DOI: 10.66106/tiyua5.20250207 ISSN: 3105-7608

米兰科尔蒂纳丹佩佐滑行中心单人雪车项目成绩预测研究（Performance Prediction for the Women’s Monobob Event at the Milano Cortina Danpezzo Sliding Centre）

徐嘉欣 Jiaxin Xu, 尹一全 Yiquan Yin
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Abstract：In preparation for the 2026 Milan Winter Olympics, this study focuses on Chinese women's monobob athlete Huai Mingming, developing a multi-dimensional performance prediction framework integrating in-depth interviews, random forest regression, and the grey GM(1,1) model. Using sliding data collected from the Eugenio Monti track in Cortina d'Ampezzo— including start time, intermediate timing points, and finish speed—the study scientifically imputed missing data and validated physical relationships among variables. The random forest model revealed a significant negative correlation between finish speed (SP.1) and total time (TIME) (r = –0.914), with a prediction error below 1.1%, confirming model reliability under small-sample conditions. The grey GM(1,1) model, based on seven trial runs, predicted the eighth run time as 65.65 seconds, with a maximum fitting error of only 0.015%, demonstrating strong trend prediction capability. The study indicates that the athlete's track adaptability improved significantly over time (TIME decreased by 2.41 seconds daily on average, R² = 0.762). The findings provide data-driven support for developing targeted training strategies for the Milan Olympics, while also highlighting the need for expanded data sources and model optimization to enhance prediction accuracy.

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