Abstract：
Purpose: Monitoring players\' training load allows practitioners to enhance physical performance while reducing injury risk. The aim of this study was to identify the key external load indicators in professional U19 soccer. Methods: Twenty-four-professional Italian young (U19) soccer players were monitored by using the rating of perceived exertion (CR-10 RPE scale) and a wearable inertial sensor during the competitive season. Three main components were detected by a Principal Component Analysis (PCA): i) volume metabolic related component, ii) intensity mechanical stimuli component, and iii) intensity metabolic/mechanical component. We hence computed two scores (i.e. Performance [PERF] and total workload [WORK]) permitting to investigate the weekly microcycle. Results: Correlation analysis showed that scores (i.e. PERF and WORK) are low correlated (r = -0.20) suggesting that they were independent. Autocorrelation analysis showed that a weekly microcycle is detectable in all the scores. Two-way ANOVA RM showed a statistical difference between match day (MD) and playing position for the three PCA components and PERF score. Conclusion: We proposed an innovative approach to assess both the players\' physical performance and training load by using a machine learning approach allowing reducing a large dataset in an objective way. This approach may help practitioners to prescribe the training in the microcycle based on the two scores.
摘要：
目的：监测运动员的训练负荷可以使从业者提高身体表现，同时降低受伤风险。这项研究的目的是确定职业U19足球的关键外部负荷指标。方法：在比赛季节中，通过使用感知劳累评分（CR-10RPE量表）和可穿戴惯性传感器来监测二十四名专业的意大利年轻（U19）足球运动员。通过主成分分析（PCA）检测到三个主要成分：i）体积代谢相关成分，ii)强度机械刺激分量，和iii)强度代谢/机械组分。因此，我们计算了两个分数（即性能[PERF]和总工作量[WORK]），允许调查每周的微循环。结果：相关分析表明，得分（即PERF和WORK）是低相关性（r=-0.20），表明它们是独立的。自相关分析表明，在所有评分中都可以检测到每周的微循环。双向ANOVARM显示了三个PCA分量和PERF得分的比赛日（MD）和比赛位置之间的统计差异。结论：我们提出了一种创新的方法，通过使用机器学习方法来评估运动员的身体表现和训练负荷，从而以客观的方式减少大型数据集。这种方法可以帮助从业者根据两个分数在微循环中规定训练。
