Is “making teams better” the new holy grail of performance analytics?
Fresh from the annual MIT Sloan Sports Analytics Conference, HBR blogger and MIT Research Fellow Michael Schrage notes that one of the top themes of the event was how to move beyond the Moneyball-like era of predicting and assessing individual performance and focusing on teamness.
More quantitative attention is being paid to how well players improve the in-game performances of their teammates. Are their particular game situations where their positive — or negative — influence is statistically pronounced?
Can that impact be meaningfully correlated with psychological attributes or other behavioral characteristics? Indeed, how can the coaches improve the TQ — Teamness Quotient — of their players’ performances?
How individuals contribute to team chemistry
Or, as former Los Angeles Lakers coach Phil Jackson puts it: “The next step in analytics will be how to build chemistry.”
Not surprisingly, Moneyball author Michael Lewis already had a bead on this teamness phenomenon back in 2010, when he wrote The No-Stats All-Star for The New York Times.
The article featured NBA player Shane Battier, now with the Miami Heat, who presented (as Lewis pointed out) an intriguing statistical anomaly: “His greatness is not marked in box scores or at slam-dunk contests, but on the court, Shane Battier makes his team better, often much better, and his opponents worse, often much worse.”
From the article:
It was, and is, far easier to spot what Battier doesn’t do than what he does. His conventional statistics are unremarkable: he doesn’t score many points, snag many rebounds, block many shots, steal many balls or dish out many assists. …
He may not grab huge numbers of rebounds, but he has an uncanny ability to improve his teammates’ rebounding. He doesn’t shoot much, but when he does, he takes only the most efficient shots. He also has a knack for getting the ball to teammates who are in a position to do the same, and he commits few turnovers.”
Do we know our plus-minus ratio?
When it came out, the article prompted some interesting conversation around our dinner table. My husband Keith worked with Lewis during his Liar’s Poker days at Salomon Brothers and also coached youth hockey for nearly two decades. The Battier story had a parallel in Keith’s coaching career, where he realized early on that the key statistics for his players were not “goals” or “assists” but rather their “plus-minus.”
In ice hockey, individual players’ plus-minus stats get increased by one every time their team scores an even-strength or shorthanded goal while they are on the ice and decreased by one every time their team allows an even-strength or shorthanded goal while they are on the ice. As Keith will point out, it is the best metric for how an individual player really contributes to the success of the overall team.
As data and analytics bring increasing sophistication to our understanding of employee and organizational performance, will we be able to identify the plus-minus statistics for our teams? Will we discover metrics that capture the essence of successful team chemistry and provide insights into which individuals and efforts (while seemingly unremarkable when measured alone) help make the team and the organization better by their mere presence in the game?
And, perhaps most important of all, having quantified it, will we be able to encourage and reward teamness without undermining and ultimately destroying it?
This was originally published on Ann Bares’ Compensation Force blog.