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数据分析与知识发现  2018, Vol. 2 Issue (2): 37-45    DOI: 10.11925/infotech.2096-3467.2017.0893
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于加权有向网络熵的2017 NBA总决赛球队去中心化水平与绩效研究
吴江(),贺超城,龚正
武汉大学信息管理学院 武汉 430072
武汉大学电子商务研究与发展中心 武汉 430072
Analyzing Decentralization and Performance of 2017 NBA Final Teams with Weighted Directed Network Entropy
Jiang Wu(),Chaocheng He,Zheng Gong
School of Information Management, Wuhan University, Wuhan 430072, China
Center of Chinese E-commerce Research and Development of Wuhan University, Wuhan 430072, China
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摘要 

目的】定量研究组织的中心化水平及其对球队绩效的影响。【方法】以2017年NBA总决赛为例, 人工记录球队的传接球关系并构建网络, 原创地提出加权有向网络熵(Weighted Directed Network Entropy, WDNE)的定义与算法, 分析传接球网络。【结果】WDNE值越大, 网络的去中心化程度越高。当球队传接球网络的WDNE差距较大时, 得分差距也拉大; 反之, 得分差距也小。【局限】人工记录会导致遗漏与偏差, 同时数据量也偏小。【结论】研究发现去中心化水平对球队的绩效、球队网络的鲁棒性、球队凝聚力有积极影响。

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吴江
贺超城
龚正
关键词 NBA总决赛去中心化水平加权有向网络熵鲁棒性无序性    
Abstract

[Objective] This paper aims to analyze the impacts of de-centralization level on NBA team’s performance. [Methods] First, we watched the 2017 NBA Final videos to establish a ball-passing network. Then, we originally proposed the definition and algorithm of weighted directed network entropy(WDNE) to analyze the team’s performance. [Results] We found the WDNE value was positively correlated to the degree of network decentralization. When the team’s WDNE gap was large, the score gap was also widened, or vice versa. [Limitations] Manual recording might lead to omissions and deviations, while the sample size was small. [Conclusions] The decentralization level poses positive effects to a team’s performance, as well as the cohesion and robustness of the team’s network.

Key wordsNBA Finals    Decentralization    Weighted Directed Network Entropy    Robustness    Disorder
收稿日期: 2017-09-06     
引用本文:   
吴江,贺超城,龚正. 基于加权有向网络熵的2017 NBA总决赛球队去中心化水平与绩效研究[J]. 数据分析与知识发现, 2018, 2(2): 37-45.
Jiang Wu,Chaocheng He,Zheng Gong. Analyzing Decentralization and Performance of 2017 NBA Final Teams with Weighted Directed Network Entropy. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2017.0893.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.0893
图1  传接球网络示意图
图2  球队传接球加权有向网络示意图
发球者 接球者 次数 发球者 接球者 次数
A D 12 C D 5
A B 4 C A 4
A C 5 C B 7
B D 15 D A 20
B A 3 D B 18
B C 8 D C 6
表1  球员之间的传接球次数
发球者 接球者 次数 E H
A D 12 12/107 0.2454
A B 4 4/107 0.1229
A C 5 5/107 0.1431
B D 15 15/107 0.2754
B A 3 3/107 0.1002
B C 8 8/107 0.1939
C D 5 5/107 0.1431
C A 4 4/107 0.1229
C B 7 7/107 0.1784
D A 20 20/107 0.3135
D B 18 18/107 0.2999
D C 6 6/107 0.1616
表2  各边的重要度与加权有向边熵
Node Weight+ Weight- Weight+ +
Weight-
V + V - V + + V -
A 21 27 48 0.5114 0.5366 1.048
D 44 32 76 0.775 0.6639 1.4389
B 26 29 55 0.5695 0.6012 1.1707
C 16 19 35 0.4444 0.4986 0.943
表3  各个节点的加权出(入)向网络节点熵
图3  中心化组织与去中心化组织
图4  5场比赛的总和传接球网络
球队 球员 加权入度 加权入向节点熵 加权出度 加权出向节点熵 加权度 加权有向节点熵
骑士队 Q23 347 0.94 308 0.85 655 1.79
Q2 277 0.77 192 0.57 469 1.34
Q0 118 0.38 185 0.51 303 0.89
Q5 102 0.35 89 0.31 191 0.66
Q13 61 0.22 112 0.37 173 0.59
Q31 77 0.33 91 0.37 168 0.70
Q24 62 0.24 61 0.25 123 0.49
Q26 41 0.17 45 0.19 86 0.36
勇士队 Y30 300 0.71 257 0.66 557 1.37
Y23 212 0.60 294 0.71 506 1.31
Y35 239 0.64 189 0.53 428 1.17
Y9 154 0.48 172 0.51 326 0.99
Y11 139 0.43 112 0.38 251 0.81
Y34 93 0.32 85 0.29 178 0.61
Y27 45 0.16 66 0.23 111 0.39
Y3 48 0.18 47 0.18 95 0.36
表4  两支球队球员的传接球出入度
球队 有向边数 平均
加权度
最大加
权边度
最小加
权边度
标准差
骑士队 87 13.14 99 1 20.13
勇士队 111 12.23 124 1 17.50
表5  网络连边的相关指标
图5  两支球队的加权有向节点熵饼图
场次 球队 加权有向网络熵 得分 助攻 失误 犯规 投篮 三分 罚球 前场板 后场板 抢断 盖帽 篮板
1 骑士 3.329 91 15 20 23 30/86 11/31 20/25 15 44 0 6 59
勇士 3.455 113 31 4 24 45/106 12/33 11/16 14 36 12 3 50
2 骑士 3.033 113 27 9 18 45/100 8/29 15/19 10 31 15 1 41
勇士 3.259 132 34 20 19 46/89 18/43 22/24 10 43 5 7 53
3 骑士 3.302 113 17 12 25 40/90 12/44 21/25 10 27 9 3 37
勇士 3.363 118 29 18 28 40/83 16/33 22/24 8 36 8 4 44
4 骑士 3.537 137 27 11 24 46/87 24/45 21/31 11 30 6 3 41
勇士 3.541 116 26 12 27 39/87 11/39 27/36 16 24 5 7 40
5 骑士 3.306 120 22 14 22 47/88 11/24 15/23 12 28 6 5 40
勇士 3.394 129 27 13 24 46/90 14/38 23/28 13 29 8 2 42
表6  加权有向网络熵与绩效
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