Sports have been invested with so many resources to be competitive and entertaining and to have injury prevention and analysis or improvement of the athlete's performance. In Human Action Recognition, there is a limited study on sports. Sports moves are faster in execution and have low inter class variability which produces noisy feature and ambiguity compared to daily human actions. In this study, we proposed an approach of using skeletal data from Kinect and focusing on the preprocessing process, specifically reducing irrelevant skeleton joints with regard to the action being performed and shifting of origin. The proponents would also use key poses and atomic actions as the segmentation process. Lastly, the actions would then be classified by the use of Hidden Markov Models (HMM). The evaluation will be between a model that use a full set of joints versus a model that undergoes our methodology of preprocessing of the data.