基于GraphX传球网络的传球质量量化研究

doi:10.11896/j.issn.1002-137X.2017.12.033

计算机科学 ›› 2017, Vol. 44 ›› Issue (12): 175-182.doi: 10.11896/j.issn.1002-137X.2017.12.033

基于GraphX传球网络的传球质量量化研究

廖彬,张陶,国冰磊,于炯,牛亚锋,张旭光,刘炎

新疆财经大学统计与信息学院乌鲁木齐830012,新疆大学信息科学与工程学院乌鲁木齐830046,新疆大学信息科学与工程学院乌鲁木齐830046,新疆大学信息科学与工程学院乌鲁木齐830046,重庆大学计算机学院重庆400044,新疆财经大学统计与信息学院乌鲁木齐830012,清华大学软件学院北京100084

出版日期:2018-12-01 发布日期:2018-12-01
基金资助:
本文受国家自然科学基金项目(61562078,61262088),新疆维吾尔自治区自然科学基金(2016D01B014)资助

Research on Passing Quality Quantification Based on GraphX Passing Network

LIAO Bin, ZHANG Tao, GUO Bing-lei, YU Jiong, NIU Ya-feng, ZHANG Xu-guang and LIU Yan

Online:2018-12-01 Published:2018-12-01

摘要/Abstract

摘要： 虽然大数据技术在不断成熟,但它在竞技体育领域的相关应用研究还处于探索阶段。常规篮球统计缺乏对传球数据的记录,更缺乏对传球数据的统计分析、价值挖掘及应用等方面的研究。首先,在GraphX基础上将传球数据构建成图,为传球质量的研究奠定基础；其次,提出传球质量评估方法PESV(Pass Expectation Score Value),相比于传统的助攻数与失误数的比值ATR(Assist Turnover Ratio),PESV能更全面地评价球员传球的质量；最后,介绍基于传球网络及传球质量评估方法PESV的几种应用场景,包括传球质量对比赛结果的影响分析、基于PESV值的传球路线选择,并以华人球员林书豪为例,计算其2015－2016赛季的传球得分期望值。

关键词: 大数据应用,传球网络,GraphX,传球质量量化,球员评价

Abstract: 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 (Pass 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 value,and taking the Chinese player Jeremy Lin as an example to calculate his passing expectation values in the 2015－2016 seasons.

Key words: Big data application,Passing network,GraphX,Quantitative pass quality,Player evaluation

廖彬,张陶,国冰磊,于炯,牛亚锋,张旭光,刘炎. 基于GraphX传球网络的传球质量量化研究[J]. 计算机科学, 2017, 44(12): 175-182. https://doi.org/10.11896/j.issn.1002-137X.2017.12.033

LIAO Bin, ZHANG Tao, GUO Bing-lei, YU Jiong, NIU Ya-feng, ZHANG Xu-guang and LIU Yan. Research on Passing Quality Quantification Based on GraphX Passing Network[J]. Computer Science, 2017, 44(12): 175-182. https://doi.org/10.11896/j.issn.1002-137X.2017.12.033

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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

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