Separating mid-path Internet performance from edge effects remains a fundamental challenge in network measurement. This paper presents a methodology for detecting anomalous topology, routing policies, and congested interconnections using controlled A/B comparisons derived from Measurement Lab (M-Lab) data. The approach leverages M-Lab's uniform server selection policy: by comparing performance distributions from clients in the same access ISP to different nearby M-Lab servers, natural experiments are created that isolate mid-path effects while controlling for client-side variation, access network bottlenecks, and diurnal variation in test volume. This analysis is implemented in BigQuery using sparse multidimensional histograms enabling efficient computation of Kolmogorov-Smirnov distance and ratios of geometric mean throughput across many millions of measurements in a single pass. Differences in throughput suggest mid-path bandwidth bottlenecks or traffic management; excess differences in minimum RTT suggest suboptimal routing. These signals of interconnection problems are extracted from the noise deliberately suppressed by other measurement approaches. Public dashboards provide ongoing visibility into all M-Lab metropolitan regions with sufficient servers, with drill-down capability to individual ISP--server plots.

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