首页    期刊浏览 2024年12月05日 星期四
登录注册

文章基本信息

  • 标题:Team Performance Indicators Explain Outcome during Women’s Basketball Matches at the Olympic Games
  • 本地全文:下载
  • 作者:Anthony S. Leicht ; Miguel A. Gomez
  • 期刊名称:Sports
  • 电子版ISSN:2075-4663
  • 出版年度:2017
  • 卷号:5
  • 期号:4
  • 页码:96
  • DOI:10.3390/sports5040096
  • 语种:English
  • 出版社:MDPI Publishing
  • 摘要:The Olympic Games is the pinnacle international sporting competition with team sport coaches interested in key performance indicators to assist the development of match strategies for success. This study examined the relationship between team performance indicators and match outcome during the women’s basketball tournament at the Olympic Games. Team performance indicators were collated from all women’s basketball matches during the 2004–2016 Olympic Games (n = 156) and analyzed via linear (binary logistic regression) and non-linear (conditional interference (CI) classification tree) statistical techniques. The most parsimonious linear model retained “defensive rebounds”, “field-goal percentage”, “offensive rebounds”, “fouls”, “steals”, and “turnovers” with a classification accuracy of 85.6%. The CI classification tree retained four performance indicators with a classification accuracy of 86.2%. The combination of “field-goal percentage”, “defensive rebounds”, “steals”, and “turnovers” provided the greatest probability of winning (91.1%), while a combination of “field-goal percentage”, “steals”, and “turnovers” provided the greatest probability of losing (96.7%). Shooting proficiency and defensive actions were identified as key team performance indicators for Olympic female basketball success. The development of key defensive strategies and/or the selection of athletes highly proficient in defensive actions may strengthen Olympic match success. Incorporation of non-linear analyses may provide teams with superior/practical approaches for elite sporting success.
  • 关键词:team sports; classification tree; machine learning; performance analysis; non-linear analysis; athlete team sports ; classification tree ; machine learning ; performance analysis ; non-linear analysis ; athlete
国家哲学社会科学文献中心版权所有