The efficacy of decision diagram techniques for state space generation is known to be heavily dependent on the variable order. Ordering can be done a-priori (static) or during the state space generation (dynamic). We focus our attention on static ordering techniques. Many static decision diagram variable ordering techniques exists, but it is hard to choose which method to use, since only fragmented informations about their performances are available. In the work reported in this paper we used the models of the Model Checking Contest 2016 edition to provide an extensive comparison of 14 different algorithms, in order to better understand their efficacy. Comparison is based on the size of the decision diagrams of the generated state spaces. State space is generated using the Saturation method provided by the Meddly library.