-
Recent Posts
Archives
- June 2013
- May 2013
- April 2013
- March 2013
- February 2013
- January 2013
- December 2012
- November 2012
- October 2012
- September 2012
- August 2012
- July 2012
- June 2012
- May 2012
- April 2012
- March 2012
- February 2012
- January 2012
- December 2011
- November 2011
- October 2011
- September 2011
- August 2011
- July 2011
- June 2011
- May 2011
- April 2011
- March 2011
- February 2011
- January 2011
- December 2010
- November 2010
- October 2010
- September 2010
- August 2010
RSS
Blogroll
Tag Archives: statistics
The (Usual) Folly of Probability Matching
Generally speaking, people are fairly decent at learning how often things happen. For example, if you asked someone to predict a bunch of coin flips, they would come out pretty close to 50%. They aren’t especially good at doing other, … Continue reading
Statistics Can Lie… Sometimes
Just wanted to pass along this post, which I think does a neat job of describing why some scientific studies seem to disagree from week to week (have some wine! no, don’t have wine! coffee is good for you! caffeine … Continue reading
A Quick Thought on Plus/Minus
The Wages of Wins guys have a post on plus/minus over at their blog, and Tango has something of a reply post over at his blog. I thought I would throw in my two cents.
Accuracy and Precision
As I’ve been thinking about my football models and their less-than-stellar season of predictions, I was reminded about the distinction between accuracy and precision. Depending on the circumstances, those two words can mean a lot of different things. If you’re … Continue reading
Calvin Johnson is a Statistical Leader
Brian Burke had a post commemorating Calvin Johnson’s record by taking the opportunity to poo-poo the word ‘statistics’ as being similar to trivia. I understand where he’s coming from if that’s what he usually hears from people; Brian does much … Continue reading
RAPM – Conclusions
So that was a long series on regularized regression, the technique behind RAPM. I covered what it is and how it tends to react to collinearity, sample size, noise, and the number of predictors. I thought a bit of a … Continue reading
Regularization: The R in RAPM – Predictors
The last thing I wanted to look at with regularized regression is the impact of the number of predictors. With NBA data, you would optimally get an estimate for every player who gets in a game. However, due to collinearity … Continue reading
Regularization: The R in RAPM – Noise
For my penultimate look at regularized regression, I wanted to see how noise affected things. We know that NBA play-by-play data is very noisy; the R squared is, at best, maybe .1. How does noise affect the benefits of ridge … Continue reading
Regularization: The R in RAPM – Sample Size
Moving right along with my look at regularized, or ridge, regression, this time I’m going to look at the impact of having more data. I mentioned previously that we might expect better estimates when more data are included; as players … Continue reading
Regularization: The R in RAPM – Collinearity
As mentioned in the background, the main benefit of regularized regression over standard regression is how it deals with collinearity. When your predictors are correlated with each other, standard regression becomes unreliable but ridge regression attempts to work around the … Continue reading
