Purpose: This study aimed at comparing the predictive accuracy of the power law (PL), 2-parameter hyperbolic (HYP) and linear (LIN) models on elite 1-h track running performance, and evaluating pacing profile and running pattern of the men’s best two 1-h track running performances of all times. Methods: The individual running speed–distance profile was obtained for nine male elite runners using the three models. Different combinations of personal bests times (3000 m-marathon) were used to predict performance. The level of absolute agreement between predicted and actual performance was evaluated using intraclass correlation coefficient (ICC), paired t test and Bland–Altman analysis. A video analysis was performed to assess pacing profile and running pattern. Results: Regardless of the predictors used, no significant differences (p > 0.05) between predicted and actual performances were observed for the PL model. A good agreement was found for the HYP and LIN models only when the half-marathon was the longest event predictor used (ICC = 0.718–0.737, p < 0.05). Critical speed (CS) was highly dependent on the predictors used. Unlike CS, PLV20 (i.e., the running speed corresponding to a 20-min performance estimated using the PL model) was associated with 1-h track running performances (r = 0.722–0.807, p < 0.05). An even pacing profile with minimal changes of step length and frequency was observed. Conclusions: The PL model may offer the more realistic 1-h track running performance prediction among the models investigated. An even pacing might be the best strategy for succeeding in such running events.

Girardi M., Gattoni C., Sponza L., Marcora S.M., Micklewright D. (2022). Performance prediction, pacing profile and running pattern of elite 1-h track running events. SPORT SCIENCES FOR HEALTH, 18(4), 1457-1474 [10.1007/s11332-022-00945-w].

Performance prediction, pacing profile and running pattern of elite 1-h track running events

Marcora S. M.
;
2022

Abstract

Purpose: This study aimed at comparing the predictive accuracy of the power law (PL), 2-parameter hyperbolic (HYP) and linear (LIN) models on elite 1-h track running performance, and evaluating pacing profile and running pattern of the men’s best two 1-h track running performances of all times. Methods: The individual running speed–distance profile was obtained for nine male elite runners using the three models. Different combinations of personal bests times (3000 m-marathon) were used to predict performance. The level of absolute agreement between predicted and actual performance was evaluated using intraclass correlation coefficient (ICC), paired t test and Bland–Altman analysis. A video analysis was performed to assess pacing profile and running pattern. Results: Regardless of the predictors used, no significant differences (p > 0.05) between predicted and actual performances were observed for the PL model. A good agreement was found for the HYP and LIN models only when the half-marathon was the longest event predictor used (ICC = 0.718–0.737, p < 0.05). Critical speed (CS) was highly dependent on the predictors used. Unlike CS, PLV20 (i.e., the running speed corresponding to a 20-min performance estimated using the PL model) was associated with 1-h track running performances (r = 0.722–0.807, p < 0.05). An even pacing profile with minimal changes of step length and frequency was observed. Conclusions: The PL model may offer the more realistic 1-h track running performance prediction among the models investigated. An even pacing might be the best strategy for succeeding in such running events.
2022
Girardi M., Gattoni C., Sponza L., Marcora S.M., Micklewright D. (2022). Performance prediction, pacing profile and running pattern of elite 1-h track running events. SPORT SCIENCES FOR HEALTH, 18(4), 1457-1474 [10.1007/s11332-022-00945-w].
Girardi M.; Gattoni C.; Sponza L.; Marcora S.M.; Micklewright D.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/901559
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