Some useful words of wisdom for traders is shown above, courtesy (apparently) of Morgan Housel, who is worth a follow if you are into trading and investing.
You can’t ever know everything about a company because the deeper you dig you more you realize that things you thought were simple are actually endless webs of complicated people with different and shifting needs, held together by a precarious shared goal.As it relates to sports rather than companies, there are an infinite number of variables you can look at when analysing an individual match, but the key here is that "At some point decisions have to be made".
At some point decisions have to be made, which means pulling the trigger when you know there are things you don’t know and being OK with that. This is less about willingly closing your eyes and more about the realization that a few variables tend to dictate the majority of outcomes. Putting the odds of success in your favor is about understanding those variables while accepting the unknown baggage that rides along.
I've written before about the futility of waiting for everything being perfect before entering the fray - "perfect is the enemy of good" is the appropriate saying.
When we sample a system to test claims about the likelihood that it will produce certain outcomes, the sample needs to be random, blind. We cannot choose our sample, and present it as a valid test, when we already know that the results confirm the hypothesis. And so if we believe that there is something special about the New York Rangers, Game Sevens, and MSG as a venue–if we believe that the presence of those variables in a game changes the probability of victory–the appropriate way to test that belief is not to cite, as evidence, the seven at-home game sevens that we know the Rangers did win, the very games that led us to associate those variables with increased victory odds in the first place. Rather, the appropriate way to test the belief is to identify a different set of Rangers games with those properties, a set that we haven’t yet seen and haven’t yet extracted a hypothesis from, and look to see whether that sample yields an outsized number of Rangers victories. If it does, then can we legitimately claim that we’ve tested our belief in “the data.”
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