Wednesday, November 14, 2007

Why Statisticians Shouldn't Watch Sports

Check out this link. I got a kick out of this. First of, I really liked his reasoning. For the most part, he controlled for all the variables he needed to control for and made things simple yet elegant (I assume when he controlled for defensive points, he also controlled for only yards allowed by Defense.). Secondly, I understand this man's pain. Can't even watch a game without trying to analyze some mundane fact that only other people like him would like, which in turn causes my wife soem pain as well, having to hear it.

So, for all of you people out there that want to go check out if their team is a "bend but don't break" defense, one bit of warning. He focused on the Pac-10. So, what would be interesting to know if his ratio would stay the same in different conferences. I would assume possibly not. If not, then to have an accurate ratio, you may need to focus on each conference and then within division I football.

Friday, November 2, 2007


Earlier today I sat through a meeting in which the presenter mentioned Deming. Wow, this was the first time I have heard Deming in about 2 years, or the time period that I have spent in Marketing. Of course my interest was peaked. The presenter went on to explain how important it was to experiment every day. I totally agree with this. However, I was a little disappointed that he never spoke about how to ensure you adequately measure those results. I am giving him a pass though, as I assume it was the audience he was speaking to. Regardless, I think this is an extremely important point. Experiment all you want in this world. Tweak things and be curious...but always make sure you can adequately measure your results of the test. If not, then you have no idea what "experiment" really worked. To do this, you need to make sure your data is accurate and accessible; and that you have control of the variables. If not, you can test all you want, but you will have little understanding as to whether your manipulation effected your metrics, or something else effected them.