Why would scaling features decrease SVM performance? I have used scaling on features of a model which contains 40 features (all columns are numbers) and a binary output variable. 
This is the Kaggle contest here I've scaled the features assuming it would deliver better performance, but with a rbf kernel SVM, the accuracy with 10 fold CV fell from 0.92 to 0.87
Here is a box plot of features before and after scaling:


What I would like to know is why scaling decreases classifier performance? I have not seen any discussions that point at this type of outcome.
 A: The problem is that you used the default parameter values in both cases. Apparently, the default values happened to be better for your data set before scaling (this is a coincidence).
When using SVM, the parameters $c$ and $\gamma$ play a crucial role and it is your task to find the best values. Your intuition is correct: the optimal performance is better when all features are scaled properly (or at least 99.99% of the time). Unfortunately, neither of your settings had optimal parameters which led to a result that seemed to reject your intuition.
Searching the optimal values for $c$ and $\gamma$ is typically done via a grid search (e.g. search a set of $<c,\gamma>$ combinations). You can estimate the performance of an SVM for a given set of parameters using cross-validation.
In pseudo-code, the general idea is this:
for c in {set of possible c values}
    for gamma in {set of possible gamma values}
        perform k-fold cross-validation to find accuracy
    end
end
train svm model on full training set with best c,gamma-pair

You can find a good beginner's tutorial here.
