1. Returns to Skill in Professional Golf Leo H. Kahne International Journal of Sport Finance, 2010 A Quantile Regression Approach

2. Overview  Scully’s paper was the first to link player skills with compensation in MLB  Spurred a number of golf related studies  There are a host of papers studying the returns to skill in professional golf  Focus on the importance of driving distance, iron play, greens in regulation and putting  Past studies have determined that importance of driving has increased but putting remains the most important skill  Most recent studies prior to 2010 focus on the fact that skill does not equal earnings  Skill=Score=Rank=Earnings

3. Purpose  To examine the linkage between professional golfers’ earnings and their skills with the use of quantile regression with data from 2004-2007  Past studies did not account for the highly positively skewed distribution of earnings  Small group of highly talented golfers  Tiger Woods, Vijay Singh, Phil Mickelson – 49/135 first place wins in ‘04-’07 (36%)  Nonlinear payout structure  Distribution of earnings:  First place: 18% of purse  Second place: 10.8% of purse  Third place: 6.8% of purse  Fourth place: 4.8% of purse

4. cbssports.com

5. Model  Past models used simple conditional mean regression estimation (OLS – Ordinary Least-Squares)  Causes difficulties due to non normality of the errors  New Model: Quantile Regression  Can provide more understanding to the effects of various covariates on earnings  Model:  Yi – dependent variable, real earnings per PGA event  xi – vector containing five variables expected to explain golf earnings  B – vector of coefficients to be estimated  Ei – error term  q – specific quantile associated with the equation (5 quantiles)

6. Model, cont.  Five Variables (vector xi)  Greens in Regulation  Positive coefficient expected  Putting Average  Negative coefficient expected  Save Percentage (Up & Downs)  Positive coefficient expected  Yards per Drive  Positive coefficient expected  Driving Accuracy  Positive coefficient expected  Also measures experience, height and weight

7. Method  Took summary of data to prove that methods such as OLS can be influenced by outlier effects  Tiger Woods – he greatly affected every estimated coefficent except Driving Accuracy  Looking to get a more realistic estimation for the “typical” professional golfer

8. Method  Took the OLS results and compared them to five different quantiles:  q10 (lowest earnings)  q25  q50  q75  q90 (highest earnings)

9. Results  All five variables had expected coefficient signs, except driving accuracy  Negative coefficient could be attributed to tradeoff between driving accuracy and distance  Estimated coefficients for Greens in Regulation, Putting Average & Save Percentage most significant at 1% level  Estimated coefficients for Yards per Drive is more statistically significant at the 10 th and 25 th quantile regressions and then becomes less significant  Driving Distance is more important for those on the lower end of earnings

10. Results, cont.  From OLS results, we see that an increase in one percentage point on greens in regulation, increases earnings by $7,485  In Quantile Regression results, we see that an increase in one percentage point in GIR increases earnings by $4,111  Due to skewness and outliers in earnings measure  The coefficient becomes more significant as we move from the lower quantiles to the higher quantiles, implying that an increase in greens in regulation has a greater positive affect on expected earnings for the better golfers

11. Results, cont.  OLS results predict an increase in earnings by $700,082 for reducing putting average by one stroke  Quantile regression predicts a $374,041increase for reducing putting average by one stroke  If golfers in the 10 th quantile of earnings decrease average by one stroke, expected earnings will increase by $182,000  If golfers in the 90 th quantile of earnings decrease average by one stroke, expected earnings will increase by $717,000  Again, due to nonlinear payout structure

12. Results, cont.  This conditional quantile regression takes it a step further and will help professionals realize not just what is important, but which important aspects are the best for them to focus on given their pay rank  For example, a golfer in the 25 quantile can increase earnings by $5,739 by reducing putting average by one standard deviation, which is approximately a 32% increase in earnings per event  A golfer in the 75 quantile will see approximately a $9,973 increase by reducing putting average by one standard deviation, however this is only a 16% increase in earnings per event

13. Thoughts  ‘04-’07 did not provide the most interesting data due to the strong outliers, most specifically Tiger Woods  Would be interesting to see the results now with the huge amount of upcoming talent and new winners almost every week  Would also be interesting to see how skill and tournament earnings affect sponsorship earnings per player