a Institute for Health and Sport (IHES), Victoria University , Melbourne , Australia.
b Western Bulldogs Football Club , Footscray , Australia.
J Sports Sci. 2019 Jul;37(14):1600-1608. doi: 10.1080/02640414.2019.1577941. Epub 2019 Feb 12.
In team-sport, physical and skilled output is often described via aggregate parameters including total distance and number of skilled involvements. However, the degree to which these output change throughout a team-sport match, as a function of time, is relatively unknown. This study aimed to identify and describe segments of physical and skilled output in team-sport matches with an example in Australian Football. The relationship between the number of change points and level of similarity was also quantified. A binary segmentation algorithm was applied to the velocity time series, collected via wearable sensors, of 37 Australian football players (age: 23 ± 4 years, height: 187 ± 8 cm, mass: 86 ± 9 kg). A change point quotient of between 1 and 15 was used. For these quotients, descriptive statistics, spectral features and a sum of skilled involvements were extracted. Segment similarity for each quotient was evaluated using a random forest model. The strongest classification features in the model were spectral entropy and skewness. Offensive and defensive involvements were the weakest features for classification, suggesting skilled output is dependent on match circumstances. The methodology presented may have application in comparing the specificity of training to matches and designing match rotation strategies.
在团队运动中，通常通过总距离和熟练参与次数等综合参数来描述体能和技能表现。然而，这些表现随着时间的推移在团队运动比赛中变化的程度相对未知。本研究旨在以澳大利亚足球为例，确定和描述团队运动比赛中体能和技能表现的片段，并量化变化点数量与相似性水平之间的关系。使用穿戴式传感器收集 37 名澳大利亚足球运动员（年龄：23±4 岁，身高：187±8 厘米，体重：86±9 公斤）的速度时间序列，并应用二进制分段算法。使用的变化点商数在 1 到 15 之间。对于这些商数，提取了描述性统计、频谱特征和熟练参与次数的总和。使用随机森林模型评估每个商数的片段相似性。模型中的最强分类特征是频谱熵和偏度。进攻和防守参与是分类的最弱特征，这表明技能表现取决于比赛情况。所提出的方法可能适用于比较训练与比赛的特异性以及设计比赛轮换策略。
