Recent advancements in sports video analysis and computer vision techniques have significantly improved sports analysis, allowing for more in-depth insights into sports dynamics. Human motion analysis, the exact estimation of joint extension and acceleration, is crucial for evaluating professional athlete outcomes and improving overall athletic performance. This study aims to quantitatively assess the correlation between kinematic parameters and athletic performance in the standing long jump, by analyzing the motions of both professional and amateur athletes. We utilize Dynamic Time Warping (DTW) analysis to assess the similarities between a professional athlete and non-athletes during multiple jumping trials. Our results show that kinematic parameters significantly influence jump distance, with arm-swinging angles playing a substantial role in achieving longer jumps. However, knee angles exhibit less influence on attaining greater jumping distances. Despite technical challenges, such as markerless motion capture and low sampling frequency, our findings highlight the potential of DTW in measuring performance in the standing long jump, offering valuable insights for sports training and teaching methodologies.