How accurate are runners’ prospective predictions of their race times?

Abstract : Metacognition is a domain which has illuminated our understanding of the regulation of cognition, but has yet to be applied in detail to more physical activities. We used half marathon finish time predictions from 7211 runners to investigate the factors that influence running performance metacognitive accuracy. In particular, we were concerned with the effects of experience, gender, and age on calibration. We expected more experienced runners to be better calibrated than less experienced ones. Given analogous findings in the domain of metacognition, we expected women to be less overconfident in their predictions, and better calibrated than male runners. Based on the metacognition literature, we expected that if older runners have effectively learned from previous experience, they would be as well-calibrated as younger runners. In contrast, uninformed inferences not based on performance feedback would lead to overestimating performance for older compared to younger runners. As expected, experience in terms of both club membership and previous race completion improved calibration. Unexpectedly though, females were more overconfident than males, overestimating their performance and demonstrating poorer calibration. A positive relationship was observed between age and prediction accuracy, with older runners showing better calibration. The present study demonstrates that data, collected before a test of physical activity, can inform our understanding of how participants anticipate their performance, and how this ability is affected by a number of demographic and situational variables. Athletes and coaches alike should be aware of these variables to better understand, organise, plan, and predict running performance, potentially leading to more appropriate training sessions and faster race finish times.
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Journal articles
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Submitted on : Friday, January 11, 2019 - 9:39:14 AM
Last modification on : Thursday, July 11, 2019 - 2:10:08 PM

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Konstantinos Liverakos, Kate Mcintosh, Christopher Moulin, Akira O'Connor. How accurate are runners’ prospective predictions of their race times?. PLoS ONE, Public Library of Science, 2018, 13 (8), pp.e0200744. ⟨10.1371/journal.pone.0200744⟩. ⟨hal-01977800⟩

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