Player Benchmarking and Outcomes: A Behavioral Science Approach

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Ambra Mazzelli, MIT Sloan and Asia School of Business
Robert S. Nason, Concordia University


We draw on a rich behavioral science tradition to infuse theoretical grounding to NBA player benchmarking by examining how performance feedback impacts player outcomes. We contend that choice of benchmark (i.e. self, team average, or peer rivals) not only impacts assessment (i.e. is a player under- or over-performing), but also how performance feedback is interpreted by players and thus manifests into subsequent player outcomes (i.e. risk taking, errors, and +/-). Performance relative to a player’s own past performance (self) is likely to be attributed to effort while performance relative to team average is attributed to social standing, and performance vis-a-vis rival peers to ability. As a result, responses to performance feedback will depend on the valence of performance feedback (i.e. over or underperforming). In particular, we suggest that while performance feedback from self comparison increases motivation when underperforming and decreases motivation when overperforming, the opposite is true for performance feedback involving social comparisons – players will feel demotivated when underperforming and motivated when outperforming their social referents. In support of our arguments, we find empirical support that self underperformance increases subsequent risk-taking, errors, and +/-, while self overperformance feedback has no effect on risk-taking, errors, and +/-. In contrast, outperforming team average increases subsequent risk-taking, errors, and +/- while underperforming team average reduces subsequent risk-taking, errors, and +/-.  These findings have important implications for when and how to share analytics with players. In particular, we develop a practical tool for providing performance feedback selectively, depending on referent and desired performance change.