NeuroSwift: computer vision-based system to assess the cognitive–motor speed of soccer players—preliminary findings

Background Cognitive–motor speed (CMS) in soccer integrates perceptual–cognitive processing with motor execution, yet many tools lack this integration and have limited ecological validity. NeuroSwift was engineered as a computer vision-based automated analysis platform to standardize tactical stimuli and produce reproducible measurements. Methods A 3 × 3 interaction surface, front-facing visual stimuli, and HD video were orchestrated by a web application. Twenty-nine players (15 professionals, 14 university athletes) completed 16 scenarios (8 offensive, 8 defensive). Visuomotor reaction speed (VMRS), displacement speed (DS), and response capacity (RC) were obtained, and cognitive–motor speed (CMS = VMRS + DS, in seconds) was computed. Normality and homogeneity were verified using Shapiro–Wilk and Levene’s tests. VMRS and DS were compared using independent-samples t -tests (Bonferroni α = 0.0167). RC and CMS were assessed using the Mann–Whitney U test. Effect sizes were estimated. All tests were two-tailed, and confidence intervals were estimated where applicable. Results Professionals showed faster VMRS (0.77 ± 0.12 vs. 0.96 ± 0.12 s; p < 0.001; d = 0.79), whereas university players showed faster DS (0.64 ± 0.06 vs. 0.76 ± 0.11 s; p < 0.001; d = −0.71). RC favored professionals (median 100.00% vs. 93.75%; Z = 3.13; p < 0.001; r = 0.58). CMS tended to favor professionals (median 1.53 s vs. 1.61 s) without significance ( Z = −0.544; p > 0.05; r = 0.10). Conclusion NeuroSwift enabled standardized stimuli, automated footstep detection, and reproducible in situ laboratory metrics. Expertise was discriminated in perceptual–cognitive and decision components, supporting athlete monitoring, training prescription, and applied research.