Capturing human movement has become available in detail due to the advancement of motion sensor technology integrated by micro-machine and also due to the one of optical recording by high speed and high resolution image sensors. Therefore, we can easily record the human activity as the body movement BigData and analyze it to quest skill to become an expert of a target body movement. Especially, in the sports activity, the quest for becoming an expert athlete has been tried by using a mathematical model of an ideal body movement experienced from the biomechanics approach. The skill is discussed by comparing the differences from the predicted coordinates of body parts captured during the target performance. However, the approach potentially includes difficulties such as modeling the body control from the dynamics system for all human movements. And also the approach needs for adjusting jitters of the individual characteristics. Therefore, when applying the conventional approach, we must discuss a huge number of combinations of mathematical models and then we would find a model for the ideal body movement. To overcome the difficulty, this paper proposes an approach to visualize skill differences among experts and beginners from the BigData called the skill grouping method. It exploits the skill groups clustered by machine learning approach based on a kernel method. This paper shows applications of the skill grouping method from sports activities. Those show validities for finding the skill differences comparing to the BigData of skillful athletes, and also the one for managing skill transition of an athlete in a timeline.