Crowdsourcing College Basketball

I saw an interesting article a few days ago about using crowdsourcing to make your picks for the big college basketball tournament currently underway (I don’t think I’m allowed to use any phrases that contain the words “madness,” “final,” “March,” or “four” or the initials “N,” “C,” “A,” or “A” without paying somebody a royalty, so I won’t). 

The article turned out not to be what I thought it might be about.

When I first saw the headline, I thought it might somehow be a crowdsourcing project to find out what a perfect tournament bracket would look like going into the tournament (after all, filling out a perfect bracket after the tournament is a trivial exercise).  That seemed like a mildly interesting idea but ultimately a futile one: we don’t award titles based on how people *think* teams are going to do, but how they *actually* do.  As the old saying goes, “that’s why you play the games.” So what do you do with a crowdsourced bracket where the crowd picks the winners?

Why else fill out a bracket?  The article presented a strategy for betting (ok, not “betting” betting, but more like “winning the office pool” betting, since “betting” is illegal in a lot of places).  The idea, originally put forward by author Mark McClusky last year, is to figure out what that ideal bracket is—the one with all the favorites identified—and use some basic statistical analysis to figure out how to bet (ahem, position) against it.  In other words, if the idea of the office pool is to pick more winners than everyone else, it would be good to pick some teams that have a reasonable chance of winning, but are not the favorites.  That way, if you are right, your picks vault ahead of the “crowd” that picked the “likely” winners.

Of course, in a year where there are no upsets (which isn’t very likely) you’ll undoubtedly lose.  All the players who pick the favorites will all tie for the top spot and have to split the pool.  Kind of like playing Hurley’s lottery numbers from LOST and winning. (You would think not very many would have played those numbers in January 2011, since LOST went off the air last year. You would be wrong. Over nine thousand people hit the New York Mega Millions jackpot with those numbers this January: they all split the total pot, of course, and received a whopping $150)

Of course, using crowdsourcing to set odds is not new: if you’ve ever been to a horse race or dog race, that’s how the odds are calculated.  The horse (or dog) that most people have picked to win is the favorite, with the long odds on the horse (or dog) that the fewest people have picked to win.  (Which, by the way, is why most movies or TV shows that show someone winning a fortune by making a big bet on an underdog are bogus: if you bet a million dollars on a million-to-one long shot, you just made that long shot into the new favorite, and *if* it wins you won’t get much more than your bet back.)

In any case, I thought it was an interesting way to use the wisdom of the crowds to bet (er, position) against the crowd.  A high-risk, high-reward strategy to be sure, but an interesting one.

And for the record, this year I’m picking Kansas University to win the national championship (to be fair, I pick them every year). I didn’t attend KU, but I lived near Lawrence for nearly 30 years and know a lot of people that did. So Rock Chalk! Jayhawk! Go KU!