Support Vector Machines (SVM) vs Minimum distance between two convex hulls
You have to add a constraint of linear separability to make the comparison. The problem is that in reality most problem are not linearly separable. Either they need non-linear transformations (data processing or usage of kernel), or they are not separable at all (underlying randomness, noise). SVM can be adapted to these non-linearities quite well (allowing for missclasification, usage of kernels), while the second algo would need significant rework. Basically, as is, the second algo wouldn't work for most real life datasets.