Team performance indicators explain outcome during women's basketball matches at the Olympic games
Leicht, Anthony S., Gomez, Miguel A., and Woods, Carl T. (2017) Team performance indicators explain outcome during women's basketball matches at the Olympic games. Sports, 5 (4).
|
PDF (Published Version)
- Published Version
Available under License Creative Commons Attribution. Download (464kB) | Preview |
Abstract
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.
| Item ID: | 52037 |
|---|---|
| Item Type: | Article (Research - C1) |
| ISSN: | 2075-4663 |
| Keywords: | team sports, classification tree, machine learning, performance analysis, non-linear analysis, athlete |
| Additional Information: | This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0). |
| Date Deposited: | 17 Jan 2018 07:41 |
| FoR Codes: | 42 HEALTH SCIENCES > 4207 Sports science and exercise > 420799 Sports science and exercise not elsewhere classified @ 100% |
| SEO Codes: | 97 EXPANDING KNOWLEDGE > 970106 Expanding Knowledge in the Biological Sciences @ 100% |
| Downloads: |
Total: 1036 Last 12 Months: 7 |
| More Statistics |
Actions (Repository Staff Only)
 |
Item Control Page |