Replication Data for: Do NBA Player (Regularized) Adjusted Plus Minus Measures Need Further Adjustment? Tests toward an Adjusted, Adjusted Plus Minus Measure

Adjusted plus minus (APM) measures have redefined our understanding of player value in basketball and hockey, where both are team games featuring player productivity spillovers (see, e.g., Horrace et al., 2016). APM measures use (substantial lineup variation in) seasonal play-by-play data to estimate individual player contributions. If a team's overall score margin success is figuratively represented by a pie, APM measures are well-designed to slice the pie and attribute individual contributions accordingly. However, they do not account for the possibility that better players can increase the overall size of the pie and thus increase the size of the slice (overall APM value) for teammates. Herein, we use NBA player-season Real Plus Minus (RPM) data for all recorded player-seasons from 2013-19 and player lineup data to test whether RPM is related to teammate quality. We run sets of linear, fixed effect regression models to explain variation in RPM across player-seasons. We find strong evidence that RPM is related to on-court teammate quality. We run the same analysis using a neural network approach. By comparing root mean squared errors for the fixed effects linear model and the neural network, we confirm that the latter approach is more suited for the present estimation.