‘We know,’ Runciter said to GG, ‘that as individuals they perform well. It’s all down here on paper.’ He rattled the documents on his desk. ‘But how about together? How great a polyencephalic counter-field will they generate together? Ask yourself that, GG. That is the question to ask.’
– From Ubik (1969) by Philip K Dick
In Philip K Dick’s classic science fiction novel Ubik, one of the main characters, Runciter, is in charge of assembling a team of individuals called ‘inertials’. The hope is that they will counteract the power of ‘precogs’ and ‘telepaths’, recruited by corporations to carry out espionage and other nefarious activities. Each inertial is a superstar with a unique talent – but Runciter’s concern is their collective power.
Interest in collective behaviour is not new. It’s been the research subject of organisation scholars, anthropologists, economists, ethologists studying group-living animals and evolutionary biologists interested in the evolution of cooperation. And, of course, it’s the chief occupation of coaches and managers building teams across a wide range of sports. Although many of us believe a team is more than just the sum of its outstanding individual performers, this kind of simple-minded thinking still dominates recruitment and team assembly in sports, finance, academia and other settings.
Part of the reason why recruiters and others resort to going after the best players rather than building the best team is that it remains unclear what other factors contribute to team greatness, and how to quantify them. Moreover, simply recruiting the best players is fairly straightforward, and some analyses suggest this approach might even be the most reliable: as the sociologist Duncan Watts and colleagues argued, overall talent level is often the single best predictor of team performance. Yet we shouldn’t be lured into thinking overall talent is the best predictor because it is the most important factor. It might be the best predictor because we’re not yet good at capturing the nuance of collective dynamics. Hints that this could be the case come from studies such as that of the management scholar Satyam Mukherjee and colleagues, in which they found that prior shared success can predict performance above and beyond what would be expected from the group’s composition and talent.
These seemingly at-odds results raise the question: how does a collective work exactly? When is it more than the sum of its parts? The increasing availability of data on individual decision-making across the social sciences, coupled with how complexity science is improving our understanding of the mechanics of group performance, are changing what’s possible. Some of the questions that can now be answered include how a team synchronises, when contributions are synergistic as opposed to additive, and whether it’s the players’ skill or the strategies they use that’s more important. Before we get to promising future directions, though, it’s worth considering the existing space of ideas about what makes a good team, as well as some scenarios suggesting greater nuance is required.
In his book The Captain Class (2017), Sam Walker – deputy editor for enterprise at The Wall Street Journal – argues that a key to team performance is leadership, defined not by charisma but by the ability to resolve conflict and improve morale behind the scenes. The anthropologist Ruth Benedict proposed culture as a factor in human performance, writing in Patterns of Culture (1934):
No individual can arrive even at the threshold of his potentialities without a culture in which he participates. Conversely, no civilisation has in it any element which in the last analysis is not the contribution of an individual.
Michael Lombardi, a former executive at the US National Football League, echoes Benedict’s point in his book Gridiron Genius (2018). Lombardi proposes that the New England Patriots’ dynasty, and the success of their quarterback Tom Brady and coach Bill Belichick, are due in large part to meticulous micromanagement of every detail of recruitment, scheduling, training and play. In the case of the Patriots and other football dynasties such as Nick Saban’s Alabama teams, this focus on institutions and process is so pronounced as to give the impression that players could be swapped in or out, with little change to outcomes.
Emphasis on leadership, culture, process over outcome, and attention to detail at all scales might seem obvious. Yet these strategies for optimising team performance depart strongly from the common assumption that the key to making a great team is assembling the best players. What is it about team performance that makes it so hard to reconcile these positions?
Consider the most significant large-scale science project in history, the Manhattan Project, resulting in the world’s first nuclear weapon. It involved many significant figures but three can be singled out: the physicist Leo Szilard for vision, Major General Leslie Groves for organisational skills, and the physicist J Robert Oppenheimer for his inspirational leadership. It was Szilard who first understood that subatomic particles called neutrons could cause atoms to break apart, triggering a chain reaction that would produce vast quantities of energy. Groves, who directed the Manhattan Project from 1942 to 1946, overcame huge bureaucratic barriers to acquiring the resources the project needed. Oppenheimer had the idea to corral the projects’ scientists in an isolated summer-camp-style setting in New Mexico, now Los Alamos National Laboratory. If we subtracted any one of these men from the Manhattan Project, would the outcome have changed, and can we quantify by how much?
