In recent work [De15, PVZ15, DVZ13], we study empirical metrics for software (SW) source code, which can predict the performance of verification tools on specific types of SW. Our metrics comprise variable usage patterns, loop patterns, as well as indicators of control-flow complexity and are extracted by simple data-flow analyses. We demonstrate that our metrics are powerful enough to devise a machine-learning based portfolio solver for SW verification. We show that this portfolio solver would be the (hypothetical) overall winner of both the 2014 and 2015 International Competition on Software Verification (SV-COMP). This gives strong empirical evidence for the predictive power of our metrics and demonstrates the viability of portfolio solvers for SW verification.