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Machine Learning-Based Prediction of Track Sprint Cycling Performance in Elite-Level Male Cyclists: A Multinational

Taenam Kim¹, Seung-Bo Park^2,3

¹Graduate School of Sports Medicine, CHA University, Seongnam-si, 13503, Republic of Korea.

Sports Medicine - Open

|July 1, 2026

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Elite cyclists' sprint performance can be predicted using laboratory ergometer tests, with sustained power and relative peak power being key indicators. Fatigue resistance metrics from a 30-second sprint test did not independently predict second-half sprint success.

Area of Science:

Sports Science
Biomechanics
Exercise Physiology

Background:

Traditional linear models struggle to represent complex, non-linear relationships in sprint test metrics.
Elite male track cyclists' performance is influenced by various anaerobic ergometer metrics.

Purpose of the Study:

To predict flying sprint performance in elite male track cyclists.
To compare the predictive accuracy of multiple linear regression and random forest models.
To identify key ergometer metrics for predicting sprint outcomes.

Main Methods:

Utilized data from 333 elite male track cyclists performing 30-s all-out cycle-ergometer and indoor-velodrome sprints.
Developed and compared multiple linear regression and tuned random forest models using eight ergometer-derived predictors.

Keywords:

Anaerobic power Fatigue resistance Machine learning Peak power Performance prediction Random forest Sprint performance Track cycling

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Evaluated model performance on a held-out test set and optimized random forest hyperparameters via nested cross-validation.

Main Results:

30-second average power was the sole independent predictor of flying 100-m and 200-m times in linear regression models.
Random forest models showed marginally higher predictive accuracy (R²=0.37-0.39) compared to linear regression (R²=0.33).
Relative peak power and 30-second average power were identified as the most influential predictors by random forest importance analyses.

Conclusions:

Sprint performance can be predicted with moderate accuracy using standard ergometer metrics.
Sustained power (30-s average power) and relative peak power are primary predictors of sprint performance.
Power decline metrics did not independently predict second-half sprint performance, indicating fatigue resistance may not directly correlate with competitive outcomes.