UCI WorldTour races, the premier men's elite road cycling tour, are grueling events that put riders' physical fitness and endurance 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 coaches' judgement and experience, 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 estimate for each individual rider per stage instantly. In addition, we compare methods to quantify uncertainty in estimating the speed and power of Team Jumbo-Visma riders for cycling races. The empirical analysis of the jackknife+, jackknife-minmax, jackknife-minmax-after-bootstrap, CV+, CV-minmax, conformalized quantile regression (CQR) and inductive conformal prediction (ICP) methods in conformal prediction reveals all methods except minmax based methods achieve valid prediction intervals while producing prediction intervals tight enough to be used for decision making. Furthermore, methods computing prediction intervals of fixed size produce significantly tighter intervals for low significance value. Among the methods computing intervals of varying length across the input space, namely the CQR and ICP methods, ICP computes tighter prediction intervals at larger significance level.

翻译：世界巡回赛（UCI WorldTour）作为顶级男子精英公路自行车赛事，是对运动员体能和耐力的严峻考验。珍宝-维斯马车队（Team Jumbo-Visma）的教练团队长期负责预测该荷兰车队每位骑手在每场赛程中的能量需求。这些预测必须确保骑手在整个比赛中拥有维持高水平表现所需的能量与资源。然而，这项任务既耗时又充满挑战，因为它需要精确估算比赛速度和功率输出。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，常导致预测不准确。本文提出了一种更有效的自行车赛事能量需求预测新方法。通过使用回归模型预测速度与功率，我们能够即时为教练提供每位骑手单赛段的卡路里需求估计值。此外，我们比较了量化珍宝-维斯马车队骑手比赛速度与功率不确定性的多种方法。对共形预测中jackknife+、jackknife-minmax、jackknife-minmax-after-bootstrap、CV+、CV-minmax、共形分位数回归（CQR）和归纳共形预测（ICP）等方法的实证分析表明：除基于minmax的方法外，所有方法均可生成有效的预测区间，且区间宽度足以支撑决策。对于低显著性水平，固定宽度预测区间方法产生的区间显著更窄。而在计算输入空间变宽区间的方法（即CQR与ICP）中，ICP在较高显著性水平下能生成更紧凑的预测区间。