Firstly, there are many distance measures and the distance of the samples to the group mean is a good measure of density of cluster in my opinion. Thus, I would use, for instance, sum of the Mahalanobis distances of each sample which are the variance scaled distances of them to the group mean. You can divide that sum to the number of samples to obtain an average distance. And apparently, for your case, the average distance of TD is expected to be well less than the FXSA's.
There are also Euclidean distance, Manhattan distance and many other. They are all, however, similar except about how they consider the variations. For example in Minkowski metric which is the generalization of the Euclidean and Mahalanobis distance you can alter the impact of a distance by any power you want by changing lambda in the equation below. In this way you can change, for example, the impact of an outlier to your sum of distance measures.