Accounting for Complementary Skill Sets: Evaluating Individual Marginal Value to a Team in the National Basketball Association

Abstract

Identifying an individual worker's contribution to firm production can be difficult in a team setting where spillovers in labor productivity exist among team members. This paper studies a model of labor productivity where workers have heterogeneous abilities, and can differently affect the productivity of their fellow teammates. Applying the model to the setting of the National Basketball Association (NBA), I can identify the marginal value that a basketball player brings to a particular team lineup, both through his own individual contributions and his complementary contribution to teammates' productivity. Estimates from the model imply that teammates have a significant impact on individual player productivity, and that taking into account spillovers across teammates is important to assessing both overall team productivity and an individual player's contribution to team productivity. I then evaluate whether player complementarities are valued in the NBA labor market in terms of higher salaries, and find that they are undervalued, and that players are instead paid mainly for their individual offensive production. This creates an asymmetry between player incentives and the team objective. To assess the size of this inefficiency, the top trading cycle algorithm of Shapley and Scarf (1974) is used to identify a Pareto optimal matching between players and teams, that accounts for the complementarities between heterogeneous players' skill sets. (JEL J30, L25, L83, M51)

Predicting the winning team in basketball: A novel approach

“…Following a slightly similar approach to Lutz (2012) , Oh et al. (2015), and Kuehn (2017) , we identify different player stereotypes, completely agnostic of how players are labelled according to the 5 traditional positions in basketball. By identifying the different playing styles across the league, we aim to capture how well certain team configurations work by generating positive synergies, leading to more wins.…”

“… Kuehn (2017) uses a probabilistic model to identify how players complement or inhibit each other and models complementary skill sets. In this study, Kuehn models each possession within a game as an event tree, capturing potential outcomes.…”

The Importance of Team Fit for NBA Rookies’ Career Earnings

“…The existence of peer effects in the NBA and other sports settings, has long been recognized. Arcidiacono et al (2017) and Kuehn (2017) both find that teammate productivity has little effect on future salary, but neither focus on the the impact that teammates have on salary through their effect on individual productivity. Kendall (2003) looks specifically at this interaction and finds that more productive teammates (measured as points per field goal attempt) lead to increases in own productivity.…”

“…This data has each play from all NBA games from 2014-2015 to 2018-2019. We then measure player productivity using the methodology developed by Kuehn (2017). Kuehn (2017)’s evaluation technique allows us to separate an individual player’s contribution to his team from that of his teammates.…”

Peer interactions and performance in a high‐skilled labour market*

“…Defining attainment is not easy in sport, especially in team sports such as basketball. The marginal contribution of a player in a team setting is the result of complex dynamics, including productivity spillovers between teammates (Kuehn, 2017). A player can have a strong impact on his team, even though his individual statistics fail to capture that contribution (Oliver, 2004).…”