Purpose: Performance analyses in cross-country skiing often focus on lap or terrain-level splits. However, few studies have explored micro-pacing strategies—particularly in Skiathlon, an Olympic event requiring athletes to complete both classical and freestyle techniques on the same course. Methods: Thirteen national-level male skiers were tracked during an International Ski Federation-certified Skiathlon using GNSS and trunk-mounted sensors. Instantaneous speed profiles were analysed using one-dimensional statistical parametric mapping (SPM) to identify “race-critical clusters”: contiguous intervals where speed significantly predicted section time (α = 0.05) across all eight laps (four classical, four freestyle). Results: Freestyle laps were 4% faster than classical, with greater terrain-specific speed differences and pacing variability in classical, especially downhills. Seven race-critical clusters were identified: two uphill, four downhill, and one flat. These accounted for 11.3 s (classic) and 10.9 s (freestyle) of the time gap between the fast and slow group. In these segments, faster skiers used higher-gear sub-techniques and exhibited longer cycle lengths and/or higher frequencies (p < 0.05). Conclusions: Within race-critical clusters, the faster skiers gained substantial time advantages. Secondary analyses showed clear differences in sub-technique selection and kinematic profiles, suggesting that technical execution plays a critical role in these performance gains. Athletes and coaches may consider integrating GNSS-based tracking, SPM, and wearable-derived technique analysis into race evaluation to move beyond traditional split times and focus training on the most decisive segments of the course.