Inverse dynamic analysis (IDA) on musculoskeletal models has become a commonly used method to study human movement. However, when solving the inverse dynamics problem, inaccuracies in experimental input data and a mismatch between model and subject leads to dynamic inconsistency. By predicting the ground reaction forces and moments (GRF&Ms), this inconsistency can be reduced and force plate measurements become unnecessary. In this study, a method for predicting the GRF&Ms was adopted and validated for an array of sports-related movements. The method uses a scaled musculoskeletal model and the equations of motion alone to predict GRF&Ms from full-body motion, and entails a dynamic contact model and optimization techniques to solve the indeterminacy during double support. The method was applied to ten healthy subjects performing e.g. running, a side-cut manoeuvre and vertical jump. Pearson’s correlation coefficient (r) was used to compare the predicted GRF&Ms and associated joint kinetics to the corresponding variables obtained from a traditional IDA approach, where the GRF&Ms were measured using force plates. In addition, peak vertical GRFs and resultant JRFs were computed and statistically compared. The main findings were that the method provided estimates comparable to the traditional IDA approach for vertical GRFs (r ranging from 0.96 to 0.99, median 0.99), joint flexion moments (r ranging from 0.79 to 0.98, median 0.93) and resultant JRFs (r ranging from 0.78 to 0.99, median 0.97), across all movements. Although discrepancies were identified for some variables and the majority of the peak forces were significantly different, the former were mainly contributed to noise while the differences in peak forces could potentially be overcome by adjusting parameters in the contact model. Considering these results, this method could be used instead of force plate data, hereby facilitating IDA in sports science research and providing valuable opportunities for complete IDA using motion analysis systems that does not commonly incorporate force plate data, such as marker-less motion capture.