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

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dc.contributor.authorFojtášek, Jancs
dc.contributor.authorBöhm, Michaelcs
dc.coverage.issue1cs
dc.coverage.volume113cs
dc.date.issued2025-10-28cs
dc.description.abstractThis study presents a novel method for the rapid identification of key performance indicators (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 comprehensive 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.en
dc.description.abstractThis study presents a novel method for the rapid identification of key performance indicators (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 comprehensive 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.en
dc.formattextcs
dc.format.extent1-10cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationEngineering Proceedings. 2025, vol. 113, issue 1, p. 1-10.en
dc.identifier.doi10.3390/engproc2025113012cs
dc.identifier.issn2673-4591cs
dc.identifier.orcid0000-0003-1689-2755cs
dc.identifier.orcid0000-0001-5845-2163cs
dc.identifier.other200050cs
dc.identifier.researcheridADE-4108-2022cs
dc.identifier.researcheridMIU-5392-2025cs
dc.identifier.scopus57204353284cs
dc.identifier.scopus57223847582cs
dc.identifier.urihttp://hdl.handle.net/11012/255772
dc.language.isoencs
dc.relation.ispartofEngineering Proceedingscs
dc.relation.urihttps://www.mdpi.com/2673-4591/113/1/12cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2673-4591/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectdata analysisen
dc.subjectkey performance indicators (KPIs)en
dc.subjectmathematical modelingen
dc.subjectmotorcycle dynamicsen
dc.subjectperformance optimizationen
dc.subjectrider-vehicle interactionen
dc.subjectdata analysis
dc.subjectkey performance indicators (KPIs)
dc.subjectmathematical modeling
dc.subjectmotorcycle dynamics
dc.subjectperformance optimization
dc.subjectrider-vehicle interaction
dc.titleDevelopment of a Data-Driven Methodology for Rapid Identification of Key Performance Indicators in Motorcycle Racingen
dc.title.alternativeDevelopment of a Data-Driven Methodology for Rapid Identification of Key Performance Indicators in Motorcycle Racingen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.grantNumberinfo:eu-repo/grantAgreement/MSM/EH/EH23_020/0008528cs
sync.item.dbidVAV-200050en
sync.item.dbtypeVAVen
sync.item.insts2026.01.06 14:53:31en
sync.item.modts2026.01.06 14:32:29en
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav automobilního a dopravního inženýrstvícs

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