A Comparative Study of the Indicators of Success on the PGA Tour: A Panel Data Analysis Authors: Amarendra Sharma, Patrick Reilly Elmira College

Introduction Increase in the use of statistics in sports. Main golf statistics of interest- The drive (driving accuracy and driving distance) The approach (greens-in-regulation) The putt (average putts per round) “Slow and steady loses the race?” The need for different variables. Purpose- To study the predictability of scoring, earnings, and top ten performances among golfers on the PGA Tour.

Literature Review Peters (2008) Rinehart (2009) Watkins, Jr. (2008) McHale and Forrest (2005) Fried and Tauer (2011) Heiny (2008) Connolly and Rendleman, Jr. (2008) Coate and Goldbaum (2006) Ehrenberg and Bognanno (1990) Shmanske (1992)

Fried and Tauer (2011) Estimating “mental toughness” Effects of age

Heiny (2008) Effects of technology Increase in driving distance Importance of driving accuracy found to be diminishing Putting and greens in regulation still dominate the forecasts.

Data Data set includes 20 professional golfers over a 13 year period from Variables Scoring average (SA) Top 10 finishes per event (T10/E) Earnings per event (E/E) Driving distance (DD) Driving accuracy (DA) Greens in regulation (GIR) Putts per round (P/R)

Data (continued) VariablesMeanStd. DeviationMinimumMaximum T10/E E/E SA DD DA GIR P/R

Econometric Model Three equations

Additionally, Interact driving distance and driving accuracy to form..

Hypotheses/Expected Signs X VariablesScoring AverageEarnings/EventTop 10/Event DD-++ DA-++ GIR-++ P/R+-- DD*DA-++

Scoring Average Results VariablesPooled OLSFixed EffectsRandom Effects DD-0.05* (0.005)(0.006) DA-0.03* (0.008)() GIR-0.1* (0.06) P/R0.7*0.65*0.06* (2.05)(2.5)(2.3) Intercept73.2*75*74* (2.05)(2.5)(2.3) Overall R- Squared Adj. R-Squared0.614 * Denotes significance at 1%. Robust standard errors are provided in parentheses.

Earnings per Event Results VariablesPooled OLSFixed EffectsRandom Effects DD5904*4393*48999* (973)(1054)(999) DA (1551)(1529)(1502) GIR * * * (2203)(2094)(2081.7) P/R * * * ( )( )(11031) Intercept ( )( )( ) Overall R-Squared Adj. R-Squared * Denotes significance at 1%. Robust standard errors are provided in parentheses.

Top Ten Finishes per Event Results VariablesPooled OLSFixed EffectsRandom Effects DD0.01*0.008*0.009* (0.001)(0.002)(0.001) DA0.005** (0.002) GIR0.03* (0.003) P/R-0.186*-0.16*-0.17* (0.01)(0.02) Intercept (0.5)(0.6) Overall R-Squared Adj. R-Squared0.59 * Denotes significance at 1%. ** Denotes significance at 5%. Robust standard errors are provided in parentheses.

Scoring Average with Interaction Term Results VariablesPooled OLSFixed EffectsRandom Effects DDDA ( )( )( ) GIR-0.15*-0.13*-0.14* (0.012)(0.02) P/R0.58* (0.08)(0.11)(0.09) Intercept63.3*62.4*62.6* (2.17)(3.32)(2.42) Overall R-Squared Adj. R-Squared * Denotes significance at 1%. Robust standard errors are provided in parentheses.

Earnings per Event with Interaction Term Results VariablesPooled OLSFixed EffectsRandom Effects DDDA-18.9* (7.35)(5.8)(8.3) GIR * * * (4183.8)(3433.3)(3287.8) P/R ( )( )( ) Intercept * *770699* ( )( )( ) Overall R-Squared Adj. R-Squared * Denotes significance at 1%. Robust standard errors are provided in parentheses.

Top Ten Finishes with Interaction Term Results VariablesPooled OLSFixed EffectsRandom Effects DDDA ( )( ) GIR0.04*0.03* (0.004)(0.006)(0.005) P/R-0.16*-0.15* (0.02)(0.03)(0.02) Intercept2.36*2.3* (0.5)(0.77)(0.56) Overall R-Squared Adj. R-Squared * Denotes significance at 1%. Robust standard errors are provided in parentheses.

Conclusion Putting and approach ability strongly affect scoring, earnings, and top 10 finishes on the PGA Tour. Top 10 finishes per event is a better measure of success than earnings per event. Unique perspectives- Shows the importance of top 10 finishes. The only paper that exploits the panel nature of the data that allows us to control for the player specific heterogeneities in a dynamic setting.

References Coate, D. and Goldbaum, D. (2006), Skills, effort, and performance in tournaments: A dynamic model and empirical analysis. (Working Paper Rutgers University # ). Connolly, R. A. and Rendleman, R. J. Jr. (2008), Skill, luck and hot hands on the PGA tour, Journal of the American Statistical Association, 103(481): doi: / Ehrenberg, R. G. and Bognanno, M. L. (1990), The Incentive Effects of Tournaments Revisited: Evidence from the European PGA Tour, Industrial and Labor Relations Review, 43(3), 74S-88S. Fried, H.O. and Tauer, L.W. (2011), The impact of age on the ability to perform under pressure: golfers on the PGA tour, Journal of Productivity Analysis, 35: 51 – 59. Heiny, E. L. (2008), PGA tour pro: Long but not so straight, Chance, 21(1): doi: / McHale, I. and Forrest, D. (2005). The importance of recent scores in a forecasting model for professional golf tournaments, IMA Journal of Management Mathematics, 16: doi: //. Peters, A. (2008). Determinants of performance on the PGA Tour, Issues in the Political Economy, 17. Rinehart, K. L. (2009). The economics of golf: An investigation of the returns to skill of PGA tour golfers, Major Themes in Economics, Shmanske, S. (1992). Human capital formation in professional sports: evidence from the PGA tour, Atlantic Economic Journal, 20(3): Watkins, J. R., Jr. (2008). Drive for show, putt for dough: Rates of return to golf skills, events played, and age on the PGA tour, Michigan Journal of Business. 1:

Questions 1.Given the format of winnings in professional golf tournaments, how could it be beneficial to be a streaky player? 2.Does the data show that it is always better to hit more fairways, even when it is at the expense of distance? 3.What are the two main variables that consistently impact scoring, earnings, and top 10 finishes on the PGA Tour? 4.One would expect that earnings per event and top 10 finishes per event would be able to predict success on the PGA Tour to about the same degree. Does the data show that one is better than the other?