Training induced fatigue and recovery are deemed to drive adaptations leading to performance enhancements in exercise and sport. As a result, training loads should be accurately controlled and regulated to promote physiological and biomechanical adaptations. In this work, we investigated the sensitivity of inertial sensors to detect fatigue induced changes in gait kinematics in well trained team sports athletes. Thirteen young healthy subjects volunteered to perform a walking trial before and after an exhausting field test while wearing inertial sensors fit on their lower limbs, pelvis and trunk. Stride time (ST), ST variability (STV), stride length (SL), SL variability (SLV), gait speed (GS), symmetry index (SI), knee range of motion (ROM) and shank angular velocity (AV) were computed. These features were used to feed a support vector machines (SVM) classifier to distinguish non-fatigued and fatigued walking trials. Results showed significant (p < 0.05) pre-to-post changes in ST, STV, GS, SI and AV with the SVM classifier reporting an 84.62% accuracy. Thus, classification using gait features collected through inertial sensors could be promising for in-field fatigue detection.