian player. Finally, right-handed players earn 33 percent more than left-handed players.

VariablesIndependent VariablesTotal Wins: All registered single matches played over the course of a player’s career Years Pro: Total number of years the player has been in the WTAHeight: Player’s height in inchesUS: Refers to a player’s nationalityOther: Players of non-American and non-Russian nationalityRight: Indicates whether the player is right handedDependent VariableLog of Career Prize Money: The log of money earned through match winnings

Variable Standard Deviation

Mean Maximum Minimum

Log of Career Prize Money

$4,596,906 $3,261,129 $26,547,893 $209,511

Total Wins 106.20 261.96 550.00 88.00

Age 4.38 24.45 38.00 18.00

Years Pro 3.83 9.10 18.00 2.00

Height 2.69 68.04 75.00 61.50

DataEquation:Log of Career Prize Money = f(Total Wins, Years Pro, Height, US, Other, Right)

Table 1 Descriptive Statistics

Table 2 Regression ResultsConclusion/Results

We collected data on various factors we believed were relevant to a player’s financial success and the results suggest that many of the coefficients on these variables were in fact statistically significant. Since the Career Prize Money dependent variable was not normally distributed, we decided to use a log transformation to normalize the data. Total Wins, Years Pro, Height, Nationality, and whether the player is Right-handed were all significant determinants of Career Prize Money. While 2009 saw a surge of Russian women finishing in the top 100, they did not earn significantly more prize money than American players. Future research would expand the number of observations to include the top 100 players from 2000-2009 and create a dependent variable that includes both career prize money and money from sponsorships.

References: http://sports.espn.go.com/sports/tennis/history?type=women, http://www.tennis-x.com/rankings.php

Evaluating the Statistical Significance of Estimated Coefficients

P-values are a measure of statistical significance. If the observed significance level is less than the chosen significance level (alpha), then the researcher should reject the null hypothesis in favor of the alternative.  Otherwise, there is not enough evidence to reject the null hypothesis. For instance, if a P-value for the estimated coefficient of a variable is 0.0012, this means that there is only a 12 in 10,000 chance that the true coefficient of price is actually 0. The lower the P-value for an estimated coefficient, the more confident you are in the estimate. In our paper, P-values of 0.10 or lower are considered low enough for us to be confident that the estimated coefficient is statistically significant. If the P-value is 0.05, we say that the estimated coefficient is statistically significant at the 5 percent level.

Variable Coefficient Standard Error P-Value

Total Wins 0.005 0.001 <.0001

Years Pro 0.061 0.022 0.007

Height 0.047 0.021 0.028

US -0.135 0.208 0.518

Other -0.283 0.154 0.069

Right 0.335 0.190 0.082

Adj. R2 = 0.63N = 100