A team is optimal when it delivers superior performance relative to a set objective. The position of an individual in the team and the overall team mix is essentially an optimization problem given team member characteristics, their chemistry, and an objective function plus a set of constraints. The less constrained the optimization problem, the more optimal the solution, all else equal.

Traditionally, team formation has not been seen as an optimization problem with an analytic solution but rather as a mostly arbitrary composition of individuals. Simple combinatorics provides ample evidence of how intractable the problem of team formation is. There are over 2 million possible solutions to forming a team of five from a population of 50 people. Not all of those 2 million solutions will perform equally well, and a random selection will only produce mediocre results. As McKinsey puts it: “Innovation is a team sport. For projects to succeed, they must be staffed with the right combination of talent.”

The critical question for optimal teamwork is how roles mesh together into a team that has complementary strengths. Advances in the field of sports pave the way to more data-driven and more evidence-based decision making when it comes to team formation. Like in sports, data about the relevant characteristics of individuals are required, and profiles need to be assessed.

An additional complicating factor is that individual team members are usually assigned to multiple projects simultaneously, an organizational form that is referred to as multi-teaming. It allows projects to share individuals’ time and brainpower across functional and departmental lines. For it to work effectively, an organization must map everyone’s skills, manage time across teams and competing priorities, create a learning environment, and, critically, introduce the optimal amount of slack so that the organization is not overcommitted continuously.