Determinants of Professional Golf Earnings – An Empirical Analysis of The PGA Tour Using Machine Learning Methods

Maximizing financial success on the PGA Tour depends on a complex set of physical and mental skills, with evolving demands over time. This case study applies data science techniques to evaluate the determinants of player earnings, developing regression-based models using both traditional golf statistics and modern Strokes Gained metrics. Utilizing a comprehensive panel dataset covering the 2004 to 2021 PGA Tour seasons, the study confirms the enduring importance of putting performance while documenting the growing, though still secondary, influence of driving distance. The analysis also identifies modest temporal shifts in skill importance, with ball-striking skills becoming slightly less differentiating and putting becoming increasingly critical. Findings offer actionable insights for players, coaches, and analysts, reinforcing the need for a balanced skillset and targeted performance development. This study demonstrates the value of applied data science methods for extracting strategic insights from complex sports data and highlights how evolving metrics like Strokes Gained can enhance performance evaluation in professional golf.