If you have not read any of my previous Least Squared posting, please refer to the initial post here. Week 16 in review: Only one game was in the range where we have been fairly confident on the selections. New England was a favorite in the system, but getting 2.5 points from Vegas. New England destroyed the Ravens, so the >100% difference between the expected and Vegas lines, bring that rule to 12-3 over the past four weeks. ...
There’s no shortage of data out there when it comes to college football, so I decided to take the time to create a least squares model, based on the same principals I have been using for the NFL, and outlined here. The idea was to take all 752 schools that played college football, cross that with all 4138 games that were played (up to last weekend) and see how they predict the games (especially versus the Vegas lines). ...
Sometimes you just need to slow down and look at the data a little closer. That was the case last week when I mistakenly posted the wrong side of the New England-Miami line. I mentioned it was probably best to stay away from it all together due to the raw difference being so small, but I also stated the wrong side to take. Oh well. Lesson learned. Week 15 review – The least-squares method and choosing only those lines where the percentage difference of expected and actual Vegas line over the actual line was greater than 100% went a shocking 4-0. Did I bet it this way? Nope. The three lines between 80-100% went 1-2. In games where the absolute raw was greater than 2.5 went 3-2. Overall, the LS method went an incredible 11-4-1. ...
Just a quick update on last week’s post where I use Non-Linear Programming methods to predict the NFL lines. Let’s rehash. You can also read the explanation post HERE, where I dive into Non-Linear Programming and the methods involved. For Week 13 games, the least squares approach went 10-5 overall, 3-1 where the percentage of expected vs. Vegas line was greater than 100% and 6-4 when the absolute raw difference was greater than 2.5. ...
We finished classes yesterday, so all that is left for the semester is a homework assignment, three projects and three more exams. I have whittled this away to only needing to complete one last project and study for the exams. But, instead of finishing my database project, which is due Sunday, I elected to take a deeper dive into a classroom example for an exam I had yesterday. ...
The pages discussed here can be found at: http://www.sixmanguru.com/ncaa13.php The idea of this project came from a thought that there does not exist a single place where you can see NCAA college football teams’ records and schedules easily, without having to make a bunch of clicks on very ‘graphically full’ websites. The first step was to see if the data could be found easily. This was done easily, as Peter Wolfe keeps this information updated daily. (http://prwolfe.bol.ucla.edu/cfootball/scores.htm). Wolfe is a member of the official BCS rankings and also includes a comprehensive listing of conferences as well. ...
It has been awhile since I started this and never really followed up. I created this blog to be a space where I can share data, news and concepts I am working with. I have been feverishly working on a project that will be hosted here that just doesn’t seem to want to end. In the meantime, I decided I needed to catch up with a few projects that I have actually completed. ...
I am a self-described nerd, who is currently a 48-year old graduate student in the Business Analytics program at the McCombs Business School at the University of Texas at Austin. In 1993, I developed a rankings system for six-man football in Texas. I ran a newsletter called The Huntress Report, which later became the website, sixmanfootball.com. About five years ago I began a website dedicated to college tennis in Texas, texascollegetennis.com. I repost press releases and used to blog until I became an employee within the UT Athletics Department. I hope to get back to blogging there soon, but will definitely keep posting the team rankings I develop several years ago. ...