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Measuring individual worker output in a complementary team setting: Does regularized adjusted plus minus isolate individual NBA player contributions?

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  • Shankar Ghimire
  • Justin A Ehrlich
  • Shane D Sanders

Abstract

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. APM measures use 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 data from NBA player-season Real Plus Minus (RPM)—a leading APM measure—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 also employ a two-stage least square (2-SLS) method for robustness check. Both empirical approaches address potential endogeneity in the relationship of interest. We find strong evidence that RPM is related to on-court teammate quality. Despite adjusting for teammate and opponent quality, RPM does not control for complementarity effects. As such, RPM is not suited for out-of-sample prediction.

Suggested Citation

  • Shankar Ghimire & Justin A Ehrlich & Shane D Sanders, 2020. "Measuring individual worker output in a complementary team setting: Does regularized adjusted plus minus isolate individual NBA player contributions?," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-11, August.
    • Handle: RePEc:plo:pone00:0237920
    DOI: 10.1371/journal.pone.0237920
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    References listed on IDEAS

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    1. Hansen, Henrik & Tarp, Finn, 2001. "Aid and growth regressions," Journal of Development Economics, Elsevier, vol. 64(2), pages 547-570, April.
    2. Macdonald Brian, 2012. "Adjusted Plus-Minus for NHL Players using Ridge Regression with Goals, Shots, Fenwick, and Corsi," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(3), pages 1-24, October.
    3. Horrace, William C. & Liu, Xiaodong & Patacchini, Eleonora, 2016. "Endogenous network production functions with selectivity," Journal of Econometrics, Elsevier, vol. 190(2), pages 222-232.
    4. Macdonald Brian, 2011. "A Regression-Based Adjusted Plus-Minus Statistic for NHL Players," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-31, July.
    5. Ehrlich Justin & Sanders Shane & Boudreaux Christopher J., 2019. "The relative wages of offense and defense in the NBA: a setting for win-maximization arbitrage?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 213-224, September.
    6. Fearnhead Paul & Taylor Benjamin Matthew, 2011. "On Estimating the Ability of NBA Players," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-18, July.
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    Cited by:

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    2. Ashby, Nathan J. & Ramos, Miguel A., 2023. "Productivity within groups: An analysis of shirking in high school cross country competitions," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 106(C).

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