Some settings promote creativity and exploration, and so facilitate chance-related positive discoveries
We must acknowledge it’s difficult to know. Complicating matters, performance isn’t guaranteed even with the most gifted individuals, a constructive cultural environment and a resource-rich, ‘all details covered’ organisation. Luck plays a role in performance, although its significance varies across domains. This is one of the main themes in the book The Success Equation (2012) by the investment strategist Michael Mauboussin. Sports such as hockey – with its fast-moving puck, fewer opportunities to score due in part to having no shot clock and less ice time for skilled players – are governed by greater randomness than sports such as basketball, meaning that outcomes in hockey are harder to understand, predict and control.
Given that chance’s role in performance varies, we might ask whether it’s possible to create circumstances that favour ‘good luck’. The sociologist Robert Merton in the 1950s coined the term ‘serendipitous sociocognitive microenvironments’ to capture the idea that some settings seem to promote creativity and exploration, and so facilitate chance-related positive discoveries. Merton believed in this principle so strongly that it was part of his motivation in establishing the Stanford Center for Advanced Study in the Behavioral Sciences (CASBS) – an institution, he said, with an aim of fostering ‘sustained sociocognitive interaction between talents in different social science disciplines and subdisciplines that would prove to be symbiotic’. By attending to the ‘cognitive microenvironment’, Merton was acknowledging the factors that shape teams beyond raw talent alone. He tried to build an environment as congenial to those forces as possible, even if he couldn’t name them precisely. As the 19th-century poet Emily Dickinson wrote:
Luck is not chance–
Fortune’s expensive smile
The Father of the Mine
Is that old-fashioned Coin
Merton’s goal for the CASBS and other creative institutions brings into focus a central issue missing from much of the discussion on team performance. The terms proposed for group performance in the ‘success equations’ hide complexity that, if it can be exploited, offers a meaningful edge. This hidden complexity – whether you favour explanations that stress individual players, team dynamics, training or cultural forces – captures precisely how these factors influence individual performance and how this in turn translates into team success. Oppenheimer might have inspired his titans to work together by appealing to an urgent shared purpose (defeating the Axis Alliance) but how did the collaboration actually transpire? We might be able to describe the traits and behaviour of Walker’s quiet leaders, but what are the causal mechanisms connecting their leadership to how players perform, individually and as a team?
‘Statistics and mythology may seem the most unlikely bedfellows,’ mused the evolutionary biologist Stephen Jay Gould in the essay ‘The Streak of Streaks’ (1988) for The New York Review. ‘How can we quantify Caruso or measure Middlemarch?’ In the domain of team sports, there’s been an explosion of attempts to answer Gould in recent years. In his book Moneyball (2003), the financial journalist Michael Lewis chronicles how sports analytics now impact decisions about recruitment, training, playing time and even strategy on the court – a good example is the rise of the three-point shot in basketball. Yet many players feel frustrated by the ‘tyranny of metrics’, to riff on Jerry Muller’s 2018 book about replacing human judgment and intuition with (typically) simple statistics. The former professional basketball player and current sports commentator Jalen Rose suggests in a New Yorker interview that the emphasis on simple metrics insults player intelligence. It fails to capture hard-to-describe skills that matter – such as understanding ‘the flow of the game’ – and places too much emphasis on easy-to-quantify measures such as ‘triple doubles’ (getting double-digit baskets, rebounds and assists in a single game).
Bill Russell, one of basketball’s titans who played for the Boston Celtics in the 1960s, felt strongly enough about this to mention it in his retirement letter in 1969:
Let’s talk about statistics. The important statistics in basketball are supposed to be points scored, rebounds and assists. But nobody keeps statistics on other important things – the good fake you make that helps your teammate score; the bad pass you force the other team to make; the good long pass you make that sets up another pass that sets up another pass that leads to a score; the way you recognise when one of your teammates has a hot hand that night and you give up your own shot so he can take it. All of those things. Those were some of the things we excelled in that you won’t find in the statistics.
The problem is so common it has its own name: the McNamara fallacy, after Robert McNamara, the US defence secretary known for a slavish devotion to numerical analysis during the escalation of the Vietnam War. ‘The first step is to measure whatever can be easily measured,’ wrote Daniel Yankelovich in Corporate Priorities: A Continuing Study of the New Demands of Business (1972). ‘The second step is to disregard that which can’t be easily measured … The third step is to presume that what can’t be measured easily really isn’t important … The fourth step is to say that what can’t be easily measured really doesn’t exist.’
‘I call him Lego. When he’s on the court, all the pieces start to fit together’
In his insightful article ‘The No-Stats All-Star’ in The New York Times Magazine in 2009, Lewis captured the essence of the problem. Writing about the US National Basketball Association (NBA) player Shane Battier, he notes:
Here we have a basketball mystery: a player is widely regar
- The whole point of certification is that it independently and impartially verifies that you are complying to a standard.You can find a problem together with the