Background: Football is one of the most popular sports in the world, and it is also a sport with a high rate of injury. The study aims to investigate the effects of physical and mental mixed fatigue (PMF) on knee biomechanics during sidestep cutting maneuvers in elite male soccer players, thereby assessing the potential mechanisms underlying non-contact knee injuries. Methods: Thirty-six elite male soccer players were recruited (age: 21.61 ± 1.22 years; body mass: 75.16 ± 6.34 kg; height: 175.8 ± 3.53 cm; shoe size: 41–44 EUR). Following a targeted fatigue induction protocol, key lower limb biomechanical data were acquired during anticipated sidestep cutting maneuvers both pre- and post-PMF. Statistical analyses were performed utilizing paired sample t-tests and one-dimensional Statistical Parametric Mapping (SPM1d). Results: Following PMF, knee valgus increased at initial contact (P = 0.022). Kinetic analysis, supported by SPM1d, revealed a marked transition from an extensor-dominant to a flexor-dominant pattern in sagittal knee moments (P = 0.007), alongside elevated knee valgus moments (P = 0.039). Neuromuscularly, quadriceps and lateral gastrocnemius activation (iEMG/RMS) significantly decreased, whereas compensatory increases were observed in the hamstrings and medial gastrocnemius (all P < 0.001). Conclusion: While PMF preserved most kinematics, the statistically significant increase in knee valgus, though small in magnitude, suggests an impaired frontal-plane control that may elevate Anterior Cruciate Ligament (ACL) strain. The shift from quadriceps to hamstring dominance reflects a compensatory neuromuscular strategy. These findings emphasize the importance of incorporating cognitive load into injury-prevention programs and monitoring mental fatigue to reduce non-contact knee injury risks.

(1) Background: Changes in midsole hardness may affect lower-limb impact loading during forefoot strike (FFS) running in children, yet the biomechanical basis for discriminating elevated VALR-related loading remains unclear. (2) Methods: Fourteen school-aged boys performed FFS running tests in experimental shoes with four midsole hardness levels (37, 42, 47, and 52 Shore C). Lower-limb kinematics and surface electromyography (sEMG) data were collected during the dominant leg stance phase. After preprocessing, VALR was calculated from 336 valid trials, and 28 stance-phase biomechanical features were extracted, yielding a final machine-learning dataset of 324 trials after excluding incomplete feature data. VALR was used to compare loading changes and define trial-level elevated-loading labels based on the median VALR value. Classification models were evaluated under participant-level GroupKFold validation, and XGBoost was retained for exploratory SHAP analysis. (3) Results: VALR showed an upward trend with increasing hardness, but no statistically supported change point was identified. XGBoost achieved an accuracy of 75.93%, precision of 74.14%, recall of 79.63%, F1-value of 0.768, and pooled out-of-fold AUC of 0.738. SHAP analysis indicated that distal and non-sagittal kinematic features contributed most to model classification. (4) Conclusions: Elevated VALR-related loading during children’s FFS running may be characterized by a multi-feature model-based pattern rather than a fixed midsole hardness threshold.