Institute for Health and Sport (IHES), Victoria University, Australia; Western Bulldogs Football Club, Australia.
Institute for Health and Sport (IHES), Victoria University, Australia.
J Sci Med Sport. 2019 Oct;22(10):1157-1162. doi: 10.1016/j.jsams.2019.05.004. Epub 2019 May 10.
To evaluate the relationships between the athlete distribution of team performance indicators and quarter outcome in elite women's Australian Rules football matches.
Retrospective longitudinal cohort analysis.
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.
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.
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.
评估团队表现指标的运动员分布与精英女子澳式足球比赛季度结果之间的关系。
回顾性纵向队列分析。
从 2017 年和 2018 年澳大利亚足球联赛女子（AFLW）赛季的 56 场比赛中获得了 13 项表现指标。获得了每个运动员在所有比赛的每个季度的 13 项表现指标的绝对和相对值。进一步为每个表现指标提取了 11 个特征，共产生了 169 个特征。对不同的特征集和因变量运行了广义估计方程（GEE）和回归决策树，产生了 22 个单独的模型。
GEE 算法在所有因变量和特征集上产生的平均绝对误差略低于回归决策树模型。当考虑总得分与季度得分差距时，季度结果的解释更为准确。团队差异和个体运动员进入 50 强的第 75 百分位数是包含在模型中的最强特征。
按季度结果对表现统计数据进行建模，为每季度的比赛战术和教练提供了具体的实践信息。在当前的精英女子澳式足球比赛中，关键的高表现个体运动员在比赛中的熟练表现对成功的贡献大于集体团队努力。
