Optimal body mass normalization of power output for accurate prediction of estimated cycling performance over complex time-trial courses
IntroductionPower profiling is widely used in cycling performance analysis, but both absolute and mass-normalized power outputs have limitations as performance indicators, as they neglect external factors such as terrain, wind, aerodynamic drag, and pacing strategy. To address these limitations, thi...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Sports and Active Living |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fspor.2025.1599319/full |
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| author | Marton Horvath Erik P. Andersson |
| author_facet | Marton Horvath Erik P. Andersson |
| author_sort | Marton Horvath |
| collection | DOAJ |
| description | IntroductionPower profiling is widely used in cycling performance analysis, but both absolute and mass-normalized power outputs have limitations as performance indicators, as they neglect external factors such as terrain, wind, aerodynamic drag, and pacing strategy. To address these limitations, this study introduced a numerical method to quantify how external forces acting on the cyclist influence the conversion of power output into race velocity. Thus, the study aimed to enable accurate prediction of cycling performance based on estimated mean power output over complex time-trial courses.MethodsTime-trial performances of five elite-level road cyclist profiles—a sprinter, climber, all-rounder, general classification (GC) contender, and a time trialist—were estimated using the power-duration relationship and previously published normative data. These performance estimates were applied to both simplified hypothetical courses and complex real-world time-trial courses. Optimal mass exponents for the power-to-mass ratio were determined based on the estimated average speeds over the respective course sections, cyclist morphology, and external factors such as gradient and wind velocity.ResultsAcross two recent Grand Tour individual time-trial courses, stage 21 of the 2024 Tour de France and stage 7 of the 2024 Giro d’Italia, the duration-weighted optimally mass-normalized power output metrics were W/kg0.6068 and W/kg0.4891, respectively. These metrics accurately predicted the estimated performances of the five defined cyclist profiles (R2=0.99 for both).DiscussionThe results indicate that the duration-weighted optimal mass exponents for the power-to-mass ratio are course-specific. By deriving optimal mass exponents across various modeled courses and wind conditions, the study was able to precisely quantify the influence of road gradient, headwind speed, and bicycle mass on the conversion of power output relative to body mass into speed. Further research is needed to validate the presented method for determining optimal mass exponents in real-world performance settings. |
| format | Article |
| id | doaj-art-df5889ac423c45ffbf9ad9e1503e7c1f |
| institution | DOAJ |
| issn | 2624-9367 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Sports and Active Living |
| spelling | doaj-art-df5889ac423c45ffbf9ad9e1503e7c1f2025-08-20T03:06:57ZengFrontiers Media S.A.Frontiers in Sports and Active Living2624-93672025-08-01710.3389/fspor.2025.15993191599319Optimal body mass normalization of power output for accurate prediction of estimated cycling performance over complex time-trial coursesMarton HorvathErik P. AnderssonIntroductionPower profiling is widely used in cycling performance analysis, but both absolute and mass-normalized power outputs have limitations as performance indicators, as they neglect external factors such as terrain, wind, aerodynamic drag, and pacing strategy. To address these limitations, this study introduced a numerical method to quantify how external forces acting on the cyclist influence the conversion of power output into race velocity. Thus, the study aimed to enable accurate prediction of cycling performance based on estimated mean power output over complex time-trial courses.MethodsTime-trial performances of five elite-level road cyclist profiles—a sprinter, climber, all-rounder, general classification (GC) contender, and a time trialist—were estimated using the power-duration relationship and previously published normative data. These performance estimates were applied to both simplified hypothetical courses and complex real-world time-trial courses. Optimal mass exponents for the power-to-mass ratio were determined based on the estimated average speeds over the respective course sections, cyclist morphology, and external factors such as gradient and wind velocity.ResultsAcross two recent Grand Tour individual time-trial courses, stage 21 of the 2024 Tour de France and stage 7 of the 2024 Giro d’Italia, the duration-weighted optimally mass-normalized power output metrics were W/kg0.6068 and W/kg0.4891, respectively. These metrics accurately predicted the estimated performances of the five defined cyclist profiles (R2=0.99 for both).DiscussionThe results indicate that the duration-weighted optimal mass exponents for the power-to-mass ratio are course-specific. By deriving optimal mass exponents across various modeled courses and wind conditions, the study was able to precisely quantify the influence of road gradient, headwind speed, and bicycle mass on the conversion of power output relative to body mass into speed. Further research is needed to validate the presented method for determining optimal mass exponents in real-world performance settings.https://www.frontiersin.org/articles/10.3389/fspor.2025.1599319/fullallometric scalingcritical powernumerical methodsperformance predictionpower-duration relationshipsports engineering |
| spellingShingle | Marton Horvath Erik P. Andersson Optimal body mass normalization of power output for accurate prediction of estimated cycling performance over complex time-trial courses Frontiers in Sports and Active Living allometric scaling critical power numerical methods performance prediction power-duration relationship sports engineering |
| title | Optimal body mass normalization of power output for accurate prediction of estimated cycling performance over complex time-trial courses |
| title_full | Optimal body mass normalization of power output for accurate prediction of estimated cycling performance over complex time-trial courses |
| title_fullStr | Optimal body mass normalization of power output for accurate prediction of estimated cycling performance over complex time-trial courses |
| title_full_unstemmed | Optimal body mass normalization of power output for accurate prediction of estimated cycling performance over complex time-trial courses |
| title_short | Optimal body mass normalization of power output for accurate prediction of estimated cycling performance over complex time-trial courses |
| title_sort | optimal body mass normalization of power output for accurate prediction of estimated cycling performance over complex time trial courses |
| topic | allometric scaling critical power numerical methods performance prediction power-duration relationship sports engineering |
| url | https://www.frontiersin.org/articles/10.3389/fspor.2025.1599319/full |
| work_keys_str_mv | AT martonhorvath optimalbodymassnormalizationofpoweroutputforaccuratepredictionofestimatedcyclingperformanceovercomplextimetrialcourses AT erikpandersson optimalbodymassnormalizationofpoweroutputforaccuratepredictionofestimatedcyclingperformanceovercomplextimetrialcourses |