Faaascinating, I know. But it gives me a chance to make an Important Point about the predictive value of statistics.
My confidence in my bet was based on the fact that, from 2003 to 2011, Tulane covered just under 40% of their games against the spread. Winning 60 percent of your bets would make the average professional bettor salivate, so I was happy to bet based on this big trend.
There were two things I ignored: first, that one game is the smallest of sample sizes, and second, that past results are no guarantee of future performance. The second point is the interesting one, so let's focus on that: if a team has done better/worse than average against the spread in the past, does that tell us anything about its performance against the spread in the future?
First, a little experiment. Let's take the best teams against the spread from 2003-2010 (as again compiled from TeamRankings.com), and see how they fared in 2011:
|Best Teams ATS||'03-'10||2011|
That's disappointing, but it does make sense. Imagine the following situation: bettors realize that certain teams keep covering the spread, and start backing them regardless of who they're playing. In response, the oddsmakers raise the lines these teams have to beat higher and higher, making it harder for them to cover.
But what if no one's paying attention to the teams at the bottom of the pile? Is there value in the perennial losers?
|Worst Teams ATS||'03-'10||2011|
|N Mex State||42.0||61.5|
That's a little better. And yes, this bottom-six list looks a lot like the list of the worst teams against the spread I put in last Friday's post.
There is one other way to determine if this spread is simply random. In a perfectly efficient betting market, the odds of any team covering against any other team in any given game would be really close to 50%. Of course, we're assuming no pushes (ties) or public (popular) teams*.
* - In our case, these are schools like Notre Dame and LSU that people will back regardless of the spread because they root for them. Since there's more demand, oddsmakers can make the line a little "overpriced" for the public team, since they know people will want to bet them anyway. Examples of public teams in other sports include the Yankees, Packers, and Lakers.
Even in this "ideal" case, pure randomness dictates that you get a few schools with a cover percentage above 60% and a few below 40%. In fact, if you compare histograms of our ideal simulation and the actual results, they look really similar.
Gambler's Fallacy on me yet: just because the Tulane coin has come up tails so often in the past is no indication that it will come up heads the next time the coin's tossed.
*-Well, at least as far as covering the spread is concerned.