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Although the big data technology continues to mature , relevant application research in the field of competitive sports is still in the exploratory stage. Conventional basketball technical statistics lacks the record of the passing data as well as the statistical analysis , data mining , and application of the passing data Firstly, based on GraphX, we created the passing network graph,which laid a foundation of future research on passing quality. Secondly,PESV CPass Expectation Score Value) , a method of evaluating the quality of passing basketball , was proposed. Compared with the traditional ATR (Assist Turnover Ratio) that defined the ratio of assists to turnovers ,PESV can make a more comprehensive eva luation of the passing quality. Finally,we introduced a few application scenarios of PESV based on passing network , including the analysis of the impact of passing quality on game result, passing route selection based on PESV valu巳, and taking the Chinese player Jeremy Lin as an example to calculate his passing expectation values in the 2015 2016 seasons.

基于GraphX传球网络的传球质量量化研究 (Research on Passing Quality Quantification Based on GraphX Passing Network).