Bad Beats: Getting Back on the Bowl Prediction Horse

Last year I used the Sagarin pure points method and found the games with the biggest discrepancy between the spread Sagarin predicted and the actual spread. I charted this for all of last year's bowls and finished around .500.

This year, I'm using The Prediction Tracker, which aggregate predictions from a number of systems. Treat the picks as independent and assume they fall in a normal distribution (warning: this is probably a terrible set of assumptions, but roll with it). Then, we can see how many standard deviations each spread is from the mean predictions. The ones that are furthest away are the best values.

Let's run through the Chick-Fil-A Bowl (Duke-TAMU) as an example. There are 48 picks with a mean of TAMU -6.54 and a standard deviation of 4.2. The current spread at the LVH is TAMU -12.5, approximately 1.42 standard deviations away from the mean prediction. The high Z-score demonstrates that there's value in this line, and that TAMU is overvalued. And that's why I picked Duke.

For reference, I put all the scores in a Google Document so you can bet against them as you will.

There haven't been many updates here lately, but that doesn't mean I haven't been working. Expect another update soon with a bunch of news.

Super Bowl Hype Drive: Prediction Aggregation

Back in grade school, I used to poll my friends the week before the Super Bowl, asking who they thought would win. (Okay, fine: In order to get a better sample size, I asked the people who weren't my friends, too.) And they performed well considering the sample: off the top of my head, they correctly called the two of the three games I polled them on.

You may remember that, last March, I collected a few dozen brackets and graded them during the NCAA basketball tournament. Most of the brackets predicted that Kentucky would eventually win the championship, which they did. How accurate would a crowdsourced Super Bowl prediction be?