UCI WorldTour races, the premier men's elite road cycling tour, are grueling events that put physical fitness and endurance of riders to the test. The coaches of Team Jumbo-Visma have long been responsible for predicting the energy needs of each rider of the Dutch team for every race on the calendar. Those must be estimated to ensure riders have the energy and resources necessary to maintain a high level of performance throughout a race. This task, however, is both time-consuming and challenging, as it requires precise estimates of race speed and power output. Traditionally, the approach to predicting energy needs has relied on judgement and experience of coaches, but this method has its limitations and often leads to inaccurate predictions. In this paper, we propose a new, more effective approach to predicting energy needs for cycling races. By predicting the speed and power with regression models, we provide the coaches with calorie needs estimates for each individual rider per stage instantly. In addition, we compare methods to quantify uncertainty using conformal prediction. The empirical analysis of the jackknife+, jackknife-minmax, jackknife-minmax-after-bootstrap, CV+, CV-minmax, conformalized quantile regression, and inductive conformal prediction methods in conformal prediction reveals that all methods achieve valid prediction intervals. All but minmax-based methods also produce produce sufficiently narrow prediction intervals for decision-making. Furthermore, methods computing prediction intervals of fixed size produce tighter intervals for low significance values. Among the methods computing intervals of varying length across the input space, inductive conformal prediction computes narrower prediction intervals at larger significance level.

翻译：UCI世界巡回赛是男子精英公路自行车赛的最高级别赛事，是对运动员体能和耐力的严酷考验。珍宝-维斯马车队的教练团队长期负责预测该荷兰车队每位车手在赛历中每场比赛的能量需求。这些预测必须确保车手在整个比赛中拥有维持高水平表现所需的能量和资源。然而，这项任务既耗时又充满挑战，因为它需要对赛车速度和功率输出进行精确估算。传统上，能量需求预测依赖于教练的判断和经验，但这种方法存在局限性，常导致预测不准。本文提出了一种更有效的自行车赛能量需求预测新方法：通过回归模型预测速度和功率，我们能够即时为教练提供每位车手每赛段的卡路里需求估计。此外，我们比较了使用共形预测进行不确定性量化的方法。对共形预测中jackknife+、jackknife-minmax、jackknife-minmax-after-bootstrap、CV+、CV-minmax、共形分位数回归和归纳共形预测方法的实证分析表明，所有方法均能生成有效的预测区间。除基于minmax的方法外，其他方法生成的预测区间宽度均足够窄，可用于决策。此外，计算固定大小预测区间的方法在低显著性水平下能生成更紧凑的区间。在计算随输入空间变化的可变长度区间的方法中，归纳共形预测在较高显著性水平下能生成更窄的预测区间。