# Tag Archives: regularization

## 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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## Regularization: The R in RAPM – Background

One of the preferred NBA metrics around right now is RAPM.  The R stands for ‘regularized’, and the improvement over APM is that regularization helps with collinearity.  I’m not going to be looking at actual NBA data (sorry), but I … Continue reading

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