Too Many Variables and Too Much Randomness

My NCAA basketball tournament bracket is a mess and your bracket is too. Despite nearly 12 million entrants, the first week-end of the tournament wasn’t even over and there were no perfect brackets left in the ESPN bracket challenge. And the last man standing was hardly an expert either — he admitted not to have even seen a full game all season.

It seems like it ought to be easier than that to pick winners. There is no spread involved and a full season of games has been played, allowing us to evaluate the teams with a significant amount of data. But the short answer is that there are simply too many variables.and too much randomness involved to think that we can succeed in picking all those winners.

Villanova, a #1 seed, was a clearly superior team to North Carolina State over the course of the season. But the eighth-seeded Wolfpack handled Nova handily Saturday evening. Two #2 seeds went down in the round of 32. Two third-seeds lost in the round of 64. The better team doesn’t always win the game and the best team doesn’t always win the tournament. Kentucky is the biggest tourney favorite in a long time yet the Wildcats have a less than 50:50 likelihood of winning it all. Stuff happens.

So the next time you think that you have a lead on the next hot stock or are convinced that you can pick stocks generally, you might consider how many variables and how much randomness comes into play relative to the success of any particular company or stock. Think about how many things can go wrong at the national level or the economic level or the competitive level. Recall the vagaries involved in the success or failure of any particular good or service. Remember how fickle the public is. Then compare those variables to what can happen in just 40 minutes of basketball between only two teams (hint: basketball is much more predictable).

So if you think you have what it takes to pick stocks, you might want to think again…especially before you put any money at risk.

2 thoughts on “Too Many Variables and Too Much Randomness

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