Technology Category
Humans and AI: Planning and Decision Support for Human-Machine TeamsKnowledge Representation and Reasoning: Qualitative ReasoningMultiagent Systems: Mechanism Design
Feature Impact Analysis on Top Long-Jump Performances with Quantile Random Forest and Explainable AI Techniques
🤖 AI Summary
This study identifies key biomechanical determinants of elite long jump performance (top 10% in World Athletics Championships finals) and their sex-specific mechanisms, moving beyond the traditional speed-centric paradigm. Method: We propose a quantile random forest modeling framework integrated with interpretable AI techniques—including SHAP values, partial dependence plots (PDP), and individual conditional expectation (ICE) plots—to detect nonlinear synergistic effects while controlling for approach velocity. Contribution/Results: We find that, for men, a pre-takeoff knee angle >169° in the support leg significantly enhances performance; for women, landing stability and coordination of the approach gait are more critical—and exert effects only in synergy with velocity. This work is the first to systematically disentangle and quantify the differential contributions of support-phase joint angles, landing control, and approach pattern to elite long jump performance, providing interpretable, sex-specific biomechanical evidence to guide event-specific training design and talent identification.
Technology Category
Application Category
📝 Abstract
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°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.
Problem
Research questions and friction points this paper is trying to address.
Identify key biomechanical features affecting long-jump performance
Analyze combined effects of features on elite-level jumps
Develop framework for feature impact using explainable AI techniques
Innovation
Methods, ideas, or system contributions that make the work stand out.
Quantile Random Forest for performance analysis
SHAP and PDPs for model interpretation
Focus on elite-level biomechanical features
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