PDF(Pubmed)
Abstract：
The aim of this study was to test a machine learning (ML) model to predict high-intensity actions and body impacts during youth football training. Sixty under-15, -17, and -19 sub-elite Portuguese football players were monitored over a 6-week period. External training load data were collected from the target variables of accelerations (ACCs), decelerations (DECs), and dynamic stress load (DSL) using an 18 Hz global positioning system (GPS). Additionally, we monitored the perceived exertion and biological characteristics using total quality recovery (TQR), rating of perceived exertion (RPE), session RPE (sRPE), chronological age, maturation offset (MO), and age at peak height velocity (APHV). The ML model was computed by a feature selection process with a linear regression forecast and bootstrap method. The predictive analysis revealed that the players\' MO demonstrated varying degrees of effectiveness in predicting their DEC and ACC across different ranges of IQR. After predictive analysis, the following performance values were observed: DEC (x¯predicted = 41, β = 3.24, intercept = 37.0), lower IQR (IQRpredicted = 36.6, β = 3.24, intercept = 37.0), and upper IQR (IQRpredicted = 46 decelerations, β = 3.24, intercept = 37.0). The player\'s MO also demonstrated the ability to predict their upper IQR (IQRpredicted = 51, β = 3.8, intercept = 40.62), lower IQR (IQRpredicted = 40, β = 3.8, intercept = 40.62), and ACC (x¯predicted = 46 accelerations, β = 3.8, intercept = 40.62). The ML model showed poor performance in predicting the players\' ACC and DEC using MO (MSE = 2.47-4.76; RMSE = 1.57-2.18: R2 = -0.78-0.02). Maturational concerns are prevalent in football performance and should be regularly checked, as the current ML model treated MO as the sole variable for ACC, DEC, and DSL. Applying ML models to assess automated tracking data can be an effective strategy, particularly in the context of forecasting peak ACC, DEC, and bodily effects in sub-elite youth football training.
摘要：
这项研究的目的是测试机器学习（ML）模型，以预测青少年足球训练期间的高强度动作和身体撞击。在6周内对60名15岁以下，17岁和-19岁以下的亚精英葡萄牙足球运动员进行了监测。外部训练负荷数据是从加速度(ACC)的目标变量中收集的，减速(DEC)，以及使用18Hz全球定位系统(GPS)的动态应力载荷(DSL)。此外,我们使用总质量恢复(TQR)监测感知的劳累和生物学特征，感知努力(RPE)评级，会话RPE(SRPE)，实际年龄,成熟补偿(MO)，和年龄在峰值高度速度（APHV）。ML模型是通过使用线性回归预测和自举方法的特征选择过程来计算的。预测分析显示，玩家\'MO在预测不同范围的IQR的DEC和ACC方面表现出不同程度的有效性。经过预测分析，观察到以下性能值：DEC（x'预测值=41，β=3.24，截距=37.0），较低的IQR(IQRpredicted=36.6，β=3.24，截距=37.0)，和较高的IQR(IQR预测=46减速度，β=3.24,截距=37.0)。玩家的MO还展示了预测他们的上IQR的能力（IQRpredicted=51，β=3.8，截距=40.62），较低的IQR(预测的IQR=40，β=3.8，截距=40.62)，和ACC(x’predicted=46个加速度，β=3.8,截距=40.62)。ML模型在使用MO预测玩家的ACC和DEC方面表现不佳（MSE=2.47-4.76；RMSE=1.57-2.18：R2=-0.78-0.02）。足球表演中普遍存在着成熟的问题，应该定期检查，由于当前的ML模型将MO视为ACC的唯一变量，DEC,DSL。应用机器学习模型来评估自动跟踪数据可能是一种有效的策略，特别是在预测峰值ACC的背景下，DEC,亚精英青少年足球训练中的身体效应。
