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

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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

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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.

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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

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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

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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

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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

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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

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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

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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

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