Journal of Sports Science and Medicine
Journal of Sports Science and Medicine
 
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Journal of Sports Science and Medicine
ISSN: 1303 - 2968
       SCImago 2016     SJR: 0.981   Cites per Doc. 2-Year: 2.04    3-Year: 2.17
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©Journal of Sports Science and Medicine (2017) 16, 468 - 473
Research article
Explaining Match Outcome During The Men’s Basketball Tournament at The Olympic Games
Anthony S. Leicht1,, Miguel A. Gómez2, Carl T. Woods1

1 Sport and Exercise Science, James Cook University, Townsville, Australia
2 Faculty of Physical Activity and Sport Sciences, Polytechnic University of Madrid, Madrid, Spain

Anthony S. Leicht
✉ Sport and Exercise Science, James Cook University, Townsville, Australia
Email: Anthony.Leicht@jcu.edu.au

Received:
03-08-2017 -- Accepted: 23-08-2017 --
Published (online): 01-12-2017

ABSTRACT

In preparation for the Olympics, there is a limited opportunity for coaches and athletes to interact regularly with team performance indicators providing important guidance to coaches for enhanced match success at the elite level. This study examined the relationship between match outcome and team performance indicators during men’s basketball tournaments at the Olympic Games. Twelve team performance indicators were collated from all men’s teams and matches during the basketball tournament of the 2004-2016 Olympic Games (n = 156). Linear and non-linear analyses examined the relationship between match outcome and team performance indicator characteristics; namely, binary logistic regression and a conditional interference (CI) classification tree. The most parsimonious logistic regression model retained ‘assists’, ‘defensive rebounds’, ‘field-goal percentage’, ‘fouls’, ‘fouls against’, ‘steals’ and ‘turnovers’ (delta AIC <0.01; Akaike weight = 0.28) with a classification accuracy of 85.5%. Conversely, four performance indicators were retained with the CI classification tree with an average classification accuracy of 81.4%. However, it was the combination of ‘field-goal percentage’ and ‘defensive rebounds’ that provided the greatest probability of winning (93.2%). Match outcome during the men’s basketball tournaments at the Olympic Games was identified by a unique combination of performance indicators. Despite the average model accuracy being marginally higher for the logistic regression analysis, the CI classification tree offered a greater practical utility for coaches through its resolution of non-linear phenomena to guide team success.

Key words: Team sport, classification tree, machine learning, performance analysis, non-linear analysis, athlete
Key Points
A unique combination of team performance indicators explained 93.2% of winning observations in men’s basketball at the Olympics.
Monitoring of these team performance indicators may provide coaches with the capability to devise multiple game plans or strategies to enhance their likelihood of winning.
Incorporation of machine learning techniques with team performance indicators may provide a valuable and strategic approach to explain patterns within multivariate datasets in sport science.

 


  

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