Deciphering the drivers and trends of an NBA player

The study tests the relative importance of different player metrics in determining an NBA player’s salary. Using each year’s data of every league athlete,a dataset comprising 8798 unique player rows and 26 distinct independent variables was compiled. Multi-variable linear and polynomial regression models were constructed using Ordinary Least Squares (OLS) estimation. Points Scored per game and Age turned outto be the most dominant factors in the polynomial regression model which had the highest adjusted R2 of 0.72. Game’s evolution and salary trends were also analysed from perspective of a player’s playing position and lifecycle. Over the study period, an NBA game has clearly turned more offensive and higher scoring due to sharp rise in 3-Point shots leadingto greater value in the team for forward players. A player’s golden age for earning is between the age of 30-34 when he earns the maximum value per point scored. Average player salary growth has also beaten inflation by a wide margin in the study period.

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