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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (2): 37-45    DOI: 10.11925/infotech.2096-3467.2017.0893
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Analyzing Decentralization and Performance of 2017 NBA Final Teams with Weighted Directed Network Entropy
Wu Jiang(), He Chaocheng, Gong Zheng
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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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     
Received: 06 September 2017      Published: 07 March 2018
ZTFLH:  N94 G35  

Cite this article:

Wu Jiang,He Chaocheng,Gong Zheng. Analyzing Decentralization and Performance of 2017 NBA Final Teams with Weighted Directed Network Entropy. Data Analysis and Knowledge Discovery, 2018, 2(2): 37-45.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.0893     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I2/37

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
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
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
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
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
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
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