Objective of the study was to assess the usefulness of step-by-step integrated monitoring metrics in forecasting performance in ski sprints, using the outcomes of the Russian Cup 2023-2024 as a case study. Methods and structure of the study. The research employed a comprehensive approach, incorporating the analysis of scientific literature, pedagogical assessments, biomedical techniques, and statistical methods. The research was conducted using Python 3.10 in the Google Colab environment, with the aid of regression analysis and the least squares method. The analysis was performed using standard Microsoft Office Excel software. Results and conclusions. It is revealed that the results of the stage-by-stage integrated control are informative for predicting the performance of cross-country skiers in the winter season. A regression model of the rating points of female athletes in the winter season sprint races has been developed. It was determined that the time to overcome the fourth and fifth test laps of the field test on ski scooters, the time of work on the ski ergometer before the threshold of anaerobic metabolism (PANO), the relative power of work at the last stage of the test on the ski ergometer, the stress level during the response of the glycolytic motor units of the right hand in a state of relative rest limit the performance of athletes in sprinting ski season races.
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