The relationship of team and individual athlete performances on match quarter outcome in elite women's Australian Rules football. Issue 10 (October 2019)
- Record Type:
- Journal Article
- Title:
- The relationship of team and individual athlete performances on match quarter outcome in elite women's Australian Rules football. Issue 10 (October 2019)
- Main Title:
- The relationship of team and individual athlete performances on match quarter outcome in elite women's Australian Rules football
- Authors:
- Cust, Emily E.
Sweeting, Alice J.
Ball, Kevin
Anderson, Hamish
Robertson, Sam - Abstract:
- Abstract: Objectives: To evaluate the relationships between the athlete distribution of team performance indicators and quarter outcome in elite women's Australian Rules football matches. Design: Retrospective longitudinal cohort analysis. Methods: Thirteen performance indicators were obtained from 56 matches across the 2017 and 2018 Australian Football League Women's (AFLW) seasons. Absolute and relative values of 13 performance indicators were obtained for each athlete, in each quarter of all matches. Eleven features were further extracted for each performance indicator, resulting in a total of 169 features. Generalised estimating equations (GEE) and regression decision trees were run across the different feature sets and dependent variables, resulting in 22 separate models. Results: The GEE algorithm produced slightly lower mean absolute errors across all dependent variables and feature sets comparative to the regression decision tree models. Quarter outcome was more accurately explained when considered as total points scored comparative to quarter score margin. Team differential and the 75th percentile of individual athlete Inside 50s were the strongest features included in the models. Conclusions: Modelling performance statistics by quarter outcomes provides specific practical information for in-game tactics and coaching in relation to athlete performances each quarter. Within the current elite women's Australian Rules football competition, key high performingAbstract: Objectives: To evaluate the relationships between the athlete distribution of team performance indicators and quarter outcome in elite women's Australian Rules football matches. Design: Retrospective longitudinal cohort analysis. Methods: Thirteen performance indicators were obtained from 56 matches across the 2017 and 2018 Australian Football League Women's (AFLW) seasons. Absolute and relative values of 13 performance indicators were obtained for each athlete, in each quarter of all matches. Eleven features were further extracted for each performance indicator, resulting in a total of 169 features. Generalised estimating equations (GEE) and regression decision trees were run across the different feature sets and dependent variables, resulting in 22 separate models. Results: The GEE algorithm produced slightly lower mean absolute errors across all dependent variables and feature sets comparative to the regression decision tree models. Quarter outcome was more accurately explained when considered as total points scored comparative to quarter score margin. Team differential and the 75th percentile of individual athlete Inside 50s were the strongest features included in the models. Conclusions: Modelling performance statistics by quarter outcomes provides specific practical information for in-game tactics and coaching in relation to athlete performances each quarter. Within the current elite women's Australian Rules football competition, key high performing individual athletes' skilled performances within matches contribute more to success rather than a collective team effort. … (more)
- Is Part Of:
- Journal of science and medicine in sport. Volume 22:Issue 10(2019)
- Journal:
- Journal of science and medicine in sport
- Issue:
- Volume 22:Issue 10(2019)
- Issue Display:
- Volume 22, Issue 10 (2019)
- Year:
- 2019
- Volume:
- 22
- Issue:
- 10
- Issue Sort Value:
- 2019-0022-0010-0000
- Page Start:
- 1157
- Page End:
- 1162
- Publication Date:
- 2019-10
- Subjects:
- Sports analytics -- AFLW -- Machine learning -- Performance analysis
Sports sciences -- Periodicals
Sports medicine -- Periodicals
Exercise -- Physiological aspects -- Periodicals
Sports -- physiology -- Periodicals
Sports Medicine -- Periodicals
Sportgeneeskunde
617.102705 - Journal URLs:
- http://www.sciencedirect.com/science/journal/14402440 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.jsams.2019.05.004 ↗
- Languages:
- English
- ISSNs:
- 1440-2440
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5054.840000
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British Library HMNTS - ELD Digital store - Ingest File:
- 11701.xml