Moneyball shows us that all that knowledge and experience used to pick a baseball team was wrong and that people should ignore their "common sense" (which is usually just another word for "cognitive bias") and should use hard data.
In the absence of hard data just picking a team randomly froma pool of qualifying players seems to me like it would be as good as pickping players based on the team-picker's experience of the game and "cultural fit" and other bullshit.
>Moneyball shows us that all that knowledge and experience used to pick a baseball team was wrong and that people should ignore their "common sense" (which is usually just another word for "cognitive bias") and should use hard data.
Nothing of the sort. Moneyball showed that an actuarial method was better than other methods in picking a baseball team. This does not mean that the previous methods were no better than random chance. In the case of baseball there was a large corpus of data which had good correlations to players' future performance. That's a rather unique situation.
In this case assuming that whatever previous methods are immediately wrong was on a much weaker basis: just the dataset from one particular toy challenge. A comparison with Moneyball is misleading.
Moneyball is a totally different matter. Moneyball is simply saying the metrics used in the past were wrong.
Let me ask you this. Who do you think would win a chess match. The top 5 rated players in the world on one team, our five random adults chosen from the population who knows the game of chess?
Furthermore I'm dating their theory need not be tested with a poor computational model. You can easily test it with real people with problems to solve.
Lastly you add the caveat of "with no hard data". The paper makes the EXACT opposite claim, which is, if you HAVE hard data you should NOT pick the top performing individuals but rather sample randomly to create a team.
But you said "qualifying players", which implies that there is some qualifier. What is that qualifier?
And Moneyball didn't show us that all knowledge and experience was wrong. We didn't all of a sudden start recruiting marine biologists to become baseball players. The old "common sense" still stood. It's just that resources were reallocated in a very technical, wonkish way.
It's not like someone would look at you like you were crazy if you said "hey, we should pay more attention to on-base percentage than we do now, and maybe we could win with lower salaries". It was, at best, a minor revolution in baseball asset allocation.
In the absence of hard data just picking a team randomly froma pool of qualifying players seems to me like it would be as good as pickping players based on the team-picker's experience of the game and "cultural fit" and other bullshit.