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Complex system modeling often provides general insights about similar systems in vastly different settings by identifying the key mechanisms driving their dynamics. Here, we demonstrate how this perspective may help avoid many individual case studies and expensive pilot projects when planning on-demand ridepooling services that promise to reduce the number of cars on the road and cut back emissions by dynamically combining trips of multiple users in the same vehicle. By exploiting the transferability of complex system models of ridepooling based on a small number of cornerstone parameters, we built an open-data framework to estimate the potential reliability and efficiency of these services. We apply our framework to create a map of the ridepooling potential in Germany, quantifying key performance and operating parameters of ridepooling services across more than 400 vastly different service areas. Our framework is easily applicable to other regions and provides valuable insights about the influence of network topology, fleet size, and demand structure on the potential performance of ridepooling services. The results presented here may help focus the planning of ridepooling services by informing decision-making from the earliest point.