Explaining match outcome during the men's basketball tournament at the Olympic Games

Leicht, Anthony, Gomez, Miguel, and Woods, Carl (2017) Explaining match outcome during the men's basketball tournament at the Olympic Games. Journal of Sports Science and Medicine, 16 (4). pp. 468-473.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: https://www.jssm.org/newback.php?id=16&i...
 
6
1


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.

Item ID: 49952
Item Type: Article (Research - C1)
ISSN: 1303-2968
Keywords: team sport; classification tree; machine learning; performance analysis; non-linear analysis; athlete
Date Deposited: 25 Aug 2017 04:25
FoR Codes: 11 MEDICAL AND HEALTH SCIENCES > 1106 Human Movement and Sports Science > 110699 Human Movement and Sports Science not elsewhere classified @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970106 Expanding Knowledge in the Biological Sciences @ 100%
Downloads: Total: 1
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page