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The application of principal component analysis and cluster analysis in comprehensive evaluation for the NBA

Ke wang

This research mainly through to the 2013 season in the NBA basketball game in his first season in pre-season technical data analysis of each project. By using the statistical methods of principal components analysis for each project, the multiple statistical index clustering scores for binary ball shot composition, binary composition, free-throw scores composition, composition of shots, and the error component in the game. At the same time according to each component to the importance of the impact on the overall variables combined with the characteristics of the NBA teams put forward a few research, the game players dribbling, passing, and widen the vision control ball skill training is of great significance to reduce mistakes and other technical points. Through the above analysis of the NBA basketball game in each link, using principal component analysis and clustering analysis, and applies this method to the comprehensive evaluation of the NBA basketball game. Principal component analysis (pca) and cluster analysis method for the NBA basketball game is very extensive in application of comprehensive evaluation, the research for improving our country on the principal component analysis (pca) and cluster analysis method research has the very vital significance.