Background Traditional linear approaches may not adequately capture the non-linear relationships and interactions among laboratory-derived sprint test metrics. This study aimed to predict flying sprint performance of elite-level male track cyclists using multiple linear regression and random forest models based on anaerobic ergometer metrics. Methods A total of 333 elite male track cyclists completed a 30-s all-out cycle-ergometer sprint test and indoor-velodrome flying 100-m and 200-m sprints. Eight predictors derived from the ergometer test result (body mass, peak power output, 30-s mean power, mean cadence, relative peak power, 5-s maximal mean power normalized to body mass, maximal 5-s power decline, and percentage power drop) were used to develop multiple linear regression and tuned random forest models. Model performance was evaluated on a held-out test set, with random forest hyperparameters optimized via nested cross-validation on the training data. The 100–200-m split time (flying 200-m minus flying 100-m time) was examined in unadjusted and peak power-adjusted models to assess the independent association of power decline metrics with second-half sprint performance. Results In mutually adjusted multiple linear regression models, 30-s average power was the only independent predictor of flying 100-m and 200-m times (p < 0.001). Test-set predictive performance was modest for linear regression (R2 = 0.326 for 100 m; R2 = 0.329 for 200 m) and marginally higher for the tuned random forest models (R2 = 0.369 for 100 m; R2 = 0.393 for 200 m). Random forest importance analyses consistently ranked relative peak power and 30-s average power as the most influential predictors across both outcomes. Percentage power drop was not independently associated with the 100–200-m split in either unadjusted or peak power-adjusted models. Conclusions Sprint performance could be predicted with modest accuracy from routinely collected ergometer metrics, with sustained power and relative peak power emerging as the primary contributors. Power decline metrics showed no independent association with second-half sprint performance after accounting for peak power, suggesting that fatigue resistance, as measured by a 30-s all-out cycle-ergometer sprint test, may not directly translate to competitive second-half sprint outcomes.

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

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2026年7月1日

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Taenam Kim,
Seung-Bo Park

摘要

Background

Traditional linear approaches may not adequately capture the non-linear relationships and interactions among laboratory-derived sprint test metrics. This study aimed to predict flying sprint performance of elite-level male track cyclists using multiple linear regression and random forest models based on anaerobic ergometer metrics.

Methods

A total of 333 elite male track cyclists completed a 30-s all-out cycle-ergometer sprint test and indoor-velodrome flying 100-m and 200-m sprints. Eight predictors derived from the ergometer test result (body mass, peak power output, 30-s mean power, mean cadence, relative peak power, 5-s maximal mean power normalized to body mass, maximal 5-s power decline, and percentage power drop) were used to develop multiple linear regression and tuned random forest models. Model performance was evaluated on a held-out test set, with random forest hyperparameters optimized via nested cross-validation on the training data. The 100–200-m split time (flying 200-m minus flying 100-m time) was examined in unadjusted and peak power-adjusted models to assess the independent association of power decline metrics with second-half sprint performance.

Results

In mutually adjusted multiple linear regression models, 30-s average power was the only independent predictor of flying 100-m and 200-m times (p < 0.001). Test-set predictive performance was modest for linear regression (R² = 0.326 for 100 m; R² = 0.329 for 200 m) and marginally higher for the tuned random forest models (R² = 0.369 for 100 m; R² = 0.393 for 200 m). Random forest importance analyses consistently ranked relative peak power and 30-s average power as the most influential predictors across both outcomes. Percentage power drop was not independently associated with the 100–200-m split in either unadjusted or peak power-adjusted models.

Conclusions

Sprint performance could be predicted with modest accuracy from routinely collected ergometer metrics, with sustained power and relative peak power emerging as the primary contributors. Power decline metrics showed no independent association with second-half sprint performance after accounting for peak power, suggesting that fatigue resistance, as measured by a 30-s all-out cycle-ergometer sprint test, may not directly translate to competitive second-half sprint outcomes.