Biomechanical comparison of throwing motions for speed ball and distance in the varsity baseball players

1994 ◽  
Vol 27 (6) ◽  
pp. 678
Author(s):  
Tomohisa Miyanishi ◽  
Michiyoshi Ae ◽  
Morihiko Okada ◽  
Yasuo Kunugi
Author(s):  
Wei-Han Chen ◽  
Yu-Cheng Chiu ◽  
Chiang Liu ◽  
Ming-Sheng Chan ◽  
Nicholas J Fiolo ◽  
...  

This study compared the kinematic parameters of swing mechanics under toss batting (TB), motor imagery (MI), video projection (VP), and virtual reality (VR) conditions during baseball batting. Nine college baseball players performed three swings to hit a tossed ball under TB conditions or a virtual ball under MI, VP, and VR conditions. The results revealed that upper trunk backward rotation was smaller in the loading phase under the VP and VR conditions than under the TB and MI conditions and lower under VR than under the VP condition ( p < 0.05) except at the load event. Pelvic backward rotation was smaller under the VR condition than under the TB, MI, and VP conditions ( p < 0.05). In the swing phase, TB demonstrated higher peak velocity at the head of the bat, lead elbow extension, and pelvis and upper trunk rotation than did MI, VP, and VR, whereas VP also demonstrated higher peak velocity in pelvic forward rotation than did VR ( p < 0.05). In summary, VR demonstrates a more realistic response in the loading phase and reduced pelvic backward rotation but lower movement velocities. Coaches should pay attention to movement differences between swing conditions when arranging a swing training plan.


2008 ◽  
Vol 17 (4) ◽  
pp. 473-482
Author(s):  
임승길 ◽  
김병곤 ◽  
Kimyoungjae
Keyword(s):  

Author(s):  
Garrett S. Bullock ◽  
Edward C. Beck ◽  
Gary S. Collins ◽  
Stephanie R. Filbay ◽  
Kristen F. Nicholson

Author(s):  
Hyeuk Kim

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.


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