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Open AccessFeature PaperArticle

Team Performance Indicators Explain Outcome during Women’s Basketball Matches at the Olympic Games

1
Sport and Exercise Science, James Cook University, Townsville 4811, Australia
2
Faculty of Physical Activity and Sport Sciences, Polytechnic University of Madrid, Madrid, Spain
*
Author to whom correspondence should be addressed.
Sports 2017, 5(4), 96; https://doi.org/10.3390/sports5040096
Received: 19 October 2017 / Revised: 27 November 2017 / Accepted: 7 December 2017 / Published: 17 December 2017
(This article belongs to the Special Issue Improving Practice and Performance in Basketball)
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. View Full-Text
Keywords: team sports; classification tree; machine learning; performance analysis; non-linear analysis; athlete team sports; classification tree; machine learning; performance analysis; non-linear analysis; athlete
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Leicht, A.S.; Gomez, M.A.; Woods, C.T. Team Performance Indicators Explain Outcome during Women’s Basketball Matches at the Olympic Games. Sports 2017, 5, 96.

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