Purpose: To assess a coach`s subjective assessment of their athletes` performances and whether the use of athlete-monitoring tools could improve on the coach`s prediction to identify performance changes. Methods: Eight highly trained swimmers (7 male and 1 female, age 21.6 [2.0] y) recorded perceived fatigue, total quality recovery, and heart-rate variability over a 9-month period. Prior to each race of the swimmers` main 2 events, the coach (n=1) was presented with their previous race results and asked to predict their race time. All race results (n=93) with aligning coach`s predictions were recorded and classified as a dichotomous outcome (0=no change; 1=performance decrement or improvement [change +/- > or < smallest meaningful change]). A generalized estimating equation was used to assess the coach`s accuracy and the contribution of monitoring variables to the model fit. The probability from generalized estimating equation models was assessed with receiver operating characteristic curves to identify the model`s accuracy from the area under the curve analysis. Results: The coach`s predictions had the highest diagnostic accuracy to identify both decrements (area under the curve: 0.93; 95% confidence interval, 0.88-0.99) and improvements (area under the curve: 0.89; 95% confidence interval, 0.83-0.96) in performance. Conclusions: These findings highlight the high accuracy of a coach`s subjective assessment of performance. Furthermore, the findings provide a future benchmark for athlete-monitoring systems to be able to improve on a coach`s existing understanding of swimming performance.
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