  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 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.
