Byzantine Fault-Tolerant (BFT) protocols play an important role in blockchains. As the deployment of such systems extends to wide-area networks, the scalability of BFT protocols becomes a critical concern. Optimizations that assign specific roles to individual replicas can significantly improve the performance of BFT systems. However, such role assignment is highly sensitive to faults, potentially undermining the optimizations' effectiveness. To address these challenges, we present OptiLog, a logging framework for collecting and analyzing measurements that help to assign roles in globally distributed systems, despite the presence of faults. OptiLog presents local measurements in global data structures, to enable consistent decisions and hold replicas accountable if they do not perform according to their reported measurements. We demonstrate OptiLog's flexibility by applying it to two BFT protocols: (1) Aware, a highly optimized PBFT-like protocol, and (2) Kauri, a tree-based protocol designed for large-scale deployments. OptiLog detects and excludes replicas that misbehave during consensus and thus enables the system to operate in an optimized, low-latency configuration, even under adverse conditions. Experiments show that for tree overlays deployed across 73 worldwide cities, trees found by OptiLog display 39% lower latency than Kauri.