Because no functional was the top performer in more than one category, this pruning generated 13 different DFAs for 13 properties. Our analysis then asked the essential question of how well the winner in one category performed for the other 12 properties. The accuracy percentiles from our consistency analysis are curated in Table . The Group 3 global hybrid DFAs produce the highest number of category winners (6), followed by Groups 1 and 2 with three apiece. Only one Group 6 double-hybrid functional is a category winner. The most accurate ΔEe(rxn) prediction is by MPWB1K (Group 3). This functional exhibits very poor performance across the re and ωi quantities with a mean percentile of only 17.7, but the associated Δre and Δω shifts are surprisingly good. Although not a particularly robust method, MPWB1K was optimized against a kinetics database for thermochemical applications, so its performance is consistent with expectations. Developed in tandem with MPWB1K was the Group 3 functional XB1K, 100 which appears in Table as the top performer for ω5, with comparably good results for ω4 (96.3%-ile). However, where the lowest %-ile across all properties is above 24. Despite giving the best re(N-O), TPSS-NL incorrectly predicts HCNO to have a bent minimum. Likewise, the bent minimum given by VSXC (Group 2) actually shows the best Δre(H-C) %-ile but inconsistently displays (59.0, 21.9)%-ile for TPSSh-D3(BJ)2B (Group 3) produces the best ω1, and its mean percentile for the other properties is 68.1, second only to B3LYP-D3(BJ)2B in this respect. TH4 and oPBE-D (Group 1) both give erroneous bent HCNO minima but have the best performance for ω2 and ω3, respectively.