Abstract
Objectives
This study aimed to investigate predictors of cycling performance in U23 cyclists
by comparing traditional approaches to a novel method – the compound score. Thirty
male U23 cyclists (N = 30, age 20.1 ± 1.1 yrs, body mass 69.0 ± 6.9 kg, height 182.6 ± 6.2 cm,
O2max 73.8 ± 2.5 mL·kg−1·min−1) participated in this study.
Design
Power output information was derived from laboratory and field-testing during pre-season
and mean maximal power outputs (MMP) from racing season. Absolute and relative 5-min
MMP, 5-min MMP after 2000 kJ (MMP2000 kJ), allometric scaling and the compound score were compared to the race score and podium
(top 3) performance during a competitive season.
Methods
Positive and negative predictive values were calculated for all significant performance
variables for the likelihood of a podium performance.
Results
The absolute 5-min MMP of the field test revealed the highest negative predictive
capacity (82.4%, p = 0.012) for a podium performance. The compound score of the 5-min
MMP2000 kJ demonstrated the highest positive and average predictive capacity (83.3%, 78.0%,
p = 0.007 - respectively). The multi-linear regression analysis revealed a significant
predictive capacity between performance variables and the race score (R2 = 0.55, p = 0.015).
Conclusions
Collectively the results of the present study reveal that the compound score, alongside
absolute power, was able to predict the highest positive and average likelihood for
a podium performance. These findings can help to better understand performance capacity
from field data to predict future cycling success.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Journal of Science and Medicine in SportAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Exercise intensity and load during mass-start stage races in professional road cycling.Med Sci Sports Exerc. 2001; 33: 796-802https://doi.org/10.1097/00005768-200105000-00019
- Intensity and load characteristics of professional road cycling: differences between men’s and women’s races.Int J Sports Physiol Perform. 2019; 14: 296-302https://doi.org/10.1123/ijspp.2018-0190
- Demands of professional cycling races: influence of race category and result.Eur J Sport Sci. 2020; 16: 1-12https://doi.org/10.1080/17461391.2020.1788651
- Cross-sectional differences in race demands between junior, under 23, and professional road cyclists.Int J Sports Perform Analys. 2022; (aop): 1-8
- Physiological and biomechanical factors associated with elite endurance cycling performance.Med Sci Sports Exerc. 1991; 23: 93-107
- Determinants of cycling performance: a review of the dimensions and features regulating performance in elite cycling competitions.Sports Med Open. 2020; 6: 23https://doi.org/10.1186/s40798-020-00252-z
- The power profile predicts road cycling MMP.Int J Sports Med. 2010; 31: 397-401https://doi.org/10.1055/s-0030-1247528
- Predicting cycling performance in trained to elite male and female cyclists.Int J Sports Perform Analys. 2014; 9: 610-614https://doi.org/10.1123/IJSPP.2013-0040a
- The record power profile to assess performance in elite cyclists.Int J Sports Med. 2011; 32: 839-844https://doi.org/10.1055/s-0031-1279773
- Training, performance, and physiological predictors of a successful elite senior career in junior competitive road cyclists.Int J Sports Physiol Perform. 2018; 13: 1287-1292
- Do race results in youth competitions predict future success as a road cyclist? A retrospective study in the Italian Cycling Federation.Int J Sports Physiol Perform. 2022; 17: 621-626
- Maintaining power output with accumulating levels of work done is a key determinant for success in professional cycling.Med Sci Sports Exerc. 2021; 53: 1903-1910
- Power profiling, workload characteristics and race performance of U23 and professional cyclists during the multistage race Tour of the Alps.Int J Sports Physiol Perform. 2021; 16: 1-7https://doi.org/10.1123/ijspp.2020-0381
- Level ground and uphill cycling ability in professional road cycling.Med Sci Sports Exerc. 1999; 31: 878-885https://doi.org/10.1097/00005768-199906000-00017
- Critical power and aerodynamic drag accurately predict road time-trial performance in British champion cyclists.Med Sci Sports Exerc. 2011; 43: 160-161
