Team Performance with Test Scores

TL;DR

This paper investigates how different testing strategies for individual team members impact the ability to assemble high-performing teams, proposing new tests that better predict team success.

Contribution

It introduces a family of tests measuring individual potential that can produce near-optimal teams for certain performance measures, advancing team selection theory.

Findings

Tests based on individual potential can approximate optimal team performance.

Certain submodular and supermodular performance functions resist effective individual testing.

Implications for submodular maximization methods like hill-climbing are discussed.

Abstract

Team performance is a ubiquitous area of inquiry in the social sciences, and it motivates the problem of team selection -- choosing the members of a team for maximum performance. Influential work of Hong and Page has argued that testing individuals in isolation and then assembling the highest-scoring ones into a team is not an effective method for team selection. For a broad class of performance measures, based on the expected maximum of random variables representing individual candidates, we show that tests directly measuring individual performance are indeed ineffective, but that a more subtle family of tests used in isolation can provide a constant-factor approximation for team performance. These new tests measure the "potential" of individuals, in a precise sense, rather than performance, to our knowledge they represent the first time that individual tests have been shown to produce near-optimal teams for a non-trivial team performance measure. We also show families of subdmodular and supermodular team performance functions for which no test applied to individuals can produce near-optimal teams, and discuss implications for submodular maximization via hill-climbing.