  The National Basketball Association (NBA) imposes a player salary cap. It is
therefore useful to develop tools to measure the relative realized return of a
player's salary given their on court performance. Very few such studies exist,
however. We thus present the first known framework to estimate a return on
investment (ROI) for NBA player contracts. The framework operates in five
parts: (1) decide on a measurement time horizon, such as the standard 82-game
NBA regular season; (2) calculate the novel game contribution percentage (GCP)
measure we propose, which is a single game summary statistic that sums to unity
for each competing team and is comprised of traditional, playtype, hustle, box
outs, defensive, tracking, and rebounding per game NBA statistics; (3) estimate
the single game value (SGV) of each regular season NBA game using a standard
currency conversion calculation; (4) multiply the SGV by the vector of realized
GCPs to obtain a series of realized per-player single season cash flows; and
(5) use the player salary as an initial investment to perform the traditional
ROI calculation. We illustrate our framework by compiling a novel, sharable
dataset of per game GCP statistics and salaries for the 2022-2023 NBA regular
season. A scatter plot of ROI by salary for all players is presented, including
the top and bottom 50 performers. Notably, missed games are treated as defaults
because GCP is a per game metric. This allows for break-even calculations
between high-performing players with frequent missed games and average
performers with few missed games, which we demonstrate with a comparison of the
2023 NBA regular seasons of Anthony Davis and Brook Lopez. We conclude by
suggesting uses of our framework, discussing its flexibility through
customization, and outlining potential future improvements.
