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
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徐嘉欣
,尹一全
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1.烟台南山学院体育学院，山东烟台，265713；2.北京体育大学中国冰雪运动学院，北京，100084
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摘要：为备战 2026 年米兰冬奥会，本研究以中国女子单人雪车运动员怀明明为研究对象，结合深度访谈、随机森林回归模型与灰色GM(1,1)模型，构建多维度成绩预测体系。通过采集其在科尔蒂纳丹佩佐尤金尼奥蒙蒂赛道的试滑数据，包括起始阶段时间、分段计时点与终点速度，对缺失数据进行科学插补，并验证变量间的物理关系。随机森林模型结果显示，终点速度（SP.1）与总耗时（TIME）呈显著负相关（r =–0.914），预测误差低于 1.1%，验证了模型在小样本条件下的可靠性。灰色 GM(1,1) 模型基于 7 次试滑成绩预测第 8 次成绩为 65.65 秒，拟合误差最大值仅为 0.015%，表现出良好的趋势捕捉能力。研究表明，运动员对赛道的适应性随训练时间显著提升（TIME 日均减少 2.41 秒，R2 = 0.762）。研究结论为制定米兰冬奥会针对性训练策略提供了数据支持，并指出未来需扩展数据源、优化模型以提升预测精度。
关健词：成绩预测；随机森林；灰色 GM(1,1) 模型；雪车；米兰冬奥会 |
Performance Prediction for the Women’s Monobob Event at the Milano Cortina Danpezzo Sliding Centre
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Jiaxin Xu,Yiquan Yin
1.School of Physical Education, Yantai Nanshan University，Yantai Shandong 265713，China；
2.China Snow and Ice Sports College, Beijing Sport University，Beijing 100084，China
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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.
Keywords : Performance Prediction；Random Forest；Grey GM(1,1) Model；Bobsleigh；Milano Cortina Olympics
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