- Influence of body position when considering the ecological validity of laboratory time-trial cycling performance.J Sports Sci. 2008; 26: 1269-1278https://doi.org/10.1080/02640410802183585
- Allometric scaling of uphill cycling performance.Int J Sports Med. 2008; 29: 753-757
- The influence of body mass in endurance bicycling.Med Sci Sports Exerc. 1994; 26: 58-63
- The physical demands and power profile of professional men’s cycling races: an updated review.Int J Sports Physiol Perform. 2021; 16: 3-12https://doi.org/10.1123/IJSPP.2020-0508
- The relationship between training characteristics and durability in professional cyclists across a competitive season.Eur J Sport Sci. 2022; (AoP): 1-17https://doi.org/10.1080/17461391.2022.2049886
- The relationship between physiological characteristics and durability in male professional cyclists.Med Sci Sports Exerc. 2022; (Published online)https://doi.org/10.1249/MSS.0000000000003024
- Five-minute power-based test to predict maximal oxygen consumption in road cycling.Int J Sports Physiol Perform. 2021; 17: 9-15https://doi.org/10.1123/ijspp.2020-0923
- Allometric scaling of peak power output accurately predicts time trial performance and maximal oxygen consumption in trained cyclists.Br J Sports Med. 2012; 46: 36-41
- Preferred pedalling cadence in professional cycling.Med Sci Sports Exerc. 2001; 33: 1361-1366https://doi.org/10.1097/00005768-200108000-00018
- A static method for obtaining a calibration factor for SRM bicycle power cranks.Sports Eng. 2005; 8: 137-144
- Power profiling and workload characteristcs in U23 and professional cyclists during the multistage race “Tour of the Alps”.Int J Sports Med. 2020; 16: 1089-1095
- The record power profile of male professional cyclists: normative values obtained from a large database.Int J Sports Physiol Perform. 2022; 1: 1-10
- The record power profile of male professional cyclists: fatigue matters.Int J Sports Physiol Perform. 2022; 1: 1-6
- Morris A.S. Langari R.B.T.M. Third E. Statistical Analysis of Measurements Subject to Random Errors. Academic Press, 2020https://doi.org/10.1016/B978-0-12-817141-7.00004-9
- Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation.Caspain J Intern Med. 2013; 4: 627-635
- Likert scales, levels of measurement and the “laws” of statistics.Adv Health Sci Edu. 2010; 15: 625-632
- Aerodynamic drag in cycling pelotons: new insights by CFD simulation and wind tunnel testing.J Wind Eng Ind Aerodyn. 2018; 179: 319-337
- Mechanisms of performance improvements due to a leading teammate during uphill cycling.Int J Sports Med. 2018; 13: 1215-1222
- Elite versus non-elite cyclist–stepping up to the international/elite ranks from U23 cycling.J Sports Sci. 2022; (Published online): 1-11https://doi.org/10.1080/02640414.2022.2117394
- Physiological and performance characteristics of male professional road cyclists.Sports Med. 2001; 31: 479-487https://doi.org/10.2165/00007256-200131070-00003
- Level ground and uphill cycling ability in elite female mountain bikers and road cyclists.Eur J Appl Physiol. 2008; 102https://doi.org/10.1007/s00421-007-0590-9
- Climbing performance in U23 and professional cyclists during a multi-stage race.Int J Sports Med. 2021; 43: 161-167
- “Engine matters”: A first large scale data driven study on cyclists’ performance.in: 2013 IEEE 13th International Conference on Data Mining Workshops. IEEE, 2013: 147-153
- Aerodynamic analysis of uphill drafting in cycling.Sports Eng. 2021; 24: 1-11
- Durability and repeatability of professional cyclists during a Grand Tour.Eur J Sport Sci. 2021; (Published online): 1-8
- Accuracy of cycling power meters against a mathematical model of treadmill cycling.Int J Sports Med. 2017; 38: 454-461https://doi.org/10.1055/s-0043-102945
- Caveats and recommendations to assess the validity and reliability of cycling power meters: a systematic scoping review.Sensors. 2022; 22: 1-32https://doi.org/10.3390/s22010386
- UCI Constitution and Regulations.
Article info
Publication history
Published online: November 30, 2022
Accepted:
November 28,
2022
Received in revised form:
November 16,
2022
Received:
July 10,
2022
Identification
Copyright
© 2022 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
