标题： 基于分位数随机森林和可解释人工智能技术的顶级跳远表现特征影响分析 显示英文标题

标题： Feature Impact Analysis on Top Long-Jump Performances with Quantile Random Forest and Explainable AI Techniques

摘要： 生物力学特征已成为评估运动员技术的重要指标。 传统上，专家提出显著特征并使用物理方程进行评估。 然而，人体及其运动的复杂性使得一些特征与运动员最终表现之间的关系难以明确分析。 随着现代机器学习和统计学的进步，数据分析师方法在体育分析中变得越来越重要。 在本研究中，我们利用机器学习模型分析世界田径锦标赛跳远决赛中专家提出的生物力学特征。 分析的目标包括识别对顶级跳跃表现贡献最大的特征，并探索这些关键特征的综合效应。 使用分位数回归，我们建立了生物力学特征集与目标变量（有效距离）之间的关系，特别关注精英级别的跳跃。 为了解释模型，我们应用了SHapley Additive exPlanations（SHAP）以及部分依赖图（PDPs）和个体条件期望（ICE）图。 研究结果表明，除了已知的速度相关特征外，特定的技术方面也起着关键作用。 对于男性运动员，起跳前支撑腿的膝关节角度被确定为在我们的数据集中达到前10%表现的关键因素，角度大于169{\deg }显著提高了跳跃表现。 相反，对于女性运动员，落地姿势和助跑步技术成为影响前10%表现的最关键特征，同时伴随着速度。 本研究建立了一个分析各种特征对运动表现影响的框架，特别强调顶级比赛的表现。 显示英文摘要

摘要： Biomechanical features have become important indicators for evaluating athletes' techniques. Traditionally, experts propose significant features and evaluate them using physics equations. However, the complexity of the human body and its movements makes it challenging to explicitly analyze the relationships between some features and athletes' final performance. With advancements in modern machine learning and statistics, data analytics methods have gained increasing importance in sports analytics. In this study, we leverage machine learning models to analyze expert-proposed biomechanical features from the finals of long jump competitions in the World Championships. The objectives of the analysis include identifying the most important features contributing to top-performing jumps and exploring the combined effects of these key features. Using quantile regression, we model the relationship between the biomechanical feature set and the target variable (effective distance), with a particular focus on elite-level jumps. To interpret the model, we apply SHapley Additive exPlanations (SHAP) alongside Partial Dependence Plots (PDPs) and Individual Conditional Expectation (ICE) plots. The findings reveal that, beyond the well-documented velocity-related features, specific technical aspects also play a pivotal role. For male athletes, the angle of the knee of the supporting leg before take-off is identified as a key factor for achieving top 10% performance in our dataset, with angles greater than 169{\deg}contributing significantly to jump performance. In contrast, for female athletes, the landing pose and approach step technique emerge as the most critical features influencing top 10% performances, alongside velocity. This study establishes a framework for analyzing the impact of various features on athletic performance, with a particular emphasis on top-performing events.