The NBA’s Milwaukee Bucks aren’t a very good basketball team – yet. The team is in the rebuilding phase and in the 2015-16 season yielded only 33 wins and 49 losses, not good enough for the playoffs.
In the spring draft a few weeks ago, the team drafted Thon Maker, a player born in the Sudan. While Maker has a reputation for having a good work ethic, athleticism and ability to shoot a three-pointer, the team doesn’t know how old he is. He wasn’t born in a hospital and doesn’t have a birth certificate. When he came to the US he was listed as being born in 1997, but some scouts worry that he’s older.
At 7’1”, 223 lbs, Thon is so tall and skinny he doesn’t show up on a Body Mass Index chart. The team wants him to gain weight, some say as much as 25 pounds in three years, and that’s where his age becomes important. If he’s 19, it will be much easier than if he’s older and in a very different phase of his development. Accordingly, whether he’s one age versus another in this instance is meaningfully different for how the Bucks may project Maker’s future performance and his impact on the team.
This is a case of a unique high-stakes decision. The Bucks are missing a seemingly critical data point but drafted Maker anyway. The dilemma didn’t prevent the team from making the decision –just as incomplete information doesn’t prevent most us from moving forward with big decisions in our lives.
Did the Bucks employ a good method to make its decision? We don’t know, but Milwaukee fans (like me) hope so.
The Bucks’ decision impacts multiple stakeholders, fans, teammates, the city of Milwaukee and shareholders, and everyone has an incentive to want Maker to be in his teens instead of his 20s. We might all be prey to Projection Bias, where we see what we want to see, namely confirming data that Maker is in his teens. By using a perspective-taking research and decision making framework like the AREA Method we may make it easier to identify that this is an underlying bias. That in turn may enable us to heighten our awareness to new information, spot disconfirming data and analyze feedback as objectively as possible to best position Maker –and the team– to succeed.
After all, life is filled with uncertainty, but we don’t want to let it hobble us, and frankly we don’t want to gamble with our future either. We also don’t want to rely on hope alone. Instead, we want a proactive way to work with, and work through, ambiguity to make thoughtful high conviction decisions despite our uncertain and volatile world.