Path planning is a critical part of modern computer games; rare is the game where nothing moves and path planning is unneeded. A* is the workhorse for most path planning applications. Block A* is a state-of-the-art algorithm that is always faster than A* in experiments using game maps. Unlike other methods that improve upon A*'s performance, Block A* is never worse than A* nor require any knowledge of the map. In our experiments, Block A* is ideal for games with randomly generated maps, large maps, or games with a highly dynamic multi-agent environment. Furthermore, in the domain of grid-based any-angle path planning, we show that Block A* is an order of magnitude faster than the previous best any-angle path planning algorithm, Theta*. We empirically show our results using maps from Dragon Age: Origins and Starcraft. Finally, we introduce ``populated game maps'' as a new test bed that is a better approximation of real game conditions than the standard test beds of this field. The main contributions of this paper is a more rigorous set of experiments for Block A*, and introducing a new test bed (populated game maps) that is a more accurate representation of actual game conditions than the standard test beds.