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Just waves of confirmation bias?
We recently came across another excellent article on data, decision-making, and cognitive biases. It’s a story about Kristaps Porzingis , a 7’1″ 19-year-old, playing in Liga ACB, perhaps the second-best basketball league in the world. He’s “the type of prospect that has historically torn coaching staffs and front offices apart” as they try to assess his NBA bona fides before the draft.
All draft picks are crapshoots, but some feel like crappier shots than others. It’s uncouth to plainly say, “I have a bad feeling about this guy,” so we do our best to justify our vague inklings. The stronger our distaste, the stronger our effort. So of course it’s the foreigner with the spindly frame and the funny name who has people [grasping for answers]. … What is the draft if not complete pseudoscience? …
He’s like a young Robin trying on Batman’s utility belt — the tools are there, and they’re incredible. They just don’t fit yet, and you can’t be too sure that they ever will. His issues on defense are the same most players his age experience. He bites on pump fakes, he gets caught ball-watching, and he can be a step slow recovering to his man. But there is a chance that, five years down the line, he’ll be doing things that only a handful of NBA big men can do at a high level.
Maybe all of that hokey pseudoscience will prove prescient. Drafting isn’t an art, and it isn’t a science, but if you squint hard enough, it can look like a happy medium. It’s all just waves of confirmation bias on both ends of the spectrum posing as data points, right? It can tell you anything you want it to if you wait long enough. But it can’t, at the very moment, tell you the fate of Kristaps Porzingis. And so, like any other year, we’ll go on trying to find some illuminating detail that will solve the puzzle once and for all, blissfully ignorant to the fact that there’s only one person with the final pieces.
As with the NFL draft, pre-draft metrics have only some predictive power. The data don’t predict a player’s ceiling, can’t account for what kind of system a player will enter, the talent he’ll have around him, the luck he’ll have with injuries, or the intangibles he possesses.
If you’re looking for a bellwether of NBA success, look to the NCAA tournament. Its pressure-packed contests featuring the best college players in the country in front of gigantic audiences turns out to be a meaningful simulation of NBA conditions. Even though it’s a very small sample size – for most players just a game or two – the data show that players who move up the draft board as a result of their performance in March Madness deserve it.
The crucial distinction to remember on this topic is that Big Data has limits. While it may help make accurate predictions or guide knotty optimization choices or help avoid common biases, it doesn’t control events. Models can predict the rainfall and days of sunshine on a given farm in central Iowa but can’t change the weather. A top draft pick may or may not develop based on the system, surrounding talent, &etc.
In our experience the best results often come from a combination of deliberation and intuition. Too much data can lead to analysis paralysis, common sense can be a shockingly unreliable guide, and those who rely on intuition alone tend to overestimate its effectiveness.
The answer for Porzingis is obvious: enroll him in an American D-I hoops powerhouse – we’d recommend a school in the Southeast or Texas – and hope that school enjoys a deep run next March.