期刊文献+

Probit线性回归模型对2015~2016赛季CBA联赛前8强球队间攻防综合实力的等级分档研究

Comparison and Analysis on the Offensive and Defensive Ability of the Top 8 Teams in the CBA League of 2015-2016 Regular Season Based on Linear Regressive Model of Probit
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摘要 本研究运用文献资料调研、数理统计、Probit线性分析与录像观察等方法对2015~2016赛季中国男子职业篮球联赛前8强球队在常规赛的248场比赛中的攻、防技术指标进行统计分析,对其加以系统研究与等级评价。采用Probit模型对攻防综合RSR值的分布进行线性回归分析,该模型的决定系数R2=0.838,F=25.780,P=0.004(﹤0.01),检验呈高度显著相关性,说明模型具有很好的拟合结果,所建立的线性回归方程具有统计意义。最后以此线性回归方程模型对8支球队进行分档处理,总体分析结果与球队在常规赛中的表现较为相符,能够客观地反应球队自身能力的不足之处以及不同球队间的差距所在,为各球队今后的发展指明方向。 This paper makes a statistical analysis and grade evaluation on the offensive and defensive technical indexes of the top eight teams,which contain a great deal of data about 248 games during the regular season in the2015 ~ 2016 season of Chinese Basketball Association( CBA),based on the methods of literature study,mathematical statistics,linear regressive model of Probit and video observation. Linear regression analysis is used to analyze the offensive-defensive RSR values based on Probit model,and the determination coefficient value is0. 838. Furthermore,the F test result shows that the linear models which have been established are efficacious and significant correlation,which could be proved by F = 25. 780 and P = 0. 004( ﹤ 0. 01). Finally,the 8 teams are classified into four levels according to linear regressive model. The overall results for this statistical process are well matched with the actual situation in the regular season,which could objectively reflect the shortcomings and gaps among teams and points out the future direction of team development.
作者 李金桥 Li Jinqiao(Sport College of Yangzhou University,Yangzhou 225009,Jiangsu,China)
出处 《体育科技文献通报》 2018年第7期45-47,共3页 Bulletin of Sport Science & Technology
关键词 中国 CBA联赛 线性回归 分档评价 China CBA linear regression grade evaluation
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  • 1杨桦.论篮球运动的本质、特征及规律[J].成都体育学院学报,2001,27(4):60-62. 被引量:67
  • 2黄建伟,刘庆山.第28届奥运会中外男篮中锋防守意识比较研究[J].西安体育学院学报,2006,23(1):97-101. 被引量:11
  • 3中国篮球协会官方网站.CBA数据排名[EB/Ol].http://www.cba.gov.cn/cbastats/wcba/default.asp)L.
  • 4徐子佩.大数据:正在到来的数据革命[M].桂林:广西师范大学出版社,2014.4.
  • 5胡勇.大数据如何改变NBA[EB/OL].[2015-1-14].http://content.businessvalue.tom.cn/.
  • 6BHANDARI I S, COLET E, RARKER J, et al. Advanced scout: Data mining and knowledge discovery in NBA data[J]. Data Min Knowl Disco, 1997,1(1) : 121-125.
  • 7GOLDSBERRY K. Courtvision: New visual and spatial analytics for the NBA[C]//MIT Sloan Sports Analytics Conference, 2012,3-6.
  • 8HOLLINGER J. Pro Basketball Forecast: 2005-2006 [M]. USA: Potomac Books Press, 2005 : 6-8.
  • 9JAMES B, HENZLER J. Win Shares[M]. USA: STATS Pub- lishing Press, 2002 : 23-31.
  • 10MAHESWARAN R, CHANG Y H, HENEHAN A, et a l. De- constructing the rebound with optical tracking data[C]//MIT Sloan Sports Analytics Conference, 2012 : 5-7.

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