Data for a publication - Development of a Data-Driven Methodology for Rapid Identification of Key Performance Indicators in Motorcycle Racing †

This study presents a novel method for the rapid identification of key performance indi cators (KPIs) from measured riding data of a Ducati Panigale V2 motorcycle, aimed at enhancing racing performance through a deeper understanding of rider-vehicle interaction. The methodology involves the design and implementation of mathematical tools within the RaceStudio3 software to analyze data from the motorcycle’s sensor system. This approach facilitates the swift detection of critical events, including gearshift delays, improper throttle control, and suspension issues. The fusion of data from the motorcycle enables a compre hensive evaluation of the rider’s influence on performance. The results demonstrate the potential of the proposed method to provide valuable insights for optimizing motorcycle setup and rider technique.本研究提出一种全新方法，可从杜卡迪Panigale V2摩托车的实测骑行数据中快速识别关键性能指标（Key Performance Indicators，KPIs），旨在通过深入理解骑手与车辆的交互关系，提升赛事竞技表现。该方法通过在RaceStudio3软件中设计并实现数学工具，对摩托车传感器系统采集的数据开展分析。此方法可快速检测各类关键事件，包括换挡延迟、油门控制不当以及悬架系统异常等。通过融合摩托车各传感器采集的数据，能够全面评估骑手对车辆性能的影响程度。研究结果表明，所提方法可为优化摩托车赛事调校与骑手驾驶技术提供极具价值的参考依据，展现出良好的应用潜